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Aiyegbeni, Gifty, Yang Li, Joseph Annan, and Funminiyi Adebayo. "Credit Rating Prediction Using Different Machine Learning Techniques." International Journal of Data Science and Advanced Analytics 5, no. 5 (2023): 219–38. http://dx.doi.org/10.69511/ijdsaa.v5i5.193.
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Credit rating prediction is a crucial task in the banking and financial industry. Financial firms want to identify the likelihood of customers repaying loans or credit. With the advent of machine learning algorithms and big data analytics, it is now possible to automate and improve the accuracy of credit rating prediction. In this research, we aim to develop a machine learning-based approach for customer credit rating prediction. Machine learning algorithms, including decision trees, random forests, support vector machines, and logistic regression, were evaluated and compared in terms of accuracy, precision, and AUC. Feature selection was also performed to analyze the importance of different features in predicting credit ratings. Findings suggested that status, duration, credit history, amount, savings, other debtors, property, and employment duration are the most important features in predicting credit ratings. Results showed that the support vector machine algorithm did best in predicting bad credits, achieving an accuracy of 79.7%, AUROC of 0.76, and a precision of 0.88. After optimization, an AUROC of 78% was obtained. This is a 78% accuracy for properly identifying bad credits. This research demonstrates the potential of machine learning algorithms for customer credit rating prediction and could have significant implications for the banking and financial industry by enabling more accurate and efficient credit rating predictions and reducing the risk of defaults and financial losses.
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Fauser, Daniel V., and Andreas Gruener. "Corporate Social Irresponsibility and Credit Risk Prediction: A Machine Learning Approach." Credit and Capital Markets – Kredit und Kapital: Volume 53, Issue 4 53, no. 4 (2020): 513–54. http://dx.doi.org/10.3790/ccm.53.4.513.
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This paper examines the prediction accuracy of various machine learning (ML) algorithms for firm credit risk. It marks the first attempt to leverage data on corporate social irresponsibility (CSI) to better predict credit risk in an ML context. Even though the literature on default and credit risk is vast, the potential explanatory power of CSI for firm credit risk prediction remains unexplored. Previous research has shown that CSI may jeopardize firm survival and thus potentially comes into play in predicting credit risk. We find that prediction accuracy varies considerably between algorithms, with advanced machine learning algorithms (e. g. random forests) outperforming traditional ones (e. g. linear regression). Random forest regression achieves an out-of-sample prediction accuracy of 89.75% for adjusted R2 due to the ability of capturing non-linearity and complex interaction effects in the data. We further show that including information on CSI in firm credit risk prediction does not consistently increase prediction accuracy. One possible interpretation of this result is that CSI does not (yet) seem to be systematically reflected in credit ratings, despite prior literature indicating that CSI increases credit risk. Our study contributes to improving firm credit risk predictions using a machine learning design and to exploring how CSI is reflected in credit risk ratings.
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Nguyen, Quoc Giang, Linh Hoang Nguyen, Md Monir Hosen, et al. "Enhancing Credit Risk Management with Machine Learning: A Comparative Study of Predictive Models for Credit Default Prediction." American Journal of Applied Sciences 07, no. 01 (2025): 21–30. https://doi.org/10.37547/tajas/volume07issue01-04.
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This study investigates the application of machine learning algorithms for predictive analytics in credit risk management, aiming to enhance the accuracy of predicting credit defaults. The research compares multiple machine learning models, including logistic regression, decision trees, random forests, gradient boosting, XGBoost, and LightGBM, using a real-world credit risk dataset. The study focuses on evaluating the models' performance based on metrics such as accuracy, precision, recall, and F1-score. The results show that ensemble models, particularly XGBoost and LightGBM, outperform traditional algorithms in terms of predictive accuracy and computational efficiency, demonstrating their ability to effectively handle complex datasets. The comparative analysis highlights the strengths and weaknesses of each model, providing insights into the trade-offs between interpretability and predictive power. XGBoost and LightGBM are found to be highly effective for credit risk prediction, though challenges such as model interpretability and overfitting remain. The findings suggest that machine learning offers a promising approach for improving credit risk management, with implications for the financial industry to make more informed, data-driven lending decisions. The study underscores the importance of addressing interpretability concerns and data quality issues in real-world applications, paving the way for future advancements in machine learning for credit risk prediction.
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Sarder Abdulla Al Shiam, Md Mahdi Hasan, Md Jubair Pantho, et al. "Credit Risk Prediction Using Explainable AI." Journal of Business and Management Studies 6, no. 2 (2024): 61–66. http://dx.doi.org/10.32996/jbms.2024.6.2.6.
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Despite advancements in machine-learning prediction techniques, the majority of lenders continue to rely on conventional methods for predicting credit defaults, largely due to their lack of transparency and explainability. This reluctance to embrace newer approaches persists as there is a compelling need for credit default prediction models to be explainable. This study introduces credit default prediction models employing several tree-based ensemble methods, with the most effective model, XGBoost, being further utilized to enhance explainability. We implement SHapley Additive exPlanations (SHAP) in ML-based credit scoring models using data from the US-based P2P Lending Platform, Lending Club. Detailed discussions on the results, along with explanations using SHAP values, are also provided. The model explainability generated by Shapely values enables its applicability to a broad spectrum of industry applications.
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Hjouji, Zaynab, Imane Hasinat, and Amal Hjouji. "A New Method in Machine Learning Adapted for Credit Risk Prediction of Bank Loans." Statistics, Optimization & Information Computing 13, no. 3 (2024): 1209–32. https://doi.org/10.19139/soic-2310-5070-1476.
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The recent global financial crisis has significantly impacted the financial system, leading to major bank failures and prompting a reevaluation of credit risk management models. Given its critical role in maintaining banking stability, effective credit risk forecasting methods are essential. In light of this, various studies have introduced techniques to analyze, detect, and prevent bank credit defaults. In this paper, we present a new approach for predicting credit risk, known as the “Method of Separating the Learning Set into Two Balls.” This method involves partitioning a learning set into two distinct categories: the "Performing Ball," which contains feature vectors of customers with non-defaulting credits, and the "Non-Performing Ball," which includes vectors of customers with defaulting credits. To predict a customer’s default risk, it is sufficient to determine which ball their feature vectors belong to. If a customer’s vectors do not fall into either category, additional analysis is required for making a credit decision. We evaluated the performance of this method through extensive experimental tests and a comparative analysis. The findings suggest that our approach shows considerable promise for enhancing credit risk prediction in the banking sector.
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Xiang, Siyu, and Haowen Yan. "Credit Default Prediction Based on Random Forest." Applied and Computational Engineering 97, no. 1 (2024): 89–95. http://dx.doi.org/10.54254/2755-2721/97/20241348.
