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Journal articles on the topic 'Artificial debt'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Yahiya, Ahmad, and Bani Ahmad. "Automated debt recovery systems: Harnessing AI for enhanced performance." Journal of Infrastructure, Policy and Development 8, no. 7 (2024): 4893. http://dx.doi.org/10.24294/jipd.v8i7.4893.

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Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and stre

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2

Sonawane, Tejas, Vatsalya Tiwari, and Dania Shah. "Kuber: An AI-Driven Finance Management Solution Integrating Generative LLMs for Personalized Advisory." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 3752–57. https://doi.org/10.22214/ijraset.2025.69138.

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Abstract: Kuber is a personalized finance management platform that leverages artificial intelligence to deliver tailored financial advice. This paper presents the design and evaluation of Kuber, focusing on its AI-driven advisory engine (using Google’s Gemini model) and finance management methodology. The system collects a user’s financial information – income, expenses, debts, assets, and goals – and generates customized recommendations for budgeting, saving, debt repayment, and investment. Using advanced AI capabilities for data analysis and natural language generation, Kuber bridges the gap

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Kovalenko, Dmytro, Olga Afanasieva, Nani Zabuta, Tetiana Boiko, and Rosen Rosenov Baltov. "Model of Assessing the Overdue Debts in a Commercial Bank Using Neuro-Fuzzy Technologies." Journal of Risk and Financial Management 14, no. 5 (2021): 216. http://dx.doi.org/10.3390/jrfm14050216.

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This article considers the problems of overdue credit debt and the creation of effective methods to manage problem debts in banks. The purpose of this paper is to study the problem of overdue credit debt and create effective methods to manage problem debts in financial institutions. Based on a combination of tools of fuzzy logic theory and artificial neural networks, an economic-mathematical model of collection scoring was built. Kohonen self-organizing maps were used to set the parameters of membership functions in the process of fuzzification of quantitative variables of the built model. Dat

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4

Findler, Nicholas V. "The debt of artificial intelligence to John von Neumann." Artificial Intelligence Review 2, no. 4 (1988): 311–12. http://dx.doi.org/10.1007/bf00138819.

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5

Dmitrieva, O. "Deformation of Fiscal Policy and Debt Management as a Result of the Stabilization Fund Forming." Voprosy Ekonomiki, no. 3 (March 20, 2013): 20–32. http://dx.doi.org/10.32609/0042-8736-2013-3-20-32.

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The article shows the systematic mistake in the form of underestimation of project budget revenues. It is accompanied by the artificial increase in budget deficit which causes excessive borrowings and debt growth while in fact budget surplus takes place. It is proved that state borrowing and saving of assets in the sovereign funds (Reserve Fund and National Wealth Fund) lead to a combination of negative effects related to both deficit and surplus budgets: artificial slowdown of economic growth and increase in expenses for debt service.

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6

Ramesh Kumar, Sowmya. "Navigating Technical Debt in the Evolving Landscape of Machine Learning and Artificial Intelligence." International Journal of Science and Research (IJSR) 10, no. 4 (2021): 1370–71. http://dx.doi.org/10.21275/sr24212235641.

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7

Chuklova, Elena Valerievna, and Aleksandr V. Babich. "Abuse of right, unequal counter-execution, artificial debt in the context of challenging of debtor’s transactions under bankruptcy proceedings." Jus strictum, no. 3 (September 30, 2024): 27–33. http://dx.doi.org/10.18323/3034-2945-2024-3-58-4.

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The ambiguity of interpretation of the rules applied when challenging transactions within the framework of the initiated bankruptcy case and the absence of clear criteria for the grounds for challenging debtor’s transactions determine the reasonability of a deep comprehensive theoretical study of such categories as abuse of rights, unequal counter-execution, and artificial debt. The conducted analysis of judicial practice allowed concluding that invalid debtor’s transactions concluded with abuse of rights in order to create artificial debt or with unequal counter-execution are identified by si

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Pan, Nai Hsin, Ming Li Lee, and Chia Wei Chang. "Construction Financial Crisis Warning Model Using Data Mining." Advanced Materials Research 271-273 (July 2011): 684–88. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.684.

