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1
Meenakshi A, Niranjana P, Pavitra Rao S, and Dhivya G. "Machine Learning Based Loan Eligibility Prediction and Automation." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 426–32. https://doi.org/10.47392/irjaeh.2025.0058.
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As the demand for bank loans rises, banks receive more loan applications every day. To determine who qualifies, they carefully assess each applicant’s credit score and overall financial risk. However, even with these rigorous assessments, some borrowers still fail to repay their loans leading to significant financial losses for banks. To address this, challenge an advanced solution in a web application based on machine learning in the automation of loan evaluation and improves decision-making. It uses an historical model of loan to analyze key financial features such as the customer's credit history, status of income, employment status, and debt to income ratio that will help appropriately understand the qualifications of the applicants with the result of automating much of the processes, the solution will reduce heavy manual loads increase efficiency in deciding speed, consistency, and more transparency. The web application provides real- time insights and instant decision results, hence enabling easier communication with applicants and a more excellent customer experience. The project perfectly aligns with the digital transformation goals of the bank and presents a scalable solution that responds to changes in regulatory and market conditions. The bank is now able to portray itself as an innovator in financial services due to the adoption of machine learning in the automated decision-making process and is better placed to be responsive to customer needs while reducing operational costs. It modernizes loan evaluations, but basically, it is aligned with strategic objectives as it establishes customer trust.
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Kowsar, Md Masud, Mohammad Mohiuddin, and Hosne Ara Mohna. "CREDIT DECISION AUTOMATION IN COMMERCIAL BANKS: A REVIEW OF AI AND PREDICTIVE ANALYTICS IN LOAN ASSESSMENT." American Journal of Interdisciplinary Studies 04, no. 04 (2023): 01–26. https://doi.org/10.63125/1hh4q770.
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The increasing integration of artificial intelligence (AI) and predictive analytics in commercial banking has fundamentally transformed credit decision-making, enabling faster, more accurate, and more inclusive loan assessment processes. This systematic review aims to synthesize the current academic and empirical literature on AI-powered credit decision automation, with particular attention to methodological advancements, operational efficiency, financial inclusion, ethical governance, and regulatory challenges. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 102 peer-reviewed studies published between 2000 and 2023 were selected and analyzed from major databases including Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar. The review finds that machine learning models particularly ensemble methods and deep neural networks consistently outperform traditional statistical approaches in credit scoring accuracy, especially in complex borrower environments. Operationally, AI-driven systems significantly reduce loan processing time and operating costs, while enabling real-time credit adjudication and scalability across diverse lending portfolios. Furthermore, the use of alternative data, such as mobile phone metadata, utility payments, and psychometric testing, has expanded credit access to previously underserved groups, demonstrating the potential of AI to promote financial inclusion. However, the review also identifies significant concerns around algorithmic bias, model transparency, and compliance with legal frameworks such as GDPR, ECOA, and FCRA. To address these issues, the literature increasingly supports the adoption of explainable AI (XAI) methods, fairness-aware algorithms, and ethics-by-design principles in model development and deployment. Overall, this review highlights that while AI and predictive analytics offer transformative potential in automating credit decisions, their effectiveness depends on the balance between technological sophistication, ethical responsibility, and regulatory alignment. The findings contribute a comprehensive foundation for future research, policy formulation, and strategic implementation of credit automation systems in the evolving landscape of digital finance.
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Mamadiyarov, Zokir, and O‘tkirjon Tajimatov. "INSURANCE OF COMMERCIAL BANKS' CREDIT FACILITIES AND CREDIT RISK MANAGEMENT." European Journal of Artificial Intelligence and Digital Economy 1, no. 9 (2024): 79–85. https://doi.org/10.61796/jaide.v1i9.956.
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The practice of insurance of credit facilities of commercial banks is an important means of credit risk management in banking activities. Through insurance, banks can protect their loan portfolio from risks and reduce losses related to customers who face default or other debt situations. Development of diversified insurance products, automation of processes by introduction of technological solutions and use of international experiences are considered important for improvement of insurance practice. These approaches are of great importance in reducing credit risks and making banks more efficient. This is it in the article commerce of banks credit objects insurance to do practice improvement and on prudent credit risk management approaches and international experience analysis will be done
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He, Jinyu. "The Impact of Financial Technology on Green Credit: An Empirical Analysis Based on Commercial Banks." Advances in Economics, Management and Political Sciences 153, no. 1 (2025): 171–76. https://doi.org/10.54254/2754-1169/2024.19520.
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This study explores how financial technology influences green credit operations in China's commercial banks, using data from over 30 listed banks (20112021) and a financial technology development index built with text mining. Financial technology enhances green credit by optimizing resource allocation, directing funds to environmentally compliant projects, and improving risk assessment with big data and AI, thereby lowering bad loan ratios. It also increases efficiency through automation and faster approvals, shortening credit cycles. Bank operating conditions significantly impact these benefits, highlighting financial technology's role in mitigating risks and driving China's green economic transformation.
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Oluwole, Biodun P. "How Data and Automation Transformed Small Business Lending Amid COVID-19." European Journal of Computer Science and Information Technology 13, no. 7 (2025): 1–18. https://doi.org/10.37745/ejcsit.2013/vol13n7118.
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The global COVID-19 pandemic triggered unprecedented economic disruptions, severely impacting micro, small, and medium enterprises (MSMEs) across developing economies. In Nigeria, small businesses, already grappling with limited access to credit, encountered additional constraints as traditional loan disbursement systems became overwhelmed by the volume and urgency of pandemic-related relief applications. Manual lending processes, characterized by bureaucratic delays and in-person verifications, proved ill-equipped to handle the crisis, prompting an accelerated shift toward data-driven digital solutions. In response, financial institutions—ranging from commercial banks to fintech startups—deployed automation technologies to streamline loan origination, eligibility assessments, fraud detection, compliance reporting, and customer engagement. These technologies not only enhanced the speed and accuracy of credit delivery but also contributed to greater transparency and accountability in the disbursement of public funds.This paper investigates the transformative role of automation in Nigeria’s small business lending landscape during COVID-19. Using a mixed-method research design, we surveyed 500 key stakeholders, including small business owners, financial service providers, fintech innovators, and regulatory officials. The findings reveal that automation significantly improved loan approval timelines, increased user satisfaction, and enhanced fraud prevention capabilities. Furthermore, the study underscores automation’s long-term potential in deepening financial inclusion, improving regulatory oversight, and driving operational efficiency within Nigeria’s financial sector. By offering empirical insights, this research contributes to the evolving discourse on digital transformation in emerging markets and provides a framework for future innovation in crisis-resilient financial systems.
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Dong, Zhonghao. "A study on personal credit risk assessment model based on integrated learning." BCP Business & Management 32 (November 22, 2022): 515–31. http://dx.doi.org/10.54691/bcpbm.v32i.2994.
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Financial risk control is an important means to control capital security. Under the background of market economy and inclusive finance, the demand for personal credit loans is increasing rapidly. Therefore, it is very important to control financial risks. However, in the financial field, due to the requirements of security, data is scarce and large-scale data cannot be obtained, so many researchers cannot carry out detailed data analysis and technical research, and can only carry out simple theoretical analysis. However, the current financial risk control mostly relies on manual operation, which is inefficient. In order to solve this problem, this paper firstly obtains a large number of personal credit loan information from foreign websites. Then, the feature engineering technology is used to clean the data. Finally, the data mining technology in the field of artificial intelligence is used for improvement, which greatly promotes the automation and intelligent process of financial risk control. In this paper, the data mining algorithm is used to classify the credit loan dataset. The results show that our model achieves good results and can effectively avoid nearly 70% default risk, and the time cost is very low.
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Naramessakh, Kezia Tirza, and Cahyo Prianto. "Otomatisasi Keputusan Pemberian Kredit Pensiun Menggunakan Metode Weighted Product." EFISIENSI - KAJIAN ILMU ADMINISTRASI 16, no. 1 (2019): 33–48. http://dx.doi.org/10.21831/efisiensi.v16i1.24475.
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Dalam memberikan kredit pensiun, PT. Pos Indonesia masih mengalami kesulitan dalam menentukan Pensiun yang berhak atau layak untuk mendapatkan pinjaman kredit, sehingga dalam menentukan Pensiun yang berhak dan layak untuk diberikan pinjaman kredit membutuhkan waktu yang lama serta belum tentu hasil yang didapat juga benar. Maka penulis melakukan analisis kelayakan pemberian kredit agar bisa mempermudah dalam menentukan Pensiun yang layak atau diprioritaskan untuk diberikan pinjaman kredit. Penelitian ini menggunakan metode Weighted Product, yaitu salah satu metode penyelesaian untuk masalah Multi Attribute Decision Making (MADM). Multi Attribute Decision Making adalah keputusan analisis multi kriteria yang popular dan merupakan metode pengambilan keputusan multi kriteria. Dengan menggunakan metode Weighted Product (WP) akan mencari bobot untuk setiap kriteria, kemudian dilakukan proses peringkat yang akan menentukan alternatif optimal dari Pensiunan. Metode Weighted Product dapat membantu dalam mengambil keputusan kelayakan pemberian kredit. Penulis menggunakan lima kriteria yaitu, besar gaji, jumlah pinjaman, usia, jangka waktu kredit, kredit ke-berapa dan jaminan. Hasil dari penelitian ini, mempermudah dalam menentukan pensiun yang layak atau diprioritaskan untuk diberikan pinjaman kredit pensiun. Kata kunci: Weighted Product, Pemberian Kredit Pensiun, Pensiun Abstract: PENSION CREDIT ADMINISTRATION AUTOMATION USING THE WEIGHTED PRODUCT METHOD. In providing pension credit, PT. Pos Indonesia still has difficulties in determining which pensions are entitled or eligible to get a loan, so in determining which pensions are entitled and eligible to be given a credit loan requires a long time and not necessarily the results obtained are also correct. So the author analyzes the feasibility of lending in order to make it easier to determine which pensions are feasible or prioritized for credit loans. This study uses the Weighted Product method, which is one of the settlement methods for the Multi Attribute Decision Making (MADM) problem. Multi Attribute Decision Making is a popular multi-criteria analysis decision and is a multi-criteria decision making method. Using the Weighted Product (WP) method will look for weights for each criterion, then a ranking process will be carried out that will determine the optimal alternative of the Pensioner. Weighted product methods can help in making credit worthiness decisions. The author uses five criteria, namely, the amount of salary, loan amount, age, credit period, what credit and collateral. The results of this study make it easier to determine a decent or prioritized pension for a pension credit loan. Keyword: Weighted Product, Providing Pension Credit, Pension.