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Abstract. To help the lender to make reasonable prediction judgments on the lender in advance, to get the most appropriate way of lending amount, as well as facing the risk of a reasonable response program and the ability to cope with the need to make predictions in advance before lending for personal credit risk. This paper aims to predict credit default based on random forest, logistic regression and decision tree algorithms, by comparing and analyzing the advantages and disadvantages of these algorithms, this paper finally chooses the random forest algorithm. This paper concludes that in predicting the risk of credit default, the three characteristics of Credit amount, Duration and Job have the most significant influence in predicting the risk value of the borrower, Credit amount is the most important factor that affects the risk value, Duration is also a key factor, a more relaxed repayment period will reduce the pressure of repayment, and thus reduce the risk of default. Job, different occupations, different incomes and different income stability will lead to different repayment abilities of each borrower, according to the repayment ability and the loan amount of the comparison, you can initially arrive at the corresponding risk value and the development of the corresponding risk control strategy.
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Nooji, Pavitha, and Shounak Sugave. "Explainable ensemble technique for enhancing credit risk prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 917–24. https://doi.org/10.11591/ijai.v13.i1.pp917-924.
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Credit risk prediction is a critical task in financial institutions that can impact lending decisions and financial stability. While machine learning (ML) models have shown promise in accurately predicting credit risk, the complexity of these models often makes them difficult to interpret and explain. The paper proposes the explainable ensemble method to improve credit risk prediction while maintaining interpretability. In this study, an ensemble model is built by combining multiple base models that uses different ML algorithms. In addition, the model interpretation techniques to identify the most important features and visualize the model's decisionmaking process. Experimental results demonstrate that the proposed explainable ensemble model outperforms individual base models and achieves high accuracy with low loss. Additionally, the proposed model provides insights into the factors that contribute to credit risk, which can help financial institutions make more informed lending decisions. Overall, the study highlights the potential of explainable ensemble methods in enhancing credit risk prediction and promoting transparency and trust in financial decision-making.
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Nooji, Pavitha, and Shounak Sugave. "Explainable ensemble technique for enhancing credit risk prediction." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 917. http://dx.doi.org/10.11591/ijai.v13.i1.pp917-924.
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<span>Credit risk prediction is a critical task in financial institutions that can impact lending decisions and financial stability. While machine learning (ML) models have shown promise in accurately predicting credit risk, the complexity of these models often makes them difficult to interpret and explain. The paper proposes the explainable ensemble method to improve credit risk prediction while maintaining interpretability. In this study, an ensemble model is built by combining multiple base models that uses different ML algorithms. In addition, the model interpretation techniques to identify the most important features and visualize the model's decision-making process. Experimental results demonstrate that the proposed explainable ensemble model outperforms individual base models and achieves high accuracy with low loss. Additionally, the proposed model provides insights into the factors that contribute to credit risk, which can help financial institutions make more informed lending decisions. Overall, the study highlights the potential of explainable ensemble methods in enhancing credit risk prediction and promoting transparency and trust in financial decision-making.</span>
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Liu, Xiqing. "Credit Risk Classification and Prediction Based on Deep Neural Network Algorithm." Advances in Economics, Management and Political Sciences 88, no. 1 (2024): 235–41. http://dx.doi.org/10.54254/2754-1169/88/20241007.
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This study predicts whether a user has defaulted based on correlation analysis and deep neural network algorithms. The results of the study show that the occurrence of default by a user is positively correlated with age, family, years of employment, and credit length, and negatively correlated with income, amount, rate, status, and percentage of income. After model training and testing, the prediction accuracy was 81.68% on the training set and 81.68% on the test set. Specifically, there were 2858 correct predictions and 641 incorrect predictions in the training set, of which 469 incorrectly predicted that no default had occurred as having occurred and 172 incorrectly predicted that a default had occurred as not having occurred, and there were also 2858 correct predictions and 641 incorrect predictions in the test set. The results of this study show that the established model has high reliability and accuracy in accurately predicting whether a user has defaulted or not, which provides an important reference for risk assessment and decision-making.
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Kealhofer, Stephen. "Quantifying Credit Risk I: Default Prediction." Financial Analysts Journal 59, no. 1 (2003): 30–44. http://dx.doi.org/10.2469/faj.v59.n1.2501.
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Twala, Bhekisipho. "Combining classifiers for credit risk prediction." Journal of Systems Science and Systems Engineering 18, no. 3 (2009): 292–311. http://dx.doi.org/10.1007/s11518-009-5109-y.
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Kamalloo, Ehsan, and Mohammad Saniee Abadeh. "Credit Risk Prediction Using Fuzzy Immune Learning." Advances in Fuzzy Systems 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/651324.
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The use of credit has grown considerably in recent years. Banks and financial institutions confront credit risks to conduct their business. Good management of these risks is a key factor to increase profitability. Therefore, every bank needs to predict the credit risks of its customers. Credit risk prediction has been widely studied in the field of data mining as a classification problem. This paper proposes a new classifier using immune principles and fuzzy rules to predict quality factors of individuals in banks. The proposed model is combined with fuzzy pattern classification to extract accurate fuzzy if-then rules. In our proposed model, we have used immune memory to remember good B cells during the cloning process. We have designed two forms of memory: simple memory andk-layer memory. Two real world credit data sets in UCI machine learning repository are selected as experimental data to show the accuracy of the proposed classifier. We compare the performance of our immune-based learning system with results obtained by several well-known classifiers. Results indicate that the proposed immune-based classification system is accurate in detecting credit risks.
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Wang, Simiao, Shengqi You, and Shenwei Zhou. "Loan Prediction Using Machine Learning Methods." Advances in Economics, Management and Political Sciences 5, no. 1 (2023): 210–15. http://dx.doi.org/10.54254/2754-1169/5/20220081.
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Credit risk has always been the most important risk faced by commercial banks. Credit risk management has important practical significance for preventing credit risk. With the emerging of machine learning algorithms, numerous frameworks, including linear regression, support vector machine, random forest and decision tree are proposed with satisfying performance and robust accuracy. This paper will focus on predicting credit outcomes and calculating forecast accuracy from a given dataset. This paper adopts three algorithms, decision tree, random forest and logistic regression, to calculate the dataset from the Bank of Portugal separately and obtain relevant conclusions. Finally, the authors evaluate the advantages and disadvantages of the three methods according to the accuracy of the prediction results, and the conclusion is described as follow, First, all three methods have great potential on handling loan prediction task. Second, the logistic regression algorithm is the most accurate, which obtains 86.4% accuracy.
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Li, Guiping. "The prediction algorithm of credit risk of science and technology finance based on cloud computing." Journal of Computational Methods in Sciences and Engineering 22, no. 1 (2022): 235–51. http://dx.doi.org/10.3233/jcm-215723.