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This paper employs artificial neural network of data mining and decision tree algorithm to build financial crisis warning model. The research results show that, forecasting performance of artificial neural network is better than that of decision tree model, hence, “financial statement average warning model” established through artificial neural network based on the average revenue of the past three years before financial crisis has better forecasting performance than the “annual report forecast model”. Factor analysis is employed to select common factor in 1 year before financial crisis, and t

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9

Johnson, Jeffrey. "Addressing Water Loss With Artificial Intelligence." Opflow 51, no. 1 (2025): 8–9. https://doi.org/10.1002/opfl.2045.

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Many local leaders are just beginning to acknowledge how aging infrastructure, climate change, and population growth are putting additional pressure on their water supplies. The financial impact is substantial. US municipalities and local water utilities are facing budget shortfalls that are either passed through to residents via rate increases or are ignored, creating a vicious cycle of reduced maintenance, higher water loss, and increasing debt. The result is that citizens pay more for less water and, under current conditions, will continue to do so until effective solutions are found.

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10

Fioramanti, Marco. "Predicting sovereign debt crises using artificial neural networks: A comparative approach." Journal of Financial Stability 4, no. 2 (2008): 149–64. http://dx.doi.org/10.1016/j.jfs.2008.01.001.

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11

Jiang, Dongshu, ZhiXing Ni, Yuxiu Chen, Xue Chen, and Chaohong Na. "Influence of Financial Shared Services on the Corporate Debt Cost under Digitalization." Sustainability 15, no. 1 (2022): 428. http://dx.doi.org/10.3390/su15010428.

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Information technologies such as big data and artificial intelligence promote the development of the digital economy, accelerate the digital transformation of enterprises, and continuously facilitate the reform of enterprise production, organization, and management. This study takes Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges as a sample to examine the influence of financial shared services on the corporate debt cost under the digitalization background based on the perspectives of stakeholders such as creditors, shareholders, and society. This study found that

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12

Mehmet, Fatih BURAK. "Analyzing Financial Performances of the Artificial Intelligence Firms by Using the AHP-TOPSIS Method." Pearson Journal of Social Sciences and Humanities 8, no. 25 (2023): 500–519. https://doi.org/10.5281/zenodo.8362319.

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This study was carried out to evaluate the financial performance of artificial intelligence companies, which could not be found to be examined in the literature, by using the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). For this purpose, the weights of the liquidity, financial structure, profitability and operating ratios criteria and the weights of the sub-criteria (current ratio, cash ratio, acid test ratio, long-term debt to capital ratio,  debt to assets ratio, debt to equity ratio, net profit margin, return on equity

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13

Cheek, Eric A., and George F. Keyser. "HEURISTICS FOR DEBT MANAGEMENT: AN EMPIRICAL STUDY." Cybernetics and Systems 19, no. 3 (1988): 249–65. http://dx.doi.org/10.1080/01969728808902167.

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14

Chatterjee, Bikram, Sukanto Bhattacharya, Grantley Taylor, and Brian West. "Political competition and debt: evidence from New Zealand local governments." Accounting Research Journal 32, no. 3 (2019): 344–61. http://dx.doi.org/10.1108/arj-11-2016-0146.

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Purpose This paper aims to investigate whether the amount of local governments’ debt can be predicted by the level of political competition. Design/methodology/approach The study uses the artificial neural network (ANN) to test whether ANN can “learn” from the observed data and make reliable out-of-sample predictions of the target variable value (i.e. a local government’s debt level) for given values of the predictor variables. An ANN is a non-parametric prediction tool, that is, not susceptible to the common limitations of regression-based parametric forecasting models, e.g. multi-collinearit

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15

Yazici, Berna, Memmedaga Memmedli, Atilla Aslanargun, and Senay Asma. "Analysis of international debt problem using artificial neural networks and statistical methods." Neural Computing and Applications 19, no. 8 (2010): 1207–16. http://dx.doi.org/10.1007/s00521-010-0422-4.