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Nwangele, Chigozie Regina, Ademola Adewuyi, Omoniyi Onifade, and Ayodeji Ajuwon. "AI-Driven Financial Automation Models: Enhancing Credit Underwriting and Payment Systems in SMEs." International Journal of Social Science Exceptional Research 1, no. 2 (2022): 131–42. https://doi.org/10.54660/ijsser.2022.1.2.131-142.
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Small and Medium-sized Enterprises (SMEs) are critical drivers of economic growth and job creation globally but often face significant barriers in accessing timely credit and efficient payment systems. Traditional credit underwriting processes are typically manual, time-consuming, and reliant on limited financial data, resulting in inefficiencies, higher default risks, and exclusion of many SMEs from formal financial services. Similarly, SME payment systems encounter challenges such as transaction delays, fraud risks, and cumbersome reconciliation processes, which hinder smooth cash flow management and operational performance. This explores the transformative potential of AI-driven financial automation models to enhance credit underwriting and payment systems for SMEs. AI technologies, including machine learning algorithms and natural language processing, enable the analysis of vast and diverse data sources—ranging from traditional financial statements to alternative data such as mobile money transactions and social media activity. These advanced models improve credit risk assessment accuracy by capturing nuanced financial behaviors and reducing subjective biases inherent in manual evaluations. Furthermore, AI-powered automation in payment systems facilitates real-time transaction monitoring, fraud detection, and intelligent reconciliation, thereby enhancing operational efficiency and reducing financial losses. Integrating AI-driven credit underwriting with automated payment systems creates synergies that enable dynamic credit limit adjustments, automated loan disbursement, and continuous risk monitoring based on payment behaviors. This integration supports more adaptive and responsive financial services tailored to the evolving needs of SMEs. Despite these benefits, challenges remain, including data privacy concerns, regulatory compliance, technological infrastructure limitations, and the need for ethical AI deployment. This study highlights case examples demonstrating successful AI implementation in SME financial services and discusses future directions such as incorporating blockchain technology and expanding AI solutions to underserved markets. Ultimately, AI-driven financial automation models hold substantial promise for democratizing access to credit and enhancing payment efficiency, thereby fostering SME growth, financial inclusion, and broader economic development.
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Waliullah, Md. "LEVERAGING MANAGEMENT INFORMATION SYSTEMS FOR ENHANCING CREDIT RISK ASSESSMENT IN COMMERCIAL BANKS." ACADEMIC JOURNAL ON BUSINESS ADMINISTRATION, INNOVATION & SUSTAINABILITY 4, no. 4 (2024): 19–32. http://dx.doi.org/10.69593/ajbais.v4i04.112.
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This study explores the significant impact of Management Information Systems (MIS) on credit risk assessment in commercial banks, examining how these systems enhance decision-making accuracy, operational efficiency, and proactive risk management. By synthesizing findings from 50 peer-reviewed studies, the research reveals that banks using MIS experience a 20-30% reduction in non-performing loans and a 40-50% increase in loan processing speed due to automation and real-time data analysis. The integration of advanced technologies such as artificial intelligence (AI) and predictive analytics further improves credit risk forecasting accuracy by 15-20%, enabling banks to implement proactive risk mitigation strategies that reduce borrower defaults by up to 25%. However, the study also highlights significant challenges, particularly for smaller banks, which face high implementation costs and difficulties integrating MIS with legacy systems. Despite these challenges, the role of MIS in ensuring regulatory compliance, particularly under Basel III, and reducing overall credit exposure by 15-20% underscores its critical importance in modern credit risk management. The findings suggest that while MIS is essential for maintaining financial stability and competitiveness, scalable and cost-effective solutions are necessary for broader adoption across the banking industry.
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Researcher. "GENERATIVE AI FOR PREDICTIVE CREDIT SCORING AND LENDING DECISIONS INVESTIGATING HOW AI IS REVOLUTIONISING CREDIT RISK ASSESSMENTS AND AUTOMATING LOAN APPROVAL PROCESSES IN BANKING." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 2162–70. https://doi.org/10.5281/zenodo.14357088.
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GenAI is a process that helps in maintaining credit risk assessment and ability for making accurate decisions in loan approval process. Generative AI impacts on the growth of credit risk assessment and automating loan approval processes in the banking sector. Secondary data collection and qualitative strategy have been addressed for developing financial conditions of banking sector. Accumulation of high quality of information can develop outcomes of banking sector to maintain lending decisions and credit risk assessment processes in the banking sector. Data privacy and security can maintain bias in banking sector to develop predictive credit scoring and lending decisions.
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Andronie, Mihai, Roman Blažek, Mariana Iatagan, et al. "Generative artificial intelligence algorithms in Internet of Things blockchain-based fintech management." Oeconomia Copernicana 15, no. 4 (2024): 1349–81. https://doi.org/10.24136/oc.3283.
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Research background: Big data-driven artificial Internet of Things (IoT) fintech algorithms can provide real-time personalized financial service access, strengthen risk management, and manage, monitor, and mitigate transaction operational risks by operational credit risk management, suspicious financial transaction abnormal pattern detection, and synthetic financial data-based fraud simulation. Blockchain technologies, automated financial planning and investment advice services, and risk scoring and fraud detection tools can be leveraged in financial trading forecasting and planning, cryptocurrency transactions, and financial workflow automation and fraud detection. Algorithmic trading and fraud detection tools, distributed ledger and cryptocurrency technologies, and ensemble learning and support vector machine algorithms are pivotal in predictive analytics-based risk mitigation, customer behavior and preference-based financial product and service personalization, and financial transaction and fraud detection automation. Credit scoring and risk management tools can offer financial personalized recommendations based on customer data, behavior, and preferences, in addition to transaction history, by generative adversarial and deep learning recurrent neural networks. Purpose of the article: We show that blockchain and edge computing technologies, generative artificial IoT-based fintech algorithms, and transaction monitoring and credit scoring tools can be harnessed in financial decision-making processes and loan default rate mitigation for transaction, payment, and credit process efficiency. Generative and predictive artificial intelligence (AI) algorithmic trading systems can drive coherent customer service operations, provide tailored financial and investment advice, and influence financial decision processing, while performing real-time risk assessment and financial and trading risk scenario simulation across fluctuating market conditions. Fraud and money laundering prevention tools, blockchain and financial transaction technologies, and federated and decentralized machine learning algorithms can articulate algorithmic profiling-based transaction data patterns and structures, credit assessment, loan repaying likelihood prediction, and interest rate and credit lending risk management by real-time financial pattern and economic forecast-based credit analysis across investment payment and transaction record infrastructures. Methods: Research published between 2023 and 2024 was identified and analyzed across ProQuest, Scopus, and the Web of Science databases by use of screening and quality assessment software systems such as Abstrackr, AMSTAR, AXIS, CADIMA, CASP, Catchii, DistillerSR, Eppi-Reviewer, MMAT, Nested Knowledge, PICO Portal, Rayyan, ROBIS, and SRDR+. Findings & value added: The main value added derived from the systematic literature review is that generative AI-based operational risk management, fraud detection, and transaction monitoring tools can provide personalized financial support and services and clarify financial and credit decisions and operations by financial decision-making process automation in dynamic business environments based on fraud detection capabilities and transaction data analysis and assessment. The benefits for theory and current state of the art are that credit risk and financial forecasting tools, artificial IoT-based fintech and generative AI algorithms, and algorithmic trading and distributed ledger technologies can be deployed in financial decision-making and customer behavior pattern optimization, credit score assessment, and money laundering and fraudulent payment detection. Policy implications reveal that investment management and algorithmic credit scoring tools can streamline financial activity operational efficiency, design financial planning analysis and forecasting, and carry out financial service and transaction data analysis for informed transaction decision-making and fraudulent behavior pattern and incident detection, taking into account credit history and risk evaluation and improving personalized experiences.
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Karhe, Prakash Sukhadev, and Roundal Sitaram Rangnath Dr. "Artificial Intelligence in Cooperative Credit Societies: Enhancing Loan Processing and Risk Assessment." International Journal of Advance and Applied Research S6, no. 22 (2025): 708–12. https://doi.org/10.5281/zenodo.15532904.
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<em>This study explores the impact of Artificial Intelligence (AI) on loan processing and risk assessment in cooperative credit societies. The integration of AI can enhance the efficiency, accuracy, and transparency of loan management by automating credit scoring, fraud detection, and personalized financial recommendations. The study aims to assess how AI-driven technologies optimize decision-making processes and minimize financial risks for cooperative credit institutions. By analyzing secondary data and conducting case studies, the research highlights the advantages and challenges of AI adoption in cooperative credit societies. The findings indicate that AI improves operational efficiency, reduces non-performing assets, and enhances customer satisfaction. However, challenges like data security, infrastructure costs, and regulatory constraints remain significant.</em>
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Ekong, Rebecca Etim, Kolawole Gabriel Akintola, and Bamidele Moses Kuboye. "Development Of Credit Scoring Model For Borrowers Using Machine Learning Techniques." PERSPEKTIF 11, no. 3 (2022): 829–38. http://dx.doi.org/10.31289/perspektif.v11i3.7180.