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In order to effectively guarantee the effect of credit risk prediction of science and technology finance and improve the ability of risk prediction, a credit risk prediction algorithm of science and technology finance based on cloud computing is proposed. The logistic regression model is used to predict, and the financial indicators of science and technology credit are selected as the model covariates. According to the characteristics and strong correlation of many financial indicators of science and technology credit, this paper constructs the final index system of online supply chain technology credit risk evaluation based on SMEs. Then the principal component analysis method is used to select the principal component. Combined with the penalty method, the data space dimension of financial indicators is further reduced, and the unrelated principal components are obtained. On this basis, a logistic regression model is established to predict the credit risk by taking the selected main components as covariates. The experimental results show that the algorithm has a good fit to the credit risk of 16 science and technology credit enterprises, and the risk prediction ability is significantly improved, which can effectively guarantee the effect of science and technology credit risk prediction.
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Songwei Fan. "A SC Financial Credit Risk Assessment Model Based on Particle Filter and SVM with Gain Information." Journal of Electrical Systems 20, no. 7s (2024): 263–72. http://dx.doi.org/10.52783/jes.3268.
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The accuracy of credit risk prediction in SC financing is critical for many enterprises, based on machine learning algorithms can be good for SME credit risk assessment research, for this reason, this paper establishes a combinatorial model that can improve credit risk prediction, using support vector machine (SVM) and particle filtering to achieve credit risk classification and prediction, we and introduce information gain (IG) to extract the prediction of The model uses SVM and particle filtering to classify and predict credit risk, and we introduce information gain (IG) to extract feature variables that contribute significantly to the prediction results and optimize model feature inputs. Compared with the benchmark model, the prediction accuracy of the model in this paper is 97.62%, which is 8.97% higher than that of SVM, and the performance of IG with feature optimization improves the prediction accuracy by another 3%.
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Wang, Qiping, Raymond Yiu Keung Lau, Wai Ting Eric Ngai, Jason Bennett Thatcher, and Wei Xu. "Consumers’ Opinion Orientations and Their Credit Risk: An Econometric Analysis Enhanced by Multimodal Analytics." Journal of the Association for Information Systems 25, no. 4 (2024): 1117–56. http://dx.doi.org/10.17705/1jais.00856.
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The rise of financial technology (fintech) has motivated practitioners and researchers to explore alternative data sources and enhanced credit scoring methods for better assessment of consumers’ credit risk. In this study, we examine whether deep-level diversity derived from consumers’ multimodal social media posts (i.e., alternative data) can enhance credit risk assessment or not. First, we propose novel lifestyle-based risk constructs (e.g., opinion risk) to capture consumers’ deep-level diversity. Second, we incorporate these lifestyle-based risk constructs into econometric models to empirically evaluate the relationship between consumers’ deep-level diversity and their credit risk. Using a credit scoring dataset provided by a fintech firm listed on Nasdaq, our econometric analysis reveals that consumers’ opinion risk constructs extracted from their multimodal social media posts are positively associated with their credit risk. Furthermore, our results show that the proposed opinion risk constructs can significantly improve the effectiveness of predicting consumers’ credit risk. Interestingly, our empirical results also show that combining the opinion risk constructs derived from images and text can significantly improve the effectiveness in credit risk prediction. This work contributes to the fintech domain by proposing novel lifestyle-based risk constructs for decision support in the credit scoring context.
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Hossain, Safayet, Ashadujjaman Sajal, Sakib Salam Jamee, et al. "Comparative Analysis of Machine Learning Models for Credit Risk Prediction in Banking Systems." American Journal of Engineering and Technology 07, no. 04 (2025): 22–33. https://doi.org/10.37547/tajet/volume07issue04-04.
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The increasing complexity of credit risk management in banking systems has led to the adoption of machine learning techniques to improve the prediction of loan defaults. This study evaluates and compares the performance of several machine learning models—Logistic Regression, Random Forest, Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Neural Networks—in predicting credit risk. The models were tested on a comprehensive dataset containing demographic, financial, and historical loan data. Performance was assessed based on accuracy, precision, recall, F1-score, AUC, and confusion matrix analysis. The results indicate that Gradient Boosting (XGBoost) outperformed the other models with the highest accuracy (88.7%), precision (89.5%), recall (80.3%), and AUC (91.3%), demonstrating its superior ability to predict loan defaults and manage credit risk effectively. Random Forest followed closely in performance, while Logistic Regression showed solid results with a focus on interpretability. Neural Networks and SVM performed well in accuracy but were more resource-intensive and less interpretable. The study concludes that Gradient Boosting (XGBoost) is the most suitable model for large-scale credit risk management due to its balance of high predictive power and ability to handle complex, imbalanced datasets. However, the choice of model should consider computational resources, interpretability requirements, and specific operational constraints of the banking institution.
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Balaji, Krishna, Aashima Gupta, and Shristy Goswami. "Credit Score Prediction." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 596–97. http://dx.doi.org/10.22214/ijraset.2023.57298.
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Abstract: A credit score is the numerical representation of a person’s credit worthiness, which is the likelihood that they will replay the borrowed money. Credit scores are used by lenders, such as banks and credit companies, to evaluate the risk of lending money or extending credit to an individual. Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without being explicitly programmed. Learning algorithms in many applications that we make use of daily. These algorithms are used for various purposes like data mining, image processing, predictive analytics, etc. to name a few. The main advantage of using machine learning is that, once an algorithm learns what to do with data, it can do its work automatically. Various algorithms used in machine learning, along with their drawbacks and usages, have been discussed briefly. A model is also made using one of these algorithms which provide minimum error of provide correct predictions based on the previous data available. The model is made using random forest classifier algorithm which gave the highest accuracy ,out of all the machine learning algorithm, up to 79.97%. The result was printed at last containing the predictions made on the test data , id and customer id.
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Chen, Songtao. "A Novel Ensemble Machine Learning Model for Credit Risk Prediction." Advances in Economics, Management and Political Sciences 46, no. 1 (2023): 277–81. http://dx.doi.org/10.54254/2754-1169/46/20230356.
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Due to the increasing uncertainty of credit risk in today's society, assessing the size of credit risk has become an indispensable part of the lender, and whether it can accurately assess the size of credit risk has become extremely important, which is directly related to the benefit loss of the lender. In light of this, numerous machine learning models have been employed to enhance the prediction accuracy and robustness of credit risk assessments. This paper proposes a hybrid model to better improve the prediction accuracy and robustness of the model. Firstly, this paper collected a data set about credit risk from kaggle, which has 11 independent variables and 1 dependent variable. The dependent variable is a binary variable, representing default or not. Then the data set is cleaned and sorted, and the data set is divided into training set and test set. Then seven machine learning models were used to fit and predict the data, and the three models with the best fitting effect were found through the two indexes of Aread Under Curve (AUC) and Accuracy: Random Forest, Gradient Boosting and Categorical Naive Bayes, and then mix these three models to obtain a mixed model. The experimental results show that compared with the seven machine learning models, the hybrid model has improved in AUC and still ranks first in Accuracy. Therefore, the hybrid model can well improve the accuracy of predicting credit risk and the robustness of the model.