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16

Chłopecka, Antonina. "THE USE OF ARTIFICIAL INTELLIGENCE IN ASSESSING A BANK CUSTOMER’S DEBT CAPACITY." Studia Iuridica 101 (2023): 73–87. http://dx.doi.org/10.31338/2544-3135.si.2024-101.5.

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The purpose of this article is to discuss the issue of financial institutions, and especially banks, using artificial intelligence algorithms to assess the debt capacity of their potential borrowers. The author presents the view that the regulations currently in place are insufficient. In particular, there are no provisions in place to sufficiently protect the interests of bank customers. Additionally, the author considers what claims bank customers could have in the event that an algorithm made an incorrect assessment of their creditworthiness.

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17

Amos Abidemi Ogunola and Adriana N Dugbartey. "AI-powered financial tools for student debt management in the U.S.: Enhancing financial literacy and economic stability." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 868–91. http://dx.doi.org/10.30574/wjarr.2024.24.2.3441.

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The increasing burden of student debt in the United States has become a significant economic challenge, affecting millions of borrowers and influencing broader economic trends. With over $1.7 trillion in student loan debt, many borrowers struggle with understanding complex repayment plans, managing interest rates, and avoiding default. This paper explores the potential of AI-powered financial tools in transforming student debt management and enhancing financial literacy. It examines how artificial intelligence (AI) can provide personalized solutions to borrowers, helping them navigate loan rep

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Amos, Abidemi Ogunola, and N. Dugbartey Adriana. "AI-powered financial tools for student debt management in the U.S.: Enhancing financial literacy and economic stability." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 868–91. https://doi.org/10.5281/zenodo.15089473.

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The increasing burden of student debt in the United States has become a significant economic challenge, affecting millions of borrowers and influencing broader economic trends. With over $1.7 trillion in student loan debt, many borrowers struggle with understanding complex repayment plans, managing interest rates, and avoiding default. This paper explores the potential of AI-powered financial tools in transforming student debt management and enhancing financial literacy. It examines how artificial intelligence (AI) can provide personalized solutions to borrowers, helping them navigate loan rep

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19

Mangrum, Benjamin. "Flannery O’Connor, the Phenomenology of Race, and the Institutions of Irony." Twentieth-Century Literature 65, no. 3 (2019): 237–60. http://dx.doi.org/10.1215/0041462x-7852075.

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This essay argues that the representation of race in O’Connor’s short story “The Artificial Nigger” (1955) owes a debt to the continental tradition of phenomenology. Rather than being an abstract philosophical position, this debt signals O’Connor’s self-positioning within the postwar institutions facilitating the production and consumption of literary fiction. In particular, O’Connor’s engagement with the phenomenological tradition and her use of irony are interrelated attempts to negotiate her position within the creative writing institutions of the postwar literary marketplace. O’Connor’s st

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20

Zulu, Julius, and Gardner Mwansa. "Modelling and Forecasting Foreign Debt Using ARIMA Model: The Zambian Case from 2022 to 2035." International Journal of Research and Innovation in Social Science 06, no. 11 (2022): 590–97. http://dx.doi.org/10.47772/ijriss.2022.61127.

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The study sought to model and forecast Zambian Government foreign debt from 2022 to 2035 using Autoregressive Integrated Moving Average Model. The secondary data of time series during the period of 1973 to 2021 on Zambia’s foreign debt are used as the basis of forecasting for the next 15 years by using ARIMA (Autogressive Integrated Moving Average) Model. The ARIMA (1, 1, 2) model was used due to its accuracy, mathematical soundness, and flexibility, thanks to the inclusion of AR and MA terms over a regression analysis. The results showed that ARIMA (1, 1, 2) is an adequate model which best fi

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21

Zhang, Ruihan. "The Impact of Intelligent Auditing on Corporate Debt Default Risk Driven by New-Quality Productivity." Advances in Economics, Management and Political Sciences 126, no. 1 (2024): 40–45. https://doi.org/10.54254/2754-1169/2024.17816.