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Financial organizations such as banks have experienced an increase in demand for loans from borrowers over the years. These organizations are highly interested in knowing whether a borrower can pay back if granted the loan requested. Granting loans to defaulters can cripple the business, hence, these financial organizations are compelled to evaluate credit worthiness of clients using the vast volume of historical data related to financial position of borrowers. Like other prediction models, credit scoring is a technique used in predicting the probability that a loan applicant, existing borrower, or counterparty will default. Machine learning technique has ability to solve these challenges faced by credit analyst by automating the processing and extraction of knowledge from data. This research focuses on the development of a credit scoring model using Rough Set Theory (RST) and Multi-Layer Perceptron (MLP) Neural Network. RST was used for feature selection while ANN trained with backpropagation was used for classification. This research used two credit scoring datasets; Australian and German credit dataset. Data pre-processing and machine learning were performed using the Anaconda software. This research compares the result obtained from the RST and MLP with Decision Tree, Logistic Regression, Random Forest, Support Vector Machine, Nayes Bayes, K-Nearest Neighbour and ANN using standard evaluation metric to ascertain its performance on the two datasets and conclude the major findings. This research contributes a credit scoring model with improved performance while saving the computational costs.
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Egwa, Awuza Abdulrashid. "Default Prediction for Loan Lenders Using Machine Learning Algorithms." SLU Journal of Science and Technology 5, no. 1&2 (2022): 1–12. http://dx.doi.org/10.56471/slujst.v5i.222.
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Credit loans are considered most essential aspect of most financial institutions. All loan mortgagees or lenders are demanding to identify out effective commercial and business approaches to encourage customers to apply their credit loans. There are numerous business patrons who act negatively after their requests got approval. To avert this condition, lenders have to discover some techniques to forecast customer’s behaviors. This resulted to the usage of machine learning algorithms by the financial lending institutions for accessing loan applicants. Despite advancements in automating decision-based loan systems, most existing models do not consider the “early loan repayment” attribute as a factor in resolving this prediction error. In reality, the amendment for preliminary loan reimbursement in model building is obligatory, since a larger numbers of timely loan reimbursement observed during the loan period, reduces default rate. For effective model’s comparison based on accuracy and minimum errors of prediction, six supervised machine learning algorithms i.e. Random Forest, Artificial Neural Network, Classification and Regression Tree, Support Vector Machine, Logistic Regression, and Naïve Bayes were adopted to develop a default prediction models which include the early loan repayment attribute. The models were trained and tested on a loan dataset consisting of attributes with, and without early loan repayment attribute and were evaluated using five performance metrics. The results of the performance evaluation show that models that account for early loan repayment have higher accuracy, recall, precision, Root Mean Square Error and Receiver Operative Characteristics curve values than models trained without the early loan repayment attribute. The Random forest model proofed to be the best predictive model having 93% accuracy, 11% RMSE, 90% precision, 89% recall and 81% ROC value over others models.
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N, Ms Roshni S., Boobalan S.A, Dhanush, Krishna A.S, and Anudev Anudev. "LOAN ELIGIBILITY CHECKER." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–7. http://dx.doi.org/10.55041/ijsrem39108.
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By automating eligibility evaluation based on preset criteria, the Loan Eligibility Checker technology is intended to expedite the loan application process. This project evaluates applicants based on important indicators, such as debt-to-income ratio, income, employment stability, and credit score, using data processing, machine learning, and rule-based algorithms. This solution saves time and resources for manual assessment, improves client satisfaction through prompt feedback, and increases the efficiency and accuracy of loan evaluations for financial institutions by offering an instant conclusion on loan eligibility. Because of its scalability, the system can process a large number of applications and offer reliable, impartial evaluations to a diverse pool of candidates. In the end, financial institutions benefit much from the Loan Eligibility Checker.
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Roy, Koyel, Sandip Chanda, Chanchal Mandal, and Madhumita Dasgupta. "An Empirical Analysis of Challenges Faced by Rural Bank Customers in Health Sector Loans: Assessing the Impact of Emerging Technologies and AI Risks in Kolkata (West Bengal) and Mohanpur (Jharkhand)." Journal of Neonatal Surgery 14, no. 12S (2025): 471–86. https://doi.org/10.52783/jns.v14.3218.
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The integration of emerging technologies and artificial intelligence (AI) in the rural banking sector has revolutionized financial services, offering efficiency, automation, and enhanced decision-making capabilities. However, this rapid digital transformation has also posed significant challenges, particularly for rural customers seeking health sector loans in areas like Kolkata (West Bengal) and Mohanpur (Jharkhand). This study empirically analyzes the hurdles faced by rural borrowers due to the implementation of AI-driven credit assessments, digital banking barriers, cybersecurity risks, and financial literacy gaps. One of the primary concerns is the biased risk assessment by AI algorithms, which often disadvantage rural borrowers due to inadequate financial histories, limited digital footprints, and algorithmic opacity. Additionally, digital banking platforms, despite being promoted for their accessibility, often exclude individuals with low digital literacy, leading to difficulties in applying for and managing loans. This study also highlights the cybersecurity risks associated with AI-driven banking systems, where rural customers are more susceptible to fraud, phishing, and data breaches due to inadequate security awareness. From a legal standpoint, this paper examines the application of Indian laws such as the Reserve Bank of India (RBI) Guidelines on Digital Lending, 2022, which regulate AI-based credit disbursement and customer data protection. Furthermore, the Information Technology (IT) Act, 2000 plays a crucial role in addressing data security and privacy concerns in digital banking transactions. The Consumer Protection Act, 2019, is also relevant in cases where AI-driven loan decisions lead to unfair trade practices or financial discrimination. Moreover, the RBI's Fair Practices Code ensures transparency in lending practices, mandating that banks provide clear communication on loan terms and AI-based decision-making processes. This research employs an empirical methodology, combining field surveys and interviews with rural bank customers, financial institutions, and legal experts to assess the practical implications of AI integration in rural banking. The findings. reveal that while AI-driven credit assessment tools streamline loan processing, they often lack contextual awareness of rural socio-economic conditions, leading to loan denials or higher interest rates for rural borrowers. Moreover, financial illiteracy and the lack of regulatory awareness among customers further exacerbate challenges, creating a digital divide in financial accessibility.The study concludes by suggesting policy interventions, such as enhanced regulatory oversight on AI-powered financial systems, robust consumer awareness programs, and AI transparency mandates, to ensure equitable access to health sector loans in rural banking. Strengthening legal frameworks, improving AI accountability, and bridging the digital divide are crucial to achieving financial inclusion in India's rural banking sector.
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Wajebo, Temesgen Woldamanuel. "MICRO, SMALL AND MEDIUM ENTERPRISES ACCESS TO FINANCE CONSTRAINTS IN ETHIOPIA: DEMAND SIDE ANALYSIS." International Journal of Small and Medium Enterprises 5, no. 1 (2022): 32–39. http://dx.doi.org/10.46281/ijsmes.v5i1.1809.
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Despite the immense importance of micro, small and medium enterprises (MSMEs) to job creation and the alleviation of abject poverty, significant numbers of MSMEs cannot realize their full potential because of limited access to finance and other factors, mostly in developing countries. The study investigates micro, small and medium enterprises' access to finance constraints in Ethiopia from a demand-side perspective. This study employed a descriptive analysis using survey data collected from 814 randomly selected sample enterprises. The results show that about 76% of the MSMEs were established by initial capital majorly generated from their own savings, family or friends, Equub, and saving and credit cooperatives among others. The remaining 24% accessed their initial capital from formal financial institutions. From the total, 39.4% of MSMEs did not apply for loans due to high collateral requirements, complex application procedures, unfavorable interest rates, insufficient loan size, maturity, and grace period, and lack of transparency. The study results also reveal that 22%, 46%, and 32% of the MSMEs are partially credit constrained, fully credit constrained, and non-credit constrained, respectively. The difference between the average demand and the loan supplied is estimated to be 3241, 11444, and 30949 USD for each micro, small, and medium-sized enterprise, respectively. The total estimated finance gap for all MSMEs is 31.7 billion USD. The findings of this study suggest the establishment of public credit guarantee schemes, cash flow-based lending, and psychometric testing for credit scoring and automating the MFIs' services to improve MSMEs' access to finance.
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Alghofari, Farid, Joyo Winoto, and Yudha Heryawan Asnawi. "Optimizing Mortgage Lending Strategies: A Data-Driven Approach to Enhancing Bank BTN’s Non-Subsidized Credit Model." MARGINAL JOURNAL OF MANAGEMENT ACCOUNTING GENERAL FINANCE AND INTERNATIONAL ECONOMIC ISSUES 4, no. 2 (2025): 372–86. https://doi.org/10.55047/marginal.v4i2.1636.
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The rising demand for non-subsidized mortgages in Indonesia has intensified competition among banks, necessitating improvements in credit approval efficiency and risk management. This study evaluates the business model of PT Bank Tabungan Negara (Persero) Tbk (Bank BTN) in the non-subsidized mortgage sector using SWOT analysis and the Analytical Hierarchy Process (AHP) to prioritize strategic interventions. The findings highlight decision-making conflicts, weak initial verification processes, and fraud risks as critical weaknesses, with AHP results ranking Credit Decision-Making Integration (0.54) as the most urgent strategic action. Digital transformation (0.52) presents the greatest opportunity, while competition from more efficient banks (0.49) is the most significant external threat. Managerial implications suggest the necessity of process standardization, AI-driven credit risk assessment, and automation in document verification to enhance efficiency and mitigate fraud risks. Benchmarking against leading competitors like BCA and Mandiri underscores the importance of real-time verification and centralized decision-making in reducing non-performing loan (NPL) ratios. The study provides a data-driven roadmap for Bank BTN to enhance its competitiveness, optimize risk management, and improve operational efficiency. However, limitations include the study’s focus on internal process improvements without extensive consideration of external macroeconomic fluctuations and regulatory changes. Future research should incorporate predictive modeling techniques to refine credit evaluations and explore global best practices in mortgage lending.