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Huang, Yuhan, Zhixuan Li, Sizhe Pan, and Xinwei Wen. "A Review of Alternative Data in Credit Risk." Advances in Economics, Management and Political Sciences 31, no. 1 (2023): 213–21. http://dx.doi.org/10.54254/2754-1169/31/20231545.
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With the increasing number of loans given to people who previously have no credit history, the current credit risk prediction models trained by conventional data, namely credit history, social capital, etc., have become less effective. The aim of this essay is to figure out how alternative data is used in credit risk evaluation from those published essays. Based on EBSCO and ScienceDirect serving as the main database sources, we filter out 24 most relevant essays. We conclude that the alternative data can considerably optimize the verdict risk prediction models, as well as solve current financing conundrum when it comes to using alternative data to train the prediction models. However, the quality of alternative data and its ethical issues should require further investigations.
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Trustorff, Jan-Henning, Paul Markus Konrad, and Jens Leker. "Credit risk prediction using support vector machines." Review of Quantitative Finance and Accounting 36, no. 4 (2010): 565–81. http://dx.doi.org/10.1007/s11156-010-0190-3.
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Duman, Eren, Mehmet S. Aktas, and Ezgi Yahsi. "Credit Risk Prediction Based on Psychometric Data." Computers 12, no. 12 (2023): 248. http://dx.doi.org/10.3390/computers12120248.
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In today’s financial landscape, traditional banking institutions rely extensively on customers’ historical financial data to evaluate their eligibility for loan approvals. While these decision support systems offer predictive accuracy for established customers, they overlook a crucial demographic: individuals without a financial history. To address this gap, our study presents a methodology for a decision support system that is intended to assist in determining credit risk. Rather than solely focusing on past financial records, our methodology assesses customer credibility by generating credit risk scores derived from psychometric test results. Utilizing machine learning algorithms, we model customer credibility through multidimensional metrics such as character traits and attitudes toward money management. Preliminary results from our prototype testing indicate that this innovative approach holds promise for accurate risk assessment.
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Jin, Ruixin, and Huanyu Zhou. "Data analysis with different variables and credit risk assessment." Applied and Computational Engineering 32, no. 1 (2024): 275–84. http://dx.doi.org/10.54254/2755-2721/32/20230863.
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Nowadays, credit payment is a very common way to pay, such as credit cards, loans, many people can use their credit as a guarantee to borrow money from the bank, however some people will default. So we have to predict whether the borrower will pay on time, it is known as credit risk assessment. In this paper, we analyze a data set on credit risk to predict whether individuals will be late on their payments, helping financial firms improve their earnings and reduce their losses. We not only made predictions on the data, but also analyzed the relationship between the variables that affect the overdue probability to find some specific associations. Specifically, we performed ANOVA analysis and found that married people borrowed significantly more than other groups, and the delinquency rate of people with higher education was lower, and the delinquency rate of married people was higher than that of unmarried people. In addition, we conducted a binary logistic regression and found that gender had no significant impact on the prediction results, but an individuals amount of bill statement, amount of previous payment, past repayment situation and Amount of the given credit had an impact on the prediction results. Other variables, such as marital status and education, can also impact the predicted results. Our research puts forward more factors affecting credit risk and also different angles that can be used to analyzes individual credit risk. This has a guiding role for financial firms like banks and other companies in the financial industry, providing more ways to help them analyze the credit risk of borrowers.
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Karentia, Angel, and Derwin Suhartono. "The influence of sentiment analysis in enhancing early warning system model for credit risk mitigation." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 3 (2025): 1829. https://doi.org/10.11591/ijai.v14.i3.pp1829-1838.
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<p><span lang="EN-US">One important source of bank income is interest income from credit activities, another part of which is obtained from fee-based income. Rapid credit growth is directly proportional to an increase in potential credit risk (counterparty default). In addition to comprehensive credit assessment at the initial stage of credit initiation, banks need to monitor the condition of existing debtors. Empirically, difficulties in handling non-performing loans often occur due to delays in detection and preparation of action plans. In this case, losses due to non-performing loans can have implications for the bank's reputation and worsen its financial performance. This research aims to determine the effect of sentiment analysis (external sentiment prediction model [positive, neutral, and negative] with certain keywords) on the level of accuracy of the early warning system (EWS) model in predicting the credit quality of bank debtors in the coming months. This study found that upgrading EWS with sentiment analysis will give better accuracy levels compared to traditional EWS models. In addition, the predictive power of EWS (traditional and upgraded) is inversely proportional to the prediction period, the longer the target prediction time, and the less predictive power of the EWS model.</span></p>
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Noriega, Jomark Pablo, Luis Antonio Rivera, and José Alfredo Herrera. "Machine Learning for Credit Risk Prediction: A Systematic Literature Review." Data 8, no. 11 (2023): 169. http://dx.doi.org/10.3390/data8110169.
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In this systematic review of the literature on using Machine Learning (ML) for credit risk prediction, we raise the need for financial institutions to use Artificial Intelligence (AI) and ML to assess credit risk, analyzing large volumes of information. We posed research questions about algorithms, metrics, results, datasets, variables, and related limitations in predicting credit risk. In addition, we searched renowned databases responding to them and identified 52 relevant studies within the credit industry of microfinance. Challenges and approaches in credit risk prediction using ML models were identified; we had difficulties with the implemented models such as the black box model, the need for explanatory artificial intelligence, the importance of selecting relevant features, addressing multicollinearity, and the problem of the imbalance in the input data. By answering the inquiries, we identified that the Boosted Category is the most researched family of ML models; the most commonly used metrics for evaluation are Area Under Curve (AUC), Accuracy (ACC), Recall, precision measure F1 (F1), and Precision. Research mainly uses public datasets to compare models, and private ones to generate new knowledge when applied to the real world. The most significant limitation identified is the representativeness of reality, and the variables primarily used in the microcredit industry are data related to the Demographic, Operation, and Payment behavior. This study aims to guide developers of credit risk management tools and software towards the existing ability of ML methods, metrics, and techniques used to forecast it, thereby minimizing possible losses due to default and guiding risk appetite.
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Zhang, Zixuan. "Credit Card Default Prediction based on Machine Learning Techniques." BCP Business & Management 44 (April 27, 2023): 779–85. http://dx.doi.org/10.54691/bcpbm.v44i.4954.
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In recent years, with the development of society and economy, credit cards have been popularized due to their low interest rate and easy payment. However, with the advent of the epidemic era, the unemployment rate has increased, making the probability of credit card defaults rising. The prediction of credit card default helps banks and financial institutions balance the risk and economic interests, contributes to the stable and healthy development of the financial industry, and plays an important role in bank credit control. Therefore, this paper addresses the credit card default prediction problem by using Random forest, Decision tree, LightGBM, XGBoost, Logistic regression, and Adaboost models to make predictions and compare the results. The outcomes demonstrate that LightGBM algorithm has the most outstanding prediction score, and its AUC value can reach 0.78 and recall rate reaches 0.95.