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The new wave of artificial intelligence (AI) technological revolution is transforming existing industry dynamics and challenging traditional auditing models. Simultaneously, the phenomenon of corporate debt defaults is becoming increasingly severe, making the digital transformation of auditing an urgent necessity. Empowering auditing with new-quality productivity can facilitate a more effective transition to intelligent auditing. This paper, from the novel perspective of new-quality productivity, analyzes the impact of intelligent auditing on corporate debt default risk. Intelligent auditing r

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22

Balakrishnan, Charumathi, and Mangaiyarkarasi Thiagarajan. "CREDIT RISK MODELLING FOR INDIAN DEBT SECURITIES USING MACHINE LEARNING." Buletin Ekonomi Moneter dan Perbankan 24 (March 8, 2021): 107–28. http://dx.doi.org/10.21098/bemp.v24i0.1401.

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We develop a new credit risk model for Indian debt securities rated by major credit rating agencies in India using the ordinal logistic regression (OLR). The robustness of the model is tested by comparing it with classical models available for ratings prediction. We improved the model’s accuracy by using machine learning techniques, such as the artificial neural networks (ANN), support vector machines (SVM) and random forest (RF). We found that the accuracy of our model has improved from 68% using OLR to 82% when using ANN and above 90% when using SVM and RF.

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23

Hope Ehiaghe Omokhoa, Chinekwu Somtochukwu Odionu, Chima Azubuike, and Aumbur Kwaghter Sule. "AI-Powered Fintech innovations for credit scoring, debt recovery, and financial access in Microfinance and SMEs." Gulf Journal of Advance Business Research 2, no. 6 (2024): 411–22. https://doi.org/10.51594/gjabr.v2i6.55.

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The integration of artificial intelligence in fintech is revolutionizing financial services, particularly for microfinance institutions and small and medium-sized enterprises (SMEs). This paper explores the transformative impact of AI-powered innovations in credit scoring, debt recovery, and financial access. AI-driven credit scoring leverages alternative data and advanced machine learning techniques to enhance accuracy, inclusivity, and efficiency, addressing the limitations of traditional methods. In debt recovery, AI optimizes collection processes through predictive analytics, workflow auto

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24

Swathi Turai, Praneetha Potharaju, Rajasri Aishwarya Bepeta, Mohammed Adil, and Mani Charan Vangala. "Artificial Intelligence based code refactoring." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 639–46. https://doi.org/10.30574/wjaets.2025.15.2.0594.

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One of the most difficult aspects of software development is maintaining and updating legacy code, which frequently requires a significant investment of time and energy to make the code more manageable, efficient, and readable. Using sophisticated AI, such as machine learning and large language models, the AI-Powered Codebase Refactorer is a clever tool made to make this process easier. It converts jumbled or out-of-date code—such as old Python or Java projects—into more organized, contemporary, and well-documented forms. The tool makes the code much easier to understand by adding useful comme

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25

Petrov, Dancho. "The Impact of the Covid-19 Crisis on Global Syndicated Loans, M&A and Distressed Debt." Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series 12, no. 1 (2023): 50–58. http://dx.doi.org/10.56065/ijusv-ess/2023.12.1.50.

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The Covid-19 pandemic caused a global economic shock and left its "footprint" on the development of international financial markets. The crisis caused by the pandemic and its impact on international financing is not entirely clear-cut. Along with the negatives, the covid crisis revealed hitherto unsuspected opportunities for a boom in the development of some areas such as high technologies, artificial intelligence and telecommunications. The focus of the paper is on trend analysis of some of the most significant forms of global international financing - syndicated loans, M&A financing, dis

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26

Dmitrieva, O. G. "Excessive Borrowing in Debt Policy of the Russian Federation." Finance: Theory and Practice 28, no. 5 (2024): 6–20. http://dx.doi.org/10.26794/2587-5671-2024-28-5-6-20.