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Devan, Munivel, Sanjeev Prakash, and Suhas Jangoan. "Predictive Maintenance in Banking: Leveraging AI for Real-Time Data Analytics." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2, no. 2 (2023): 483–90. http://dx.doi.org/10.60087/jklst.vol2.n2.p490.
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Artificial intelligence (AI) has become a pivotal force in reshaping the banking landscape, fundamentally altering operational paradigms and customer interactions. This paper conducts an extensive examination of AI's impact on banking, encompassing pivotal domains such as customer service, fraud detection, personalized banking experiences, credit assessment, operational streamlining, predictive analysis, and regulatory adherence. AI-driven chatbots and virtual assistants have revolutionized customer engagement by delivering instantaneous assistance and tailored recommendations. Furthermore, AI algorithms have fortified security frameworks by swiftly identifying fraudulent activities and mitigating risks linked with credit evaluations and loan approvals. Automation powered by AI has significantly enhanced operational efficacy, while predictive analytics has empowered banks to execute data-centric strategies in financial realms. Additionally, AI solutions have facilitated regulatory compliance by meticulously monitoring transactions and ensuring alignment with regulatory standards. Nonetheless, the extensive integration of AI raises ethical and privacy apprehensions, necessitating deliberate attention to issues such as data protection and algorithmic fairness. In essence, while AI offers substantial prospects for innovation and efficiency within the banking domain, its conscientious deployment is imperative to address potential risks and uphold equitable outcomes.
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Molnár, Bálint, Galena Pisoni, Meriem Kherbouche, and Yossra Zghal. "Blockchain-Based Business Process Management (BPM) for Finance: The Case of Credit and Claim Requests." Smart Cities 6, no. 3 (2023): 1254–78. http://dx.doi.org/10.3390/smartcities6030061.
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Because of the competitive economy, organizations today seek to rationalize, innovate, and adapt to changing environments and circumstances as part of business process improvement efforts. The strength of blockchain technology lies in its usage as an apt technology to enhance the efficiency and effectiveness of business processes; furthermore, it prevents the use of erroneous or obsolete data and allows sharing of confidential data securely. The use of superior technology in the execution and automation of business processes brings opportunities to rethink the specific process itself as well. Business processes modeling and verification are essential to control and assure organizational evolution, therefore, the aim of this paper is three-fold: firstly, to provide business process management patterns in finance, based on blockchain, specifically for the loan-application process in the banking industry and claim process in the insurance industry that could be used and customized by companies; secondly, to critically analyze challenges and opportunities from the introduction of such approach for companies, and thirdly, to outline how companies can implement the loan business process as a web service. Partner companies (a bank and an insurance company) formulated the potential requirements for M2P along with the application of blockchain technology. An experimental design framework was established that gave the necessary services to model the requirements, check the models, and operationalize the models. The applied research methodologies are as follows: design science research paradigm and software case study, model-to-programming (M2P) of business processes, and utilization of patterns of workflow and blockchain.
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Wajebo, Temesgen Woldamanuel. "MICRO, SMALL AND MEDIUM ENTERPRISES ACCESS TO FINANCE CHALLENGES IN SUPPLY SIDE PERSPECTIVE IN ETHIOPIA." International Journal of Small and Medium Enterprises 5, no. 2 (2022): 23–31. http://dx.doi.org/10.46281/ijsmes.v5i1.1808.
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Empirical evidence shows that the contribution of Micro, Small and Medium Enterprises Empirical evidence shows that the contribution of Micro, Small and Medium Enterprises (MSMEs) to gross domestic product growth, job creation, and poverty alleviation is high. Because of limited access to finance and other factors, a significant number of MSMEs, particularly in developing countries, cannot realize their full potential. The study investigates the micro, small, and medium enterprises' access to finance challenges from the supply-side perspective in Ethiopia. The study employed descriptive analysis using survey data collected from purposely selected 11 banks and 9 micro-finance institutions (MFI). The results show that the lack of collateral, unavailability of financial records, poor pre-loan savings, low loan repayment rate, business feasibility problems, credit information asymmetry, attitudinal problems of the MSMEs, and diversion of the loan are demand-side constraints. From the financial institution’s perspective, the result shows that the liquidity problem, the influence of unfair competition from government banks, the lack of competent human resources, political leaders’ intervention, especially in MFI, corruption, the COVID-19 pandemic, political instability, and inadequate capital are major obstacles. Macroeconomic conditions related to challenges such as foreign exchange shortages, inflation, and trade imbalances are also mentioned as major obstacles. According to the findings of this study, designing and implementing business skill development in mindset, business planning, business bookkeeping, and financial reports can help MSMEs access loans. For financial institutions, the study suggests cash flow-based lending products, applying psychometric testing for credit scoring, improving the governance of the government-affiliated MFIs, and automating the MFIs.
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Kulliev, Istam. "IMPROVEMENT OF ASSESSMENT OF CREDITWORTHINESS OF INDIVIDUALS IN COMMERCIAL BANKS OF THE REPUBLIC OF UZBEKISTAN." Economics and Education 24, no. 3 (2023): 124–31. http://dx.doi.org/10.55439/eced/vol24_iss3/a19.
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This article highlights the importance of creditworthiness assessment and scoring system of individuals. The expansion of lending practices to individuals by commercial banks, minimizing the risks associated with this process and preventing the occurrence of problem loans requires improving the creditworthiness of customers. In particular, today's offering of online credit services requires process automation and creditworthiness assessment taking into account all factors.
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Fadare, Adewale Abiodun. "Christian Ethical Response to The Challenges of Digital Financial Borrowing in Nigeria." Shodh Sari-An International Multidisciplinary Journal 03, no. 04 (2024): 194–207. http://dx.doi.org/10.59231/sari7756.
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Digital financial borrowing is the money borrowed online through digital mobile applications, platforms or phones that are offered by financial lenders using the Internet. Due to the advancement in financial technology, banking and financial services and operations have been digitalized. The emergence of digital finance has rebranded banking and financial borrowing access and opportunities. People can now borrow money online, digitally and seamlessly. This development is applaudable because it eases traditional banking operations burdens and removes bottlenecks in borrowing and lending to finance business and personal needs. However, the attendant experiences from digital financial borrowing are worrisome and pose challenges to human well-being in developing economies, especially among Christians in Nigeria. Some of the experiences of digital financial borrowers are poor loan repayment, which may be due to high interest, breach of personal data policy and social shaming of defaulted borrowers by lending companies’ employees. The unvirtuous motives and characters of some borrowers, professionally unethical attitudes and loan recovery strategies are responsible for these challenges. This paper gives an overview of digital financial borrowing in Nigeria and discusses digital financial borrowing, which is characterized with features such as instant, automation, remote, consumer loans and short maturity. The paper argues that digital financial borrowing has assisted financial inclusion and contributed to Nigeria’s digital economy, it has however negatively impacted on borrowers’ personal, emotional, mental, marital, business, spiritual and moral well-being due to poor repayment or inability to repay, high interest, and social shaming of customers by digital loan officers. The paper concludes that Christians should act virtuously and ethically according to biblical instructions when dealing in financial borrowing, wisdom, honesty, godliness, discretion, patience and mutual respect are among Christian virtues to display in financial borrowing decisions, actions and relationships. It recommends that churches and church leaders should encourage financial education and literacy that enhance credit understanding and boost Christians’ financial wellness. It also recommends that Chartered Institute of Bankers of Nigeria, with other financial regulatory bodies in Nigeria should regulate the operations of lending companies and enforce acceptable professional work ethics and attitudes of digital lending companies’ employees to enhance friendly relationships with customers.
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Bagas Julian U and Isnin Faried. "Implementation of the Weighted Product Method for Bank Credit Restructuring Applications." International Journal of Advances in Scientific Research and Engineering 09, no. 10 (2023): 09–10. http://dx.doi.org/10.31695/ijasre.2023.9.10.1.
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The impact of the Covid-19 pandemic is felt by the company, which is a decline in business revenue. The scan also has an impact in terms of the ability to repay bank credit and has a risk of credit clogging. The XYZ Bank as a lender has an obligation to implement the established policy of National Economic Stimulus. Nowadays, credit restructuring services exist, but many of the service processes are still carried out semi-automatically, partly by the system and the other part by humans and using paper-based data. The method used to solve such problems is the Decision Support System using the Weighted Product method. (WP).Research conclusions on the implementation of the Weighted Product (WP) method show good results and can perform criteria calculations (in numerical form) for all criteria. This method can be implemented by combining server-side scripting in PHP and client-sided scripting using Javascript. The overall results can be concluded the application created to meet the needs of the research problem that is making the automation of the priority process of submission of credit restructuring in Bank XYZ. There is a minimum result that is the algorithm used affecting the increase in the load of memory and processor performance.
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Erokhin, V. V. "IMPROVEMENT OF AUTOMATED MANAGEMENT SYSTEMS FOR MICROLOANS." Juvenis scientia, no. 1 (2019): 14–18. http://dx.doi.org/10.32415/jscientia.2019.01.03.
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The article discusses modern approaches to improving the management of microloans in remote customer service. The lack of full-fledged microfinance information systems reduces the efficiency of credit institutions, which, as a result, leads to an increase in interest rates and hampers the development of the microloans market to the public. One of the modern areas of lending to the population, which allows to solve the problems of cash turnover, is the improvement of automated systems for managing micro-loans for remote customer service. The theoretical basis for improving the automation of long-term and short-term microloans is presented. Presented the development and improvement of information subsystems for loans.