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M N., Sowmiya, Jaya Sri S., Deepshika S., and Hanushya Devi G. "Credit Risk Analysis using Explainable Artificial Intelligence." Journal of Soft Computing Paradigm 6, no. 3 (2024): 272–83. http://dx.doi.org/10.36548/jscp.2024.3.004.
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The proposed research focuses on enhancing the interpretability of risk evaluation in credit approvals within the banking sector. This work employs LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) to provide explanations for individual predictions: LIME approximates the model locally with an interpretable model, while SHAP offers insights into the contribution of each feature to the prediction through both global and local explanations. The research integrates gradient boosting algorithms (XGBoost, LightGBM) and Random Forest with these Explainable Artificial Intelligence (XAI) techniques to present a more comprehensible framework. The results demonstrate how interpretability methods such as LIME and SHAP enhance the transparency and trustworthiness of machine learning models, which is crucial for applications in credit risk evaluation.
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Fan, Jiaqi. "Predicting of Credit Default by SVM and Decision Tree Model Based on Credit Card Data." BCP Business & Management 38 (March 2, 2023): 28–33. http://dx.doi.org/10.54691/bcpbm.v38i.3666.
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With the global financial crisis and increased credit risk, default forecasting is playing an increasingly important role in every sector of the economy. Currently, there are linear models and machine learning models for predicting credit defaults. In recent years, big data risk control models are superior to traditional bank models in predicting default rates, and can also conduct business quickly and on a large scale. This paper compares the SVM and the decision tree model in the machine learning model based on the credit card loan data set, and finally evaluates the prediction effect between the two models. According to the study, the decision tree model outperforms the SVM in terms of prediction accuracy. The use of big data to conduct machine learning to predict credit conditions enables financial institutions to serve small, medium and micro enterprises that were difficult to cover by traditional finance on a large scale in the past. It is a world-class innovation in finance.
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Anjani, Suputri Devi D., Bhargavi, S. M. L. Aishwarya, Sai Phanindra P. Karthik, T. Vamsi, and Naga Mainkanta Sai K. Rishi. "Credit card approval prediction using machine learning." i-manager's Journal on Information Technology 12, no. 3 (2023): 39. http://dx.doi.org/10.26634/jit.12.3.20138.
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This paper presents machine learning-based credit card approval prediction and gives an overview of the machine learning models and algorithms that are used to authorize credit cards for users. In order to improve credit card acceptance predictions and increase accuracy and adaptability in financial risk assessment, this study uses XGBoost in machine learning. The study emphasizes the importance of XGBoost in addressing challenges such as handling missing data, avoiding overfitting, and efficiently managing large datasets. Comparisons between the decision tree classifier and XGBoost reveal the latter's advantages, including interpretability, ability to handle complex relationships, and efficiency in processing large datasets. Results from experiments using the XGBoost algorithm demonstrate an accuracy of 90.06%, affirming its efficacy in credit card approval prediction.
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Li, Jingyuan, Caosen Xu, Bing Feng, and Hanyu Zhao. "Credit Risk Prediction Model for Listed Companies Based on CNN-LSTM and Attention Mechanism." Electronics 12, no. 7 (2023): 1643. http://dx.doi.org/10.3390/electronics12071643.
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The financial market has been developing rapidly in recent years, and the issue of credit risk concerning listed companies has become increasingly prominent. Therefore, predicting the credit risk of listed companies is an urgent concern for banks, regulators and investors. The commonly used models are the Z-score, Logit (logistic regression model), the kernel-based virtual machine (KVM) and neural network models. However, the results achieved could be more satisfactory. This paper proposes a credit-risk-prediction model for listed companies based on a CNN-LSTM and an attention mechanism, Our approach is based on the benefits of the long short-term memory network (LSTM) model for long-term time-series prediction combined with a convolutional neural network (CNN) model. Furthermore, the advantages of being integrated into a CNN-LSTM model include reducing the complexity of the data, improving the calculation speed and training speed of the model and solving the possible lack of historical data in the long-term sequence prediction of the LSTM model, resulting in prediction accuracy. To reduce problems, we introduced an attention mechanism to assign weights independently and optimize the model. The results show that our model has distinct advantages compared with other CNNs, LSTMs, CNN-LSTMs and other models. The research on the credit-risk prediction of the listing formula has significant meaning.
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Faria Rahman Annasha, Sabbir Hossen, Monoara Sultana Morzina, Md. Solaiman Kabir, and Md. Showrov Hossen. "Credit Card Client’s Payment Prediction for Next Month Using Machine Learning Algorithms." Journal of Computer Science and Technology Studies 6, no. 3 (2024): 76–85. http://dx.doi.org/10.32996/jcsts.2024.6.3.8.
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With the quick growth of the credit card system, there is a rising number of misconduct rates on credit card loans, which creates a financial risk for commercial banks. Thus, successful resolutions of the risks are significant for the sound advancement of the industry in the long term. Numerous financial banks and organizations become more and more attentive to the issue of credit card default because it brings about a high probability of financial risks. Credit risk plays a significant part in the financial business. One of the main functions of a bank is to issue loans, credit cards, investment mortgages, and other credit. One of the most popular financial services offered by banks in recent years has been the credit card. With its constant rise in risk factors, the banking industry is perhaps the most fragile and volatile in the world. Credit risk remains a crucial element for financial institutions that have experienced losses amounting to hundreds of millions of dollars as a result of their incapacity to retrieve the funds disbursed to clients. In the banking industry, it is now vital to forecast whether a borrower will be able to repay the loan. In this paper, we applied different machine learning classifiers, including Random Forest, K Nearest Neighbor, Logistic Regression, Decision Tree, Decision Tree with AdaBoosting, and Random Forest with AdaBoosting, to build a credit default prediction model. The results show that the AdaBoosting model achieved better accuracy than the other machine learning algorithms. Our proposed technique can support financial organizations in controlling, identifying, and monitoring credit risk, and it can identify credit card clients who pay the loan in the next month.
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Li, Dongmei, and Liping Li. "Research on Efficiency in Credit Risk Prediction Using Logistic-SBM Model." Wireless Communications and Mobile Computing 2022 (June 3, 2022): 1–11. http://dx.doi.org/10.1155/2022/5986295.