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The subject of the study is the debt policy of the Russian Federation in 2001–2023, the replenishment and withdrawal of the National Wealth Fund (NWF), the mechanism of the “fiscal rule”. The purpose of the study is to discover the real effects of implementation of the fiscal mechanism known as “fiscal rule” as well as evaluation of the real efficiency and potential of the application of the mechanism of NWF as a countercyclical regulation tool. The research used the method of system analysis of the federal budget data, budget balances in the framework of debt policy. The study also used the m

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S. Savina. "ARTIFICIAL INTELLIGENCE IN ANALYZING THE CAPITAL STRUCTURE EFFECT ON FINANCIAL STABILITY." BULLETIN 1, no. 383 (2020): 277–87. http://dx.doi.org/10.32014/10.32014/2020.2518-1467.33.

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Financial stability is an important measure used by stakeholders to assess the financial situation of an entity concerned. Economic worries caused by internal business issues, global processes, and international economic (regional) integration may increase the entity’s exposure to external factors. Financial stability considers the entity’s dependence on creditors and investors, i.e. the debt-to-equity ratio. Significant liabilities that are not fully covered by the entity’s own liquid funds create preconditions for bankruptcy should any large creditor demand settlement of any debts owed to it

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28

Zhang, Congyingzi, and Yan Wu. "A Flowchart for Rapid Technical Debt Management Decision Making." Journal of Software 11, no. 2 (2016): 212–19. http://dx.doi.org/10.17706/jsw.11.2.212-219.

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29

TÜRK, Armağan, and Cemil ERARSLAN. "Causes and Results of the Greek Debt Crisis: An Analysis with Artificial Neural Networks." Gaziantep University Journal of Social Sciences 15, no. 24217 (2016): 281–302. http://dx.doi.org/10.21547/jss.256726.

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30

Joubert, Y. "An artificial distinction? The interpretation of “debt” in the Prescription Act 68 of 1969." Tydskrif vir die Suid-Afrikaanse Reg 2025, no. 3 (2025): 632–41. https://doi.org/10.47348/tsar/2025/i3a13.

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Die litigasie in Rademeyer v Ferreira hou verband met ’n kontrak vir die verkoop van onroerende eiendom tussen Ferreira en Rademeyer. Om te bepaal of Ferreira se eis vir skadevergoeding verjaar het, moes die konstitusionele hof oorweeg of kontrakbreuk en spesifieke nakoming gebaseer is op dieselfde skuldoorsaak. Volgens die meerderheidsbeslissing is dit nie die geval nie, met die gevolg dat Ferreira se skadevergoedingseis suksesvol verdedig is deur Rademeyer se spesiale pleit van verjaring. Alhoewel Ferreira se aansoek vir spesifieke nakoming regterlike stuiting van verjaring ingevolge artikel

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31

Sunayama, Wataru, and Katsutoshi Yada. "Analysis Framework for Persuading Process and an Application to Debt-Collecting Conversation Logs." Transactions of the Japanese Society for Artificial Intelligence 22 (2007): 239–47. http://dx.doi.org/10.1527/tjsai.22.239.

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32

Researcher. "LEVERAGING AI TO TACKLE FINANCIAL DISTRESS: A COMPREHENSIVE APPROACH." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 358–69. https://doi.org/10.5281/zenodo.13255200.

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Artificial Intelligence (AI) has emerged as a powerful tool in predicting and mitigating financial distress for individuals and businesses. This article explores various AI techniques employed in financial management, including early warning systems, liquidity management, debt restructuring, personalized financial planning, and continuous monitoring strategies. AI-powered models have demonstrated remarkable accuracy in predicting financial distress, with some achieving up to 86.4% accuracy in corporate financial distress prediction. These systems utilize advanced algorithms, such as Long Short

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33

Dulger, A. V. "Artificial division of a single administrative violation of license requirements in the sphere of management of apartment buildings." Vektor nauki Tol’attinskogo gosudarstvennogo universiteta. Seria Uridicheskie nauki, no. 2 (2023): 21–28. http://dx.doi.org/10.18323/2220-7457-2023-2-21-28.