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Ajuwon, Ayodeji, Ademola Adewuyi, Chigozie Regina Nwangele, and Abiola Oyeronke Akintobi. "Blockchain Technology and its Role in Transforming Financial Services: The Future of Smart Contracts in Lending." International Journal of Multidisciplinary Research and Growth Evaluation 2, no. 2 (2021): 319–29. https://doi.org/10.54660/.ijmrge.2021.2.2.319-329.
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Blockchain technology has emerged as a disruptive force in financial services, offering unprecedented transparency, security, and efficiency. This examines the transformative potential of blockchain, with a particular focus on the evolving role of smart contracts in lending processes. Traditional lending systems often suffer from inefficiencies, including lengthy approval times, high operational costs, and lack of transparency, which hinder financial inclusion and increase risks for both lenders and borrowers. Blockchain’s decentralized ledger technology addresses these challenges by enabling secure, immutable, and transparent record-keeping. Smart contracts—self-executing agreements coded onto blockchain platforms—automate contract enforcement and reduce the need for intermediaries. In lending, smart contracts can streamline loan origination, automate repayment schedules, and enforce compliance with predefined terms without manual intervention. This automation decreases transaction costs, accelerates processing times, and mitigates counterparty risks, thereby enhancing overall lending efficiency. Additionally, blockchain’s tamper-proof nature fosters trust among participants by providing a single source of truth accessible to all stakeholders. The integration of blockchain and smart contracts also introduces novel possibilities for credit scoring and risk management by incorporating real-time data feeds and alternative data sources. This can expand lending access to underserved populations traditionally excluded due to lack of formal credit histories. However, challenges such as regulatory uncertainty, scalability constraints, and privacy concerns remain barriers to widespread adoption. This reviews recent advancements in blockchain-based lending platforms, explores use cases demonstrating smart contract applications, and discusses ongoing innovations aimed at overcoming implementation hurdles. It underscores the necessity of developing robust legal frameworks and interoperability standards to fully realize the benefits of smart contracts in lending. Ultimately, blockchain technology, coupled with smart contracts, holds significant promise for revolutionizing financial services by enabling more transparent, efficient, and inclusive lending ecosystems, paving the way for a future where lending is faster, fairer, and more accessible.
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Kumar, Mohan B. M. "Leveraging Artificial Intelligence for Enhancing Customer Relationship Management in Ujjivan Small Finance Bank." Journal of Research & Development 17, no. 1 (2025): 93–102. https://doi.org/10.5281/zenodo.14960476.
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<strong><em>Abstract</em></strong> <em>Ujjivan Small Finance Bank has effectively integrated advanced artificial intelligence (AI) technologies, such as AI-powered chatbots, virtual assistants, and predictive analytics, to revolutionize customer relationship management (CRM), offering round-the-clock support with personalized, instant responses to customer inquiries, thereby enhancing satisfaction and engagement across diverse customer segments, while simultaneously utilizing AI-driven insights to understand behavioral patterns, tailor marketing strategies, and develop customized financial products that align with customer needs, further strengthening relationships and loyalty; this transformation extends to enhancing operational efficiency by adopting CRMNEXT for a unified customer view and seamless service delivery, enabling faster issue resolution and more accurate targeting of solutions, and incorporating machine learning algorithms into banking security systems to analyze transaction patterns and detect fraudulent activities in real-time, thereby safeguarding customer accounts and building trust in their services; the AI-enabled systems also automate routine banking processes such as document verification, data entry, and customer onboarding, significantly reducing human errors, expediting workflows, and freeing staff to focus on complex, high-value tasks, contributing to an overall enhanced banking experience; furthermore, Ujjivan has successfully applied AI to optimize its credit and loan approval processes by analyzing vast datasets, including credit histories and spending patterns, to make more accurate creditworthiness assessments, thereby improving loan disbursal timelines and offering better service to both individual and small business customers, while positioning itself as a customer-first financial institution; these initiatives align with the bank’s goal of leveraging cutting-edge technology to deliver exceptional services in an increasingly digital-first world, cementing its leadership in innovative banking practices; through the seamless integration of AI across its CRM framework, Ujjivan has not only optimized its internal operations but also demonstrated a strong commitment to meeting the dynamic needs of its growing customer base by ensuring convenience, security, and personalized services, ultimately setting itself apart as a pioneering institution in the financial services sector and establishing a scalable model of success that combines technological innovation with a customer-centric approach, reinforcing its position as a leader in India’s competitive small finance banking landscape and exemplifying the transformative potential of AI in driving operational excellence and fostering sustainable growth.</em>
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Kliestik, Tomas, Robert Dragomir, Aurelian Virgil Băluță, et al. "Enterprise generative artificial intelligence technologies, Internet of Things and blockchain-based fintech management, and digital twin industrial metaverse in the cognitive algorithmic economy." Oeconomia Copernicana 15, no. 4 (2024): 1183–221. https://doi.org/10.24136/oc.3109.
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Research background: Enterprise generative AI system-based worker behavior tracking and monitoring, socially responsible organizational practices, employee performance management satisfaction, and human resource management procedures, relationships, and outcomes develop on hiring and objective performance assessment algorithms in terms of human resource management activities, functions, processes, practices, policies, and productivity. Deep reinforcement and machine learning techniques, operational and analytical generative AI and cloud capabilities, and real-time anomalous behavior recognition systems further fintech development for credit and lending services, payment analytics processes, and risk assessment, monitoring, and mitigation. Generative AI tools can bolster predictive analytics by collaborative and interconnected sensor and machine data for tailored, seamless, and fine-tuned product, operational process, and organizational workflow development, efficiency, and innovation, driving agile transformative changes in digital twin industrial metaverse. Purpose of the article: We show that enterprise generative AI-driven schedule prediction tools, job search and algorithmic hiring systems, and synthetic training data can improve team selection, job performance and firing decisions, hiring decision processes, and workforce productivity in terms of prediction and decision-making by use of algorithmic management, system performance, and production process tracking tools. Blockchain-based fintech operations can shape cloud-based financial and digital banking services, quote-to-cash process automation, cash-settled crypto futures, digital loan decisioning, asset tokenization simulated transactions, transaction switching and routing operations, tailored peer-to-peer lending, and proactive credit line management. Collaborative unstructured enterprise data processing, infrastructure, and governance can develop on AI decision and behavior automation technology, retrieval augmented generation and development management systems, and real-time data descriptive and predictive analytics, driving productivity surges and competitive advantage in digital twin industrial metaverse. Methods: Reference and review management tools, together with evidence synthesis screening software, harnessed were Abstrackr, AMSTAR, ASReview Lab, CASP, Catchii, Citationchaser, DistillerSR, JBI SUMARI, Litstream, PICO Portal, and Rayyan. Findings & value added: The current state of the art is improved for theory on organizational issues and for policy making as deep learning-based generative AI tools and workplace monitoring systems can augment performance and productivity, gauge employee effectiveness, build resilient, satisfied, and engaged workforce, assess human capital, skill, and career development, drive employee and productivity expectations in relation to flexibility and stability, and shape turnover, retention, and loyalty. Cloud and account servicing technologies can be deployed in generative AI fintechs for embedded cryptocurrency trading, transaction monitoring and processing, digital asset transfers, payment screening, corporate and retail banking operations, and fraud prevention. Generative AI technologies can reshape jobs and reimagine meaningful work, involving creativity and innovation and adaptable and resilient sustained performance, providing valuable constructive feedback, optimizing workplace flexibility and psychological safety, and measuring and supporting autonomy and flexibility-based efficiency, performance, and productivity, while configuring demanding, engaging, and rewarding experiences by cloud and edge computing devices in digital twin industrial metaverse.
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Shen, Qi. "AI-driven financial risk management systems: Enhancing predictive capabilities and operational efficiency." Applied and Computational Engineering 69, no. 1 (2024): 141–46. http://dx.doi.org/10.54254/2755-2721/69/20241494.
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The integration of artificial intelligence (AI) in financial risk management systems has revolutionized traditional approaches, providing enhanced predictive capabilities and operational efficiency. This paper explores the various applications of AI in credit risk assessment, market risk analysis, operational risk management, and regulatory compliance. AI-driven systems leverage advanced machine learning algorithms to analyze vast datasets, including real-time market data and non-traditional sources, improving risk predictions and enabling proactive risk management. Scenario simulations, predictive modeling, real-time data analysis, and automated decision-making are discussed as core components of AI-driven systems. The paper also highlights the benefits of AI in automating routine tasks, enhancing data analytics, and ensuring regulatory compliance. By continuously learning and adapting to new data, AI systems offer dynamic risk management solutions that address evolving market conditions and regulatory requirements. This comprehensive analysis demonstrates how AI-driven financial risk management systems can significantly reduce the incidence of loan defaults, enhance portfolio quality, and improve the overall resilience of financial institutions.
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Liu, He, Biao Yang, Xuanrui Xiong, et al. "A Financial Management Platform Based on the Integration of Blockchain and Supply Chain." Sensors 23, no. 3 (2023): 1497. http://dx.doi.org/10.3390/s23031497.
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Internet of Things (IoT) finance extends financial services to the whole physical commodity society with the help of IoT technology to realize financial automation and intelligence. However, the security of IoT finance still needs to be improved. Blockchain has the characteristics of decentralization, immutability, faster settlement, etc., and has been gradually applied to the field of IoT finance. Blockchain is also considered to be an effective way to resolve the problems of the traditional supply chain finance industry, such as the inability to transmit core enterprise credit, the failure of full-chain business information connections and the difficulty of clearing and settlement. Supply chain finance allows the strongest enterprise in the supply chain to apply for credit guarantee from the bank to obtain bank loans, and use the funds for circulation in the supply chain to ensure that each enterprise in the whole supply chain can obtain working capital to realize profits, so as to maximize common interests. In this paper, a financial management platform based on the integration of blockchain and supply chain has been designed and implemented. Blockchain is used to integrate supply chain finance to synchronize the bank account payment system, realize the automatic flow of funds, process supervision and automatically settle account periods based on smart contracts. The four functional modules of the system are designed using unified modeling language (UML), and the model view controller (MVC) architecture is selected as the main architecture of the system. The results of the system test show that the proposed platform can effectively improve the system security, and can use the information in the blockchain to provide multi-level financing services for enterprises in supply chain finance.