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Network lending, an innovative financial lending product, is separated from traditional financial media and implemented on the Internet platform. We study the credit risk prediction of online loan based on risk efficiency analysis. Moreover, we put forward the concept of borrower risk efficiency and apply it to risk prediction. The main task of this study is to establish risk efficiency characteristics on the basis of referring to various risk characteristics and carry out risk prediction after passing the screening of a series of features. The framework is realized by combining logistic regression and slack-based measure (SBM), and feature selection and verification are carried out through machine learning and statistics. Firstly, the efficiency risk characteristics are extracted and the risk efficiency is calculated by MaxDEA. Secondly, the features are screened and verified by Python. Then, the efficiency value obtained by SBM method is used as a new index for the training and testing of logistic model together with the initial related indexes. Moreover, in order to prove the effectiveness of the proposed credit risk prediction control scheme based on risk efficiency, the research compares the prediction before and after adding the risk efficiency feature. The simulation results demonstrated that the logistic-SBM model is more suitable for credit risk prediction than the commonly used logistic method, which realized the efficient prediction of credit risk based on the logistic-SBM model. Finally, some suggestions are put forward to China’s regulatory authorities and the platform itself to control the credit risk of Internet lending industry.
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Kumar, Rahul, Gautam Kumar, Sankalp Srivastava, and Rakhi Sharma. "Comparative Analysis on Prediction of Credit Card Approval." Zenodo 1 (January 10, 2025): 1–11. https://doi.org/10.5281/zenodo.14625864.
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Credit cards play a key role in managing our daily finances.They have made it very simple and convenient for everyone to make payments without using cash. Many people now prefer using credit cards for everyday payments, which has increased the number of applications and created challenges for quick approval. This project aims to automate the process of evaluating credit card applications by using machine learning methods like Random Forest, Gradient Boosting, Support Vector Machines (SVM), Logistic Regression, and Hyperparameter Tuning. The goal is to make credit card approval decisions faster, more accurate, and fair for everyone involved. Credit scoring is important in the financial industry to assess the risk of lending to credit card applicants. Traditional methods of credit scoring have difficulty dealing with large amounts of data and the imbalance between trustworthy and untrustworthy applicants. Credit scoring helps analyze credit risk to decide if an applicant qualifies for a loan. The decision of accepting a card request depends on the private and financial background of the applicant’s. Major factors like income level, credit history, existing debt, and lots of other attributes added for the approval decision. It is important to carefully manage credit risk and make smart approval decisions because they can impact credit management. Approving applications is a crucial step before granting credit. Key problems include slow manual approval, heavy reliance on human effort, and the risk of mistakes during the process. The manual method is not effective and can result in incorrect approvals, leading to financial losses and damaging the bank's reputation, especially if it happens on a large scale. It is very important to carefully manage credit risk, ensure proper approval processes, and minimize human errors. This study examines various machine learning models, such as decision trees, random forests, logistic regression, support vector machines, and artificial neural networks, to predict the likelihood of a credit card application being approved. The models are evaluated based on their accuracy, reliability, and timeliness, using metrics like Precision, Recall, Accuracy, and F1-Score. The results show that the Random Forest Classifier performs the best, with the highest F1-Score.
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Jin, Jing, and Yongqing Zhang. "Innovation in Financial Enterprise Risk Prediction Model." Journal of Organizational and End User Computing 36, no. 1 (2024): 1–26. http://dx.doi.org/10.4018/joeuc.361650.
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In the context of predicting financial risks for enterprises, traditional methods are inadequate in capturing complex multidimensional data features, resulting in suboptimal prediction performance. Although existing deep learning techniques have shown some improvements, they still face challenges in processing time series data and detecting extended dependencies. To address these issues, this paper proposes an integrated deep learning framework utilizing Convolutional Neural Network (CNN), Transformer model, and Wavelet Transform (WT). The proposed model leverages CNN to derive local features from the data, employs the Transformer to capture long-term dependencies, and uses WT for multiscale analysis, thereby enhancing the accuracy and stability of predictions. Experimental results demonstrate that the CNN-Transformer-WT model performs excellently across various datasets, including Kaggle Dataset (Credit Card Fraud Detection Dataset), Bank Marketing Dataset, and Yahoo Finance Historical Stock Market Dataset.
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GBEMINIYI DEBORAH ONIPEDE. "The role of machine learning in enhancing credit scoring models for financial inclusion." World Journal of Advanced Research and Reviews 17, no. 3 (2023): 1095–106. https://doi.org/10.30574/wjarr.2023.17.3.0400.
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The field of credit scoring sees a transformation through machine learning, which yields better prediction results when credit is opened to new populations. Traditional credit scoring systems, which base their predictions on limited financial records, fail to serve millions of unbanked and underbanked people with access to credit. Overlooked data from transaction histories combined with mobile payment records and online behavioral analysis enables ML-based credit assessments to boost lender decision-making precision through improved risk assessment. The research investigates how ML affects credit scoring operations by studying different prediction models that best determine creditworthiness. An evaluation of typical lending practices and ML-based methods demonstrates how automated fair and rapid loan processing emerges as their key benefits. This study utilizes data analytics examinations combined with banking and fintech industry case investigations as research methods. The findings demonstrate better access to credit and reduced bias and risk of default possible through ML implementation. Regular financial institutions and policymakers can use this research to understand how they should utilize Machine Learning techniques to improve lending accessibility while preserving integrity.
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Luo, Na, Jiayi Yang, Yuanfeng Zhu, and Yu Zhang. "The Risk Management of Commercial Banks——Credit-Risk Assessment of Enterprises." International Journal of Economics and Finance 8, no. 9 (2016): 69. http://dx.doi.org/10.5539/ijef.v8n9p69.
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With the diversified developments of the financial market, commercial banks are confronted with various risks, among which the credit risk is the core, and thus the assessment of enterprises’ credit risks is especially important in the credit process of the commercial banks. Based on the relevant researches about commercial banks’ credit risk management, the paper carries out a deep analysis on the factors that may affect the credit risk assessment and then establishes a relatively comprehensive credit risk assessment system. In this paper, we apply our risk assessment model, which is established on the basis of GRNN neural network model, to make an empirical analysis with the selected sample data. And the results suggest that the hit rates of identifying high quality enterprises and low quality enterprises are 92.16 percent and 93.75 percent, respectively, indicating that the model has realized a good prediction.
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Luo, Chuyan. "GBDT-Based Credit Default Prediction." Advances in Economics, Management and Political Sciences 170, no. 1 (2025): 77–86. https://doi.org/10.54254/2754-1169/2025.lh23995.
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In order to reduce the risk of default, machine learning techniques are relied upon to build models to predict defaults. This study focuses on the problem of default prediction in the credit market, based on the Lending Club dataset. And based on feature screening and relevance ranking, the features related to default are obtained and again analysed in detail with knowledge of economics. A variety of machine learning models LogisticRegression, DecisionTreeClassifier, RandomForestClassifier, GradientBoostingClassifier, XGBoost, AdaBoost, Bagging were also used for training and comparison, followed by further optimisation of model performance through data balancing methods such as SMOTE, ADASYN, RandomOverSampler, RandomUnderSampler, SMOTEENN, SMOTETomek. The study discovered that loan interest rate, the number of times the borrower has been queried in the last six months, the credit score, and the monthly installments owed by the borrower had a strong effect on the target variable and were able to make a good prediction of defaults. The GBDT model based on boosting algorithm is trained better. And it is further improved with the balance of RandomOverSampler which has the most significant optimisation results. This study will focus on the above aspects to improve the accuracy of credit default prediction so as to improve credit risk prevention and control.