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The paper deals with the problem of qualification and artificial division of a single administrative offense related to the presence of a debt to the public utilities suppliers by a managing company amounting double average monthly accrual when implementing the entrepreneurial activities on the management of apartment buildings with a gross violation of license requirements. The paper reveals the elements of administrative offense provided by part 3 of Art. 14.1.3 of the Code of the Russian Federation on Administrative Offenses. The author pays the particular attention to the analysis of small

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34

Liu, Wing Ki, and Mike K. P. So. "A GARCH Model with Artificial Neural Networks." Information 11, no. 10 (2020): 489. http://dx.doi.org/10.3390/info11100489.

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In this paper, we incorporate a GARCH model into an artificial neural network (ANN) for financial volatility modeling and estimate the parameters in Tensorflow. Our goal was to better predict stock volatility. We evaluate the performance of the models using the mean absolute errors of powers of the out-of-sample returns between 2 March 2018 and 28 February 2020. Our results show that our modeling procedure with an ANN can outperform the standard GARCH(1,1) model with standardized Student’s t distribution. Our variable importance analysis shows that Net Debt/EBITA is among the six most importan

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35

Sandeep Konakanchi. "Artificial Intelligence in Code Optimization and Refactoring." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1197–211. https://doi.org/10.32628/cseit25112463.

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AI has become useful in software development to help improve on code optimization/ refactoring exercises thus boosting on productivity, performance and sustainable maintainability. AI tools including CodeT5, Codex, Intel’s Neural Compressor, and Refactoring Miner help the developers to analyze the code, minimize it and advance refactoring engagements. This paper investigates the deployment of AI in code optimization and their performances in optimizing common codes used across industries on real-world case, highlighting the impacts of AI in enhancing system performance, code read abilities, an

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Hair, Joseph F., Luiz Paulo Fávero, Wilson Tarantin Junior, and Alexandre Duarte. "Deterministic and Stochastic Machine Learning Classification Models: A Comparative Study Applied to Companies’ Capital Structures." Mathematics 13, no. 3 (2025): 411. https://doi.org/10.3390/math13030411.

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Corporate financing decisions, particularly the choice between equity and debt, significantly impact a company’s financial health and value. This study predicts binary corporate debt levels (high or low) using supervised machine learning (ML) models and firms’ characteristics as predictive variables. Key features include companies’ size, tangibility, profitability, liquidity, growth opportunities, risk, and industry. Deterministic models, represented by logistic regression and multilevel logistic regression, and stochastic approaches that incorporate a certain degree of randomness or probabili

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Brilliantov, D., та L. Mandrikova. "РОЗУМНЕ ПЛАНУВАННЯ БЮДЖЕТУ ІЗ ВИКОРИСТАННЯМ ШІ: ІННОВАЦІЙНІ ПІДХОДИ В УПРАВЛІННІ ОСОБИСТИМИ ФІНАНСАМИ". Open Information and Computer Integrated Technologies, № 103 (1 травня 2025): 100–111. https://doi.org/10.32620/oikit.2025.103.08.

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As of 2023, approximately 40-45% of people worldwide track their income and expenses, with this number steadily increasing due to technological progress and the growing need for stricter financial control amid global inflation and economic instability. The aim of this research is to explore the application of artificial intelligence in a web-based personal finance management application. Artificial intelligence analyzes users' financial health, tracks income and expense trends over various periods and categories, and provides personalized recommendations for optimizing budgets, building saving

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Pao, Hsiao-Tien, and Yao-Yu Chih. "Comparison of TSCS regression and neural network models for panel data forecasting: debt policy." Neural Computing and Applications 15, no. 2 (2005): 117–23. http://dx.doi.org/10.1007/s00521-005-0014-x.

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Abderrazak, Belabes. "Islamic Finance, Artificial Intelligence, and the Debt Embedded in the Ex Nihilo Monetary Creation System." INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS 08, no. 02 (2025): 485–91. https://doi.org/10.5281/zenodo.14835850.

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The aim of this study is to shed light on a relationship that has not been explored until now. This is the relationship