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Vagaytseva, Valeria, and Aleksandra Shmyreva. "Modern Banking Products: Analysis of Development Trends in Russia and Abroad." Ideas and Ideals 15, no. 2-2 (2023): 261–76. http://dx.doi.org/10.17212/2075-0862-2023-15.2.2-261-276.
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The development of new digital technologies and their active application has an impact on the relationship of credit institutions with customers in the field of availability of products and methods of services provided. Currently, the activity of banks in creating and releasing products for customers is reaching a new level thanks to the development of technologies and innovations in this area. The research and analysis of the current directions of development in the field of development and formation of modern banking products is carried out. The object of the study is the variety of products of commercial banks, the subject of the study is the global trends of their development. Purpose of the work: analysis of trends in the development of modern and innovative banking products in Russia and abroad. Based on the purpose of the work, the main types of banking innovation processes in the world were identified, such as a banking product in new market segments, innovations such as the development of activities in new areas of the financial market, modified financial intermediation services aimed at effective asset and liability management, new methods of cash management and the use of new information technologies, new products in traditional segments of loan capital. The line of common banking products considered in the article, as well as the study of products of a new digital and remote format, allowed us to conclude that it is necessary to develop and disseminate the existing classification of banking products. Due to the increasing number of bank product developments, their classification becomes an integral stage in their formation and subsequent release. A number of classification features and distinctions were investigated, which helped to systematize the existing groups of banking products and identify areas for their development. The perspective of the banking sector is the transformation of the bank’s products. There is automation in many banking processes and, as a result, there are modernized products and services of the bank, which have not been considered in the theoretical aspect earlier in scientific and practical works and publications.
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Munira, Mosa Sumaiya Khatun, Shaharima Juthi, and Aklima Begum. "Artificial Intelligence in Financial Customer Relationship Management: A Systematic Review of AI-Driven Strategies in Banking and FinTech." American Journal of Advanced Technology and Engineering Solutions 1, no. 01 (2025): 20–40. https://doi.org/10.63125/gy32cz90.
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The integration of Artificial Intelligence (AI) into Customer Relationship Management (CRM) has revolutionized the financial services industry by enhancing customer engagement, fraud detection, predictive analytics, regulatory compliance, and marketing strategies. This study systematically reviews 83 scholarly studies, including peer-reviewed journal articles, industry reports, and financial institution case studies, to assess AI’s impact on financial CRM. The findings indicate that AI-powered chatbots, virtual assistants, and sentiment analysis tools have significantly improved customer interactions, reducing response times by 57% and operational costs by 38%, while increasing customer retention rates by 28%. AI-driven fraud detection systems have enhanced transaction monitoring, reducing false positives by 52% and improving fraud detection efficiency by 74%, leading to a 43% decrease in financial losses related to fraud. Predictive analytics has transformed credit risk assessment, improving loan approval accuracy by 67%, expediting loan processing by 29%, and reducing default rates by 23%. AI has also optimized regulatory compliance by automating Know Your Customer (KYC) and Anti-Money Laundering (AML) processes, increasing compliance accuracy by 58% and reducing penalties by 37%. Additionally, AI-driven marketing strategies have strengthened customer targeting, increasing engagement by 53% and boosting product adoption rates by 31%, while Customer Lifetime Value (CLV) models have contributed to a 27% increase in long-term customer retention and a 22% improvement in per-customer profitability. This study provides a comprehensive analysis of AI-driven CRM’s measurable benefits in financial services, demonstrating its role in enhancing decision-making, streamlining operations, improving financial security, and fostering long-term customer loyalty. The findings contribute to the expanding literature on AI in financial CRM and offer strategic insights for financial institutions, policymakers, and technology developers aiming to optimize AI adoption for sustainable growth and competitive advantage.
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BROLL, UDO, B. MICHAEL GILROY, and ELMAR LUKAS. "MANAGING CREDIT RISK WITH CREDIT DERIVATIVES." Annals of Financial Economics 03, no. 01 (2007): 0750004. http://dx.doi.org/10.1142/s2010495207500042.
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Credit risk is one of the most important forms of risk faced by national and international banks as financial intermediaries. Managing this kind of risk through selecting and monitoring corporate and sovereign borrowers and through creating a diversified loan portfolio has always been one of the predominant challenges in bank management. The aim of our study is to examine how a risky loan portfolio affects optimal bank behavior in the loan and deposit markets, when derivatives to hedge credit risk are available. In a stochastic continuous-time framework a hedging model is developed where the bank management can use derivatives to hedge credit risk. Optimal loan, deposit and hedging strategies are then studied. It is shown that the magnitude and the direction of hedging are determined by the bank manager's preferences, the corresponding risk premium and the variance of the loan rate and its hedging instrument respectively.
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Dhungel, Bidur. "Impact of Credit Diversification on Credit Risk of Nepalese Commercial Banks." Nepalese Journal of Economics 8, no. 4 (2024): 215–31. https://doi.org/10.3126/nje.v8i4.79757.
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This study examines the impact of credit diversification on the credit risk of Nepalese commercial banks. Non-performing loan and net interest margin are the selected dependent variables. The selected independent variables are loan to deposit ratio, overdraft loan, term loan, deprived sector loan, real estate loan and staff loan. The study is based on secondary data of 13 commercial banks with 104 observations for the study period from 2015/16 to 2022/23. The data were collected from Banking and Financial Statistics published by Nepal Rastra Bank, reports published by Ministry of Finance and annual report of respective commercial banks. The correlation coefficients and regression models are estimated to test the significance and importance of credit diversification on the credit risk of Nepalese commercial banks. The study showed that overdraft loan has a negative effect on non-performing loan and net interest margin. It means that increase in overdraft loans leads to decrease in non performing loan and net interest margin. Similarly, term loan has a positive effect on non performing loan. It means that increase in term loans leads to increase in non-performing loan. In addition, deprived sector loan has a positive effect on non-performing loan. It means that increase in deprived sector loan leads to increase in non-performing loan. Likewise, real estate loan has a negative effect on non-performing loan and net interest margin. It means that increase in real estate loan leads to decrease in non-performing loan and net interest margin. Moreover, staff loan has a positive effect on non-performing loan and net interest margin. It means that increase in staff loan leads to increase in non-performing loan and net interest margin.
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Sarfo, Yaw, Oliver Musshoff, and Ron Weber. "Loan officer rotation and credit access: evidence from Madagascar." Agricultural Finance Review 80, no. 1 (2019): 51–67. http://dx.doi.org/10.1108/afr-05-2019-0049.
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Purpose With exclusive data from a commercial microfinance institution (MFI) in Madagascar, the purpose of this paper is to investigate if loan officer rotation (change of loan officer) has an effect on credit access (loan approval) in rural and in urban areas. The authors further analyze how the frequency of loan officer rotation affects credit access in rural and in urban areas. Design/methodology/approach The authors apply propensity score matching to compare credit access between loan applicants who experienced loan officer rotation and loan applicants who experienced no loan officer rotation in rural and in urban areas. Findings Results show that loan officer rotation has a positive and statistically significant effect on credit access. The authors observe further that loan officer rotation has a different effect on credit access in rural and in urban areas. Whilst rural loan applicants who experienced loan officer rotation are more likely to have credit access, urban loan applicants show no statistically significant effect of loan officer rotation on credit access. For the frequency effect on credit access, the authors observe that one loan officer rotation has a positive and statistically significant effect on credit access whereas results are mixed for two loan officer rotations. Research limitations/implications Even though the authors can show that loan officer rotation can improve credit access to loan applicants, especially in rural areas, the conditions in Madagascar are unique. Therefore, results need to be verified in other countries and institutional contexts. Practical implications From the perspective of MFI, the authors recommend that the management of MFI needs to provide better tools to loan officers to improve on the evaluation of agricultural loan products or standardize the assessment of agricultural loan products to improve on lending decisions. Further, if applicable, the authors recommend that MFI should consider using credit worthiness assessment procedures which rely less on loan officer’s judgment for loan evaluation, such as automated systems. From the perspective of loan applicants, the authors recommend that loan applicants should request for a change of loan officer if they experience successive loan applications rejection. Originality/value To the authors’ knowledge, this paper is the first to provide empirical evidence on the effect and frequency of loan officer rotation on credit access in Sub-Sahara Africa, and Madagascar, in particular.
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Febrina, Dian. "Analisis Pengaruh Portofolio Kredit Terhadap Kualitas Kredit dan Profitabilitas pada BPR Konvensional di Riau." Jurnal Daya Saing 3, no. 1 (2017): 1–11. http://dx.doi.org/10.35446/dayasaing.v3i1.75.
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Abstract: The present research was conducted at Riau Province. The purpose of this research is to influence of credit portfolio to non perfoming loan (NPL) and profitability (ROA) in Bank Perkreditan Rakyat (BPR) Convensional in Riau. The population of this research is a Bank Perkreditan Rakyat Convensional from annual report are listed in Perbarindo Riau during 2009-2013 with the number of saturation samples are 33 BPR in Riau. This research apply on using portofolio credit based on a type of used that is working capital loan, investment loan and consumer loan as an exogenous variable, credit quality (NPL) as an intervening variable and profitability (ROA) as a endogenous variable. The data were analyzed using path analysis. The result of this study indicate that working capital loan through credit quality indirectly significant negative effect on profitability, but the working capital loan directly positive effect on profitability. While investmen loan and consumer loan positive impact on profitability either directly or indirectly throught credit quality. Finally, credit quality and negative significant effect on profitability.