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Zhang, Qiong, Chang Zhang, and Xin Zhao. "Credit Risk Classification Prediction Based on Optimised Adaboost Algorithm with Long Short-Term Memory Neural Network (LSTM)." Advances in Economics, Management and Political Sciences 87, no. 1 (2024): 152–58. http://dx.doi.org/10.54254/2754-1169/87/20240979.
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In this paper, the Adaboost algorithm is optimised to classify and predict the user's credit risk by combining the long and short term memory neural network LSTM. The dataset was firstly divided, transposed, normalised, tiled and format converted and then the model was trained and tested. During the training process, it is observed that the loss on the training set gradually decreases and the model gradually optimally fits the data and gradually converges to the optimal solution. The confusion matrix shows that the credit risk of 2914 customers is correctly predicted in the training set with an accuracy of 83.3%. The model performs well on the training set and is able to predict the credit risk of the customers accurately. On the test set, 1211 customers' credit risks were correctly predicted with 80.7% accuracy. Compared to the training set, the prediction on the test set has slightly decreased, but it still copes well with the demand of predicting customers' credit risk. This indicates that the model has some generalisation ability and can achieve better performance on unknown data. Overall, the Adaboost algorithm based on LSTM optimisation proposed in this paper shows high accuracy and reliability in the credit risk classification prediction task. By combining neural networks and traditional machine learning methods, it improves the model's ability to accurately predict the credit risk of customers, providing an effective solution in the financial field.
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Li, Yaoxi, Yuxuan Tian, and Jianan Zhuo. "Credit Default Prediction Based on Blending Learning Model." Applied and Computational Engineering 8, no. 1 (2023): 726–33. http://dx.doi.org/10.54254/2755-2721/8/20230112.
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Currently, the preventions of credit default usually will be evaluated by users credit value before loaning from banks. However, for the loan user, who have no existing record of loaning and the situation of low credit value, it cannot precisely recognize the risk of credit default. After a credit default, the bank not only doesnt get the signed compensation and principal in time, but also the debtor needs to bear the expensive corresponding late fees and credit costs. Therefore, reducing credit defaults can decline more burden of debtors and creditors. In this paper, the authors evaluate multiple machine learning models including algorithms belong to machine learning and deep learning, using blending model to boost the prediction effect and accuracy, while proposing an optimization design to further enhance the stability, accuracy and generalization capacity of proposed algorithm, so as to effectively decrease the credit default rate and the risk of bank loss in practice.
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Aslam, Uzair, Hafiz Ilyas Tariq Aziz, Asim Sohail, and Nowshath Kadhar Batcha. "An Empirical Study on Loan Default Prediction Models." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3483–88. http://dx.doi.org/10.1166/jctn.2019.8312.
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Loan lending has been playing a significant role in the financial world throughout the years. Although it is quite profitable and beneficial for both the lenders and the borrowers. It does, however, carry a great risk, which in the domain of loan lending is referred to as Credit risk. Industry experts and Researchers around the world assign individuals with numerical scores known as credit scores to measure the risk and their creditworthiness. Throughout the years machine learning algorithms have been used to calculate and predict credit risk by evaluating an individual’s historical data. This study reviews the present literature on models predicting risk assessment that use machine learning algorithms.
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Agarwal, Nishant, and Meghna Sharma. "Fraud Risk Prediction in Merchant-Bank Relationship using Regression Modeling." Vikalpa: The Journal for Decision Makers 39, no. 3 (2014): 67–76. http://dx.doi.org/10.1177/0256090920140305.
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Banking industry has gone through one of the worst crisis in recent times, and is still recovering from the after-shocks. However, there were a lot of learnings that banks would have taken away from this crisis. One of them is the need for a robust risk management system. The crisis dealt a blow to the banking system, catching them off guard when it came to foreseeing the risk. Banks, in the credit card business, face financial risk in the form of both credit risk and fraud risk. Sharma and Agarwal (2013) proposed a model for predicting the credit risk from the merchants. This paper builds upon their technique to predict the fraud risk posed by the merchants to the banks. Fraud risk is an important aspect of risk management systems, particularly in the credit space. The uncertainty surrounding the receipt of paybacks calls for designing robust risk prediction models. Fraud risk is very different from credit risk because fraud risk does not follow a pattern. It happens suddenly, and may not always have a trend before it happens. This creates a need for separate model for fraud risk prediction. This paper develops a fraud risk prediction model that uses logistic regression technique, deployed using SAS. The setup of the study is the merchant-bank relationship in the credit card industry. The model developed in this paper triggers on a transaction level, and assigns a ‘probability score of default (PF) to each merchant for a possible fraud risk whenever a transaction is done at the merchant. Such a score warns the management in advance of probable future losses on merchant accounts. Banks can rank order merchants based on their PF score, and instead of working on the entire merchant portfolio, they can focus on the relatively riskier set of merchants. The PF model is validated by comparing the actual defaults with those predicted by the model and a good alignment is found between the two. The results show that the model can capture 62 percent frauds in the first decile when the transactions are sorted by the probability of fraud computed by the model.
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Yang, Yubin, Xuejian Chu, Ruiqi Pang, Feng Liu, and Peifang Yang. "Identifying and Predicting the Credit Risk of Small and Medium-Sized Enterprises in Sustainable Supply Chain Finance: Evidence from China." Sustainability 13, no. 10 (2021): 5714. http://dx.doi.org/10.3390/su13105714.
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COVID-19 has created a strong demand for supply chain finance (SCF) for small and medium-sized enterprises (SMEs). However, the rapid development of SCF leads to more complex credit risks. How to effectively discriminate and manage SMEs to reduce credit risk has become one of the most critical issues in SCF. In addition, sustainable SCF (SSCF) has received increasing attention, and credit risk management is important to achieve SSCF. Therefore, it is significant to identify the key factors influencing the credit risk of SMEs and construct a prediction model to promote SSCF. This study uses the lasso-logistic model to identify factors influencing the credit risk of SMEs and to predict the credit risk of SMEs. The empirical results show that (i) the key factors influencing SMEs’ credit risk include six variables—the matching degree of order data, ratio of contract enforcement, number of contract defaults, degree of business concentration, and number of administrative penalties; and (ii) the lasso-logistic model can identify the key factors influencing credit risk and have a better prediction performance. Moreover, transaction credit and reputation supervision significantly influence the credit risk of SMEs.
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Singh, Puneet, Shubha Mishra, Gargi Porwal, Prakhar Saxena, and Rishabh Tripathi. "Credit Risk Model: Research on Credit Risk Categorization model using XGBoost." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43005.
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Machine Learning is a subset of Artificial Intelligence technology that enables systems to learn and make decisions on their own. These systems can make accurate decisions by analyzing datasets and information without the need for explicit programming. This paper mainly introduces the application of machine learning algorithm (XGBoost) in credit risk assessment in the financial industry. Credit risk assessment is a significant challenge for banks to assess credit worthiness among many applicants and plays a very crucial role in the profitability of banks. Our research paper addresses the limitations and complexities in the current models in the market that lack interpretability and transparency. The methodology section introduces the application of the random forest model in financial quantification, including the model principle, feature importance calculation and experimental design. Utilizing the dataset comprising of more than one lakh users from the CIBIL and other banks and then through the exploratory data analysis, feature selection and model construction of the credit risk prediction dataset, the construction and evaluation process of the CRM model is demonstrated. Finally, the performance of the XGBoost model after hyperparameter tuning is evaluated and compared with other models to demonstrate its advantages and applicability in financial quantification. Key Words: XGBoost, Machine Learning, Credit Risk Model, Random Forest
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Wu, Hsu-Che, Ya-Han Hu, and Yen-Hao Huang. "Two-stage credit rating prediction using machine learning techniques." Kybernetes 43, no. 7 (2014): 1098–113. http://dx.doi.org/10.1108/k-10-2013-0218.
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Purpose – Credit ratings have become one of the primary references for financial institutions to assess credit risk. Conventional credit rating approaches mainly concentrated on two-class classification (i.e. good or bad credit), which lacks adequate precision to perform credit risk evaluations in practice. In addition, most of previous researches directly focussed on employing various data mining techniques, but rare studies discussed the influence of data preprocessing before classifier construction. The paper aims to discuss these issues. Design/methodology/approach – This study considers nine-class classification (i.e. nine credit risk level) to credit rating prediction. For the development of more accurate classifiers, the paper adopts two-stage analysis, which integrates multiple data preprocessing and supervised learning techniques. Specifically, the first stage applies feature selection, data clustering, and data resampling methods to preprocess the data, and then the second stage utilizes several classification techniques and classifier ensembles to construct prediction models. Findings – The results show that Bagging-DT with data resampling method achieves excellent accuracy (82.96 percent), indicating that the proposed two-stage prediction model is better than conventional one-stage models. Originality/value – Practical implication of this study can lower credit rating expenses and also allow corporations to gain credit rating information instantly.
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Li, Shaoshu. "Machine Learning in Credit Risk Forecasting —— A Survey on Credit Risk Exposure." Accounting and Finance Research 13, no. 2 (2024): 107. http://dx.doi.org/10.5430/afr.v13n2p107.
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Credit risk is one of the most important elements in risk management area. Traditional regression types of credit risk models are straightforward to implement and model outputs are easy to interpret. However, the model accuracy can always be suboptimal to fit the real credit risk data series. Especially, the model performance even deteriorates under extreme economic scenarios. In contrast, the modern machine learning models can handle different drawbacks of regression types of models. In this paper, we survey the recent literatures on applying the machine learning or deep learning methods in credit risk forecast with special focus on study the superiorities of these techniques. Besides of delivering better prediction accuracies, we uncover other four advantages for machine learning type of default forecast which have been shown in few literatures. We also survey the less studied machine learning or deep learning type of prepayment forecast. By reviewing past literatures from both default and prepayment risk aspects, we can gain comprehensive overview of utilizing machine learning techniques in credit risk forecasting and valuable insights for future risk management research.
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Yan, Gai. "Research on the Application of Alternative Data in Credit Risk Management." Highlights in Business, Economics and Management 40 (September 1, 2024): 1156–60. http://dx.doi.org/10.54097/vn32pp64.
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With the development of financial technology, traditional credit assessment models have gradually shown their limitations. Especially in assessing borrowers with no credit history or weak credit records. The rise of alternative data provides a new dimension for credit risk prediction, including but not limited to social media behavior, online transaction records, geographic location data, etc. This paper explores the current application status, challenges, and future development trends of alternative data in personal credit risk assessment, and explores the application and effects of various forms of alternative data through different classifications. This paper refers to the relevant literature on alternative data and credit risk management and finds that the application of alternative data can not only supplement part of the information reference to enhance the risk management model but also further provide certain credit credentials for groups that cannot obtain credit services with traditional credit data. It has potential contributions to improving credit risk management and promoting the development of inclusive finance.
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Kumar Gupta, Deepak, and Shruti Goyal. "Credit Risk Prediction Using Artificial Neural Network Algorithm." International Journal of Modern Education and Computer Science 10, no. 5 (2018): 9–16. http://dx.doi.org/10.5815/ijmecs.2018.05.02.
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Óskarsdóttir, María, and Cristián Bravo. "Multilayer network analysis for improved credit risk prediction." Omega 105 (December 2021): 102520. http://dx.doi.org/10.1016/j.omega.2021.102520.
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Babeta, Selahadin Nurga, and Million Meshesha. "Telecom airtime credit risk prediction using machine learning." International Journal of Research in Engineering 6, no. 2 (2024): 14–20. http://dx.doi.org/10.33545/26648776.2024.v6.i2a.59.
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Nagalaxmi, Kandula. "AI-Powered Risk Assessment Models: Transforming Credit Scoring and Default Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43282.
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This paper examines the transformative impact of Artificial Intelligence (AI) on credit scoring, focusing on AI-driven risk assessment models for credit evaluation and default prediction. The research delves into how machine learning, real-time data processing, and alternative data sources can combine to provide more accurate predictions of an individual's creditworthiness. The study also addresses critical challenges such as bias, fairness, and the interpretability of AI models, particularly regarding the opaque nature of "black box" AI algorithms. These concerns raise significant questions about transparency and ethical compliance. A primary ethical issue in AI credit scoring is the risk of discrimination, underscoring the importance of robust bias detection and mitigation mechanisms. The paper advocates for transparent and explainable AI models, coupled with strong data governance frameworks. It also emphasizes the need for compliance with legal frameworks such as the General Data Protection Regulation (GDPR) and other relevant laws governing AI applications. In exploring real-world applications, the paper highlights how AI can promote financial inclusion, enhance risk management and decision-making, and enable faster and more equitable credit evaluations. However, it also addresses challenges such as balancing accuracy with fairness, ensuring data privacy, and improving the interpretability of AI systems. Emerging trends in the credit scoring industry, such as Explainable AI (XAI), Natural Language Processing (NLP), and blockchain integration, are also discussed as potential drivers of innovation. The study anticipates that regulatory frameworks will need to evolve to address the challenges posed by AI in credit risk assessment. At the same time, it emphasizes the importance of maintaining ethical standards while fostering innovation in this critical area. Keywords: AI-Driven Credit Scoring, Machine Learning Credit Analysis, Predictive Analytics Creditworthiness, Algorithmic Risk Assessment