 Keywords: credit portfolio, working capital loan, investment loan, consumer loan, credit quality, non performing loan (NPL), profitability, return on assets (ROA) and path analysis.
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Agaba, Francis, Caleb Tamwesigire, and Marus Eton. "Credit Risk Management Practices and Loan Performance of Commercial Banks in Uganda." Business Perspective Review 4, no. 1 (2022): 16–28. http://dx.doi.org/10.38157/bpr.v4i1.394.
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Purpose: The study examined the relationship between Credit Risk Management Practices and Loan Performance of Commercial Banks in Mbarara City. The study covered 19 commercial banks. Method: A correlational design was used to establish the relationship between different credit risk management practices and Loan Performance in selected commercial banks in the city. The study used a structured questionnaire to collect numerical data from the credit staff and management of 19 commercial banks. Correlation and regression tests to analyze the relationships and effects of Credit risk management and Loan Performance of commercial banks in Mbarara city Findings: The study found a significant relationship between credit risk identification and loan performance; credit risk assessment and loan performance; credit risk monitoring and loan performance; and credit risk control and loan performance. The study also found that some commercial banks did not have experts to accurately predict credit risks nor evaluate the consequences of the decisions taken by loan officers. Implication: Banks should source experts who can analyze and predict risks and evaluate their consequences on the bank. The bank should adopt the tool of 5cs of credit management, with this it will develop a good loan book that shall lead to good loan performance. Limitations: We still don't know clients' perceptions of the different credit risk management practices. Therefore, a qualitative study to assess clients' perception of the credit management practices in commercial banks should be conducted.
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Ahmed, Jaleel, Hui Xiaofeng, Muhammad Usman Virk, and Muhammad Abdullah. "Investigation of Causal Relationship between Trade Credit and Bank Loan during 2008 Financial Crisis." Journal of Asian Business Strategy 5, no. 5 (2015): 90–98. http://dx.doi.org/10.18488/journal.1006/2015.5.5/1006.5.90.98.
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This research paper attempts to investigate the causal relationship between trade credit and bank loan during 2008 financial crisis. After collecting data from 2005 to 2011 we have used Two Stage Least Square (TSLS) estimation technique. We have found that trade credit supply and bank loan are simultaneously determined and have a complementary effect during 2008 financial crisis. On the other side trade credit demand and bank loan are simultaneously determined where bank loan causes trade credit demand to decrease. A substitution effect has been observed between trade credit demand and bank loan during 2008 financial crisis. Net trade credit and bank loan have a positive and significant impact on each other. These relationships are also remains significant as in before and after financial crisis. Financial crisis have a positive impact on trade credit supply and demand. We have also found an inverse relationship between financial crisis and bank loan.
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Henning, Bougard, Jordaan, and Matthews. "Factors Affecting Successful Agricultural Loan Applications: The Case of a South African Credit Provider." Agriculture 9, no. 11 (2019): 243. http://dx.doi.org/10.3390/agriculture9110243.
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The purpose of the paper is to determine the influence of different factors used by a formal credit institution to evaluate loan applications in the agricultural sector. The research attempts to capture the actual factors considered by credit institutions rather than the traditional factors found in literature. Loan applications from 128 farmers, predominantly commercial farmers, were obtained from a credit institution with branches situated in various provinces of South Africa. Data consisted of loan application information which is broader than the financial information normally obtained in credit research, and the final decision of the credit provider. Principal component logistic regression was used to investigate the likeliness with which loan application variables influence the outcome of the loan application. Results indicate that loan applications that are more likely to be successful are older more experienced farmers, who can provide sufficient collateral, have more years of business with the credit provider, have an acceptable credit history, request smaller loan amounts, have lower interest expense ratio, higher production cost ratios, and have diversification strategies. This paper contributes to knowledge on information used by financial credit providers (institutions) in classifying agricultural loan applications as successful as guided by actual factors used in credit decision making by the credit provider.
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Timuneno, Antonius Yohanes William. "PENGARUH EVALUASI KREDIT DAN PENGAWASAN KREDIT TERHADAP RISIKO KREDIT MACET." Journal of Management Small and Medium Enterprises (SMEs) 15, no. 2 (2022): 207–23. http://dx.doi.org/10.35508/jom.v15i2.6724.
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The research background is risk of the loan credit which is the one problem which inseperable from the operations system in credit service unions in district Oebobo of Kupang City. In observation the loan default, the efforts of tackling was involved two important factors were credit evaluation and control credit. This research was conducted in 15 units of credit unions that were selected based on the criteria previously and the sample were 30 persons from credit management and supervisory elements. Technique in collecting data is observation interview, questionnnary, and documentation. The result proved that the Credit Evaluation and Credit Control had the significant partially influence to minimizing the Risk of Loan default on the credit union in district Oebobo of Kupang City through simple Linear Regression Analysis results in agreement line standard. This research also proved that the managements in credit union have to maintain and applying the evaluation and control credit system that usefull to minimize the level of loan default risk. Although it can be minimized with applying the balance credit.
 Keywords: Credit Control, Credit Evaluation, Risk of Loan
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Ndanitsa, M. A., Adetunji, A. O., Mohammed, D., and Ndako, N. "EFFECTS OF AGRICULTURAL CREDIT DELIVERY ON INCOME OF ARABLE CROP FARMERS IN NIGER STATE OF NIGERIA." Journal of Agripreneurship and Sustainable Development 4, no. 1 (2021): 10–32. http://dx.doi.org/10.59331/jasd.v4i1.181.
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The study examined performance of agricultural credit delivery on income of arable crop farmers in Niger State, Nigeria. Multi-stage sampling technique was used for the study and data were collected using structured questionnaires and interview schedules from a total sample size of 326. Data were analyzed using descriptive statistics, simultaneous equation model and Chow test. The result revealed that 60% of the respondents were within the age brackets of 31 – 50 years with average age of 45 years. Most (78%) of the respondents cultivated 0.5 – 3.0 hectares. The determinants of agricultural credit, potential credit demand and loan repayment were all significant at P≤0.01 probability level. Interest on loan, loan application cost, farm size and predicated loan repaid were all significant and important determinants of credit demand by farmers. However, coefficient of application form cost was negative; suggesting that high cost of loan application reduces credit demand among the beneficiaries. Furthermore, lending experience, transaction cost, credit source, interest on loans was significant at P≤0.1, P≤0.1 and P≤0.05 and P≤0.01, respectively, as the determinants of credit supply. The results revealed that late release of approved fund for disbursement, inadequate information and equipment, insufficient funds, loan diversion, illiteracy and lack of awareness; poor loan repayment and lack of infrastructure were the constraints affecting the loan beneficiaries. The constraints to credit by farmers included insufficient amount of loan, excessive bureaucracy, poor credit delivery, high interest rate, demand for collateral, short repayment period, farvouritism, lack of supervision and advisory services and dishonesty among lenders were the constraints affecting loan delivery by the beneficiaries. It was recommended that, formation of cooperative societies, use of credible credit officers and increase in farm size be put in place to effect the needed change in credit delivery in the study area.
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Davaadorj, Zagdbazar, and Jorge Brusa. "Informationally advantaged lenders and the credit derivative market: Evidence from Loan only Credit Default Swap (LCDX)." Applied Finance Letters 13 (March 27, 2024): 63–76. http://dx.doi.org/10.24135/afl.v13i.738.
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This paper explores the informational role of the Loan Only Credit Default Index (LCDX) on the pricing of syndicated loans. Despite an extensive body of research on credit indices and loan pricing, limited studies have comprehensively assessed the complex relationship between the LCDX and individual loan spreads. Contrary to indices like the CDX, which are largely linked to corporate bonds, the LCDX directly pertains to the syndicated secured loan market, offering valuable insights about the overall credit default market and the cost of credit risk insurance. Preliminary results reveal a pronounced positive correlation between the LCDX spread and the syndicated loan spread, particularly noticeable amongst borrowers with lower credit quality. The paper highlights the LCDX's pivotal role in conveying secondary credit market information, with critical implications for credit risk management and financial regulations.
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Mulyani, Elis Listiana, Dewi Permata Sari, and Alfin Nurfahmi Mufreni. "PENGARUH ALOKASI KREDIT DAN HARGA KREDIT TERHADAP PROFITABILITAS DENGAN RISIKO KREDIT SEBAGAI VARIABEL INTERVENING." Jurnal Ekonomi Manajemen 8, no. 1 (2023): 49–60. http://dx.doi.org/10.37058/jem.v8i1.5784.
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The purpose of this study is to find out and analyze the effect of credit ratio(Loan to Funding Ratio / LFR) and credit price (Loan Pricing) on profitability (Net Profit Margin) with credit risk (Non Performing Loan / NPL) as Intervening Variable. The research was conducted on banking listed on the Indonesia Stock Exchange.The research method used is descriptive analysis with the data used is secondary data in the form of Income and Balance Sheet statements on banks listed on the Indonesia Stock Exchange. Analytical tools use financial ratios in the form of Loan to Funding Ratio (LFR), Loan Pricing, Credit Risk (Non Performing Loan) and Net Profit Margin and intervening regression analysis.Based on the results of research it is known that Credit Risk (NPL) becomes a partial intervening variable between Loan Funding Rastio against Net Profit Margin (NPM), while between Credit Price (Loan Pricing)against Net Profit Margin, Credit Risk (NPL) becomes a perfect intervening variable. Tujuan Penelitian ini adalah untuk mengetahui dan menganalisis pengaruh rasio kredit (Loan to Funding Ratio /LFR) dan harga kredit (Loan Pricing) terhadap profitabilitas (Net Profit Margin) dengan risiko kredit (Non Performing Loan/NPL) sebagai Variabel Intervening .Penelitian dilakukan pada Perbankan yang tercatat di Bursa Efek Indonesia.Metode Penelitian yang digunakan adalah analisis deskriptif dengan data yang digunakan adalah data sekunder berupa laporan Keuangan Laba Rugi dan Neraca pada Perbankan yang tercatat di Bursa Efek Indonesia .Alat analisis menggunakan rasio keuangan berupa Loan to Funding Ratio (LFR), Loan Pricing ( Harga Kredit) , Risiko Kredit ( Non Performing Loan) dan Net Profit Margin dan analisis regressi intervening . Berdasarkan hasil penelitian diketahui bahwa bahwa Risiko Kredit (NPL) menjadi variable intervening parsial antara Loan Funding Rastio terhadap Net Profit Margin (NPM), sedangkan antara Harga Kredit ( Loan Pricing) terhadap Net Profit Margin, Risiko Kredit (NPL) menjadivariable intervening sempurna .
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Akram, Muhammad, Zahid Iqbal, and Muhammad Mudasir Afzal. "LOAN CHARACTERISTICS & LOAN CREDIT TERMS: DOES IT MATTER IN A MICROFINANCE CONTRACT?" Journal of Arts & Social Sciences 10, no. 2 (2023): 57–66. http://dx.doi.org/10.46662/jass.v10i2.379.
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This study examined the relationship between loan characteristics and loan credit conditions on loan repayment issues in order to assist Microfinance Institutions (MFIs) in Pakistan in improving their loan payback performance. The study also examines the link between loan credit terms and problems with loan repayment as well as the mediating function of client-business performance in the relationship between loan characteristics and loan repayment concerns. A measurement model and a structural model were both used in this investigation, which used a two-stage structural equation modelling methodology. The measurement model, also known as the outer model, was employed to evaluate the reliability and validity of the data collection technology. PLS-SEM bootstrapping was performed to test the hypothesis using the structural model (inner model). The results are consistent with the assumption that loan terms and conditions have a positive impact on microenterprise loan repayment concerns. The findings of this study also lend credence to the idea that client-business performance functions as a mediator in the relationships between loan characteristics and problems with loan repayment as well as between loan credit terms and problems with loan repayment. There hasn't been much research done in Pakistan to date on how loan characteristics and loan credit terms directly affect the challenges microenterprises face in repaying loans. The business performance of microenterprises is also investigated in relation to loan features, loan credit terms, and loan repayment issues.
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Mwakujonga, Joshua, and Coretha Komba. "Influence of Credit Risk Management Practices on Loan Performance: A Case of Selected Microfinance Institutions in Tanzania." Journal of Policy and Development Studies 15, no. 2 (2024): 26–35. http://dx.doi.org/10.4314/jpds.v15i2.3.
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This research investigates the influence of credit risk management practices on loan performance of microfinance institutions. The variables of interest in this study are credit appraisal, credit supervision, credit monitoring (as predictor variables) and loan performance (as dependent variable). However, the study includes average credit processing time as a control variable. The study focuses on five microfinance institutions operating in Tanzania. The research utilizes probability and non-probability sampling to select 140 respondents from the study population. The study collected both primary and secondary data. Data analysis was carried out by applying multiple regression analysis following the assumptions of ordinary least squares (OLS). The findings reveal that credit supervision, credit monitoring, and average credit process time are significant in influencing loan performance in microfinance institutions. Contrary, there is no evidence, whatsoever to suggest that credit supervision influences loan performance in microfinance institutions.
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Putri, Tika Larasati Harjito. "Analysis of Credit Risk Differences Based on Risk Management and Related Banking Regulation : Case Study of Banks in Indonesia and Malaysia." Owner 9, no. 1 (2025): 162–78. https://doi.org/10.33395/owner.v9i1.2394.
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Currently, banks in Indonesia face extortionate loan interest rates due to high credit risks in disbursing loan. This is different from what happens in Malaysia, which has lower credit risk and cheaper credit interest rates. This makes it difficult for banks in Indonesia to achieve loan growth target set by Bank Indonesia in the last 3 years and they are considered not optimal in distributing loan. It happened since they focused more on how to gain more loan profit than do loan expansion. This explanatory case study research aims to investigate and explain reasons behind high credit risk based on risk management and related banking regulation implemented in Indonesia and Malaysia and its impact on bank performance such as NIM and loan productivity. Descriptive analysis was used to explain result of data analysis and information obtained from official websites of Bank Mandiri, Bank BRI, and Bank UOB Malaysia for the period 2021 – 2023. This study contributes to provide policy advice for Indonesia banking regulator to control credit risks as part of loan interest rate calculations in order to optimalize bank performance.
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Lestari, Ni Putu Dian Ari, I. Made Sudarma, and I. Putu Pasek Bagiartha. "FAKTOR-FAKTOR YANG MEMPENGARUHI KREDIT MACET PADA KOPERASI SIMPAN PINJAM SEJAHTERA (KSP) DI KOTA MATARAM." Waisya : Jurnal Ekonomi Hindu 2, no. 2 (2023): 88–99. http://dx.doi.org/10.53977/jw.v2i2.1570.
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Cooperatives are a form of non-bank financial institutions that are already operating. One of them is a savings and loan cooperative. Savings and loan cooperative activities that provide credit to members. Borrowing from savings and loan cooperatives is faster and easier in the loan disbursement process compared to borrowing at a bank. The purpose of this study was to determine the factors that influence bad credit at the Prosperous Savings and Loans Cooperative (KSP) in the city of Mataram. The data in this study were obtained using non-participant observation methods, interviews and documentation. Data analysis techniques used data reduction, data presentation and data verification. The results of this study indicate the factors of bad credit in cooperatives that cause failures or calamities that befall members' businesses, causing cooperative members to lose their business and directly affect ongoing credit payments resulting in bad credit. Of all the problems that exist, this research provides a solution for bad credit, namely by visiting the homes of members who have bad credit so that communication does not break up between the cooperative and members who are experiencing bad credit, if it is due the credit member can pay interest on the loan before paying the loan tree.
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Adegbola, A. S., A. O. Ajayi-Owoeye, and Yimka S. A. Alalade. "Credit Management Practices and Loan Default in Deposit Money Banks (DMBs) in Osun State, Nigeria." South Asian Journal of Social Studies and Economics 18, no. 1 (2023): 37–47. http://dx.doi.org/10.9734/sajsse/2023/v18i1650.
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Background: Loan default has been shown in existing literature to have a negative impact on banks as it reduces the performance of banks and may lead to total collapse of these institutions. The high rate of default associated with loans in Nigerian banks is indicative of existing poor credit management practices by the banks.
 Aim: As such, this study examined the effect of credit management practices on loan default in deposit money banks in Osun State.
 Methods: The study employed the survey research design, using a well-structured questionnaire to collect responses of one hundred and twenty (120) officers and managers from two hundred and fifty two (252) officers and managers of sixty (60) bank branches in Osun State. Using descriptive and multiple linear regressions, the collated data was presented using tables, while the research hypothesis was tested at the 5% level of significance.
 Findings: The study found that credit management has no significant effect on loan default, with both credit appraisal and credit monitoring exhibiting negative but non-significant effects on loan default, and credit collection policy exhibiting positive and significant effect on loan.
 Conclusion: The study concluded that credit management practices have no significant effect on loan default. In other words, loan default experienced in the banks were not influenced by the loan management practices put in place. While the processes were in existence in the banks under investigation, they have not been able to affect the incidences of loan default.
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Wang, Yu-Lin, Chien-Hui Lee, and Po-Sheng Ko. "Do Loan Guarantees Alleviate Credit Rationing and Improve Economic Welfare?" Sustainability 12, no. 9 (2020): 3922. http://dx.doi.org/10.3390/su12093922.
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By designing credit contracts with inversely related interest rates and collateral, banks can overcome the problems of adverse selection and moral hazard when there is an informational asymmetry in competitive credit markets. One salient result points out that, if borrowers’ insufficient endowments of wealth cause a binding collateral constraint, a credit rationing equilibrium arises because of collateral’s inability to achieve perfect sorting. The purpose of this paper is to examine the consequences of government loan guarantees on equilibrium credit contracts and economic welfare. More specifically, the effects of loan guarantees on interest rates, collateral, and credit rationing were studied. Our results suggest that government loan guarantees should target high-risk entrepreneurs. Loan guarantees targeting high-risk entrepreneurs reduce a pledge of collateral in credit contracts, drop social cost, and increase economic welfare. Under the circumstances that borrowers’ insufficient wealth causes a binding collateral constraint, loan guarantees targeting high-risk entrepreneurs alleviate the problem of credit rationing and improve economic welfare.
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Xu, Huacen (Brin), Heying Jenny Zhan, Claire Elizabeth-Ellen James, Lauren Denise Fannin, and Yue Yin. "Double bind in loan access in China: the reification of gender differences in business loans." International Journal of Gender and Entrepreneurship 10, no. 4 (2018): 182–97. http://dx.doi.org/10.1108/ijge-08-2017-0048.
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Purpose This paper aims to examine gender differences in credit access and credit default. Design/methodology/approach Using panel data drawn from 917 valid credit borrowers covering the period 2012 to 2015 drawn from among 6,849 study subjects and a national household financial survey (n = 29,500) conducted in China, this study focuses on gender differences in small and micro entrepreneurs’ financial behavior, specifically with respect to credit access and credit default. Findings The study revealed the following: Women expressed having more barriers to obtaining a business loan than men; gender had a significant effect on women’ credit default; and women were less likely to default a loan than male loan borrowers did. An exploration of the reasons for credit access and default found that female loan applicants were more likely to display a lack of knowledge and confidence in loan application. Originality/value The study contributes to literature by using the Marxian concept of reification in explaining women and their financial behaviors in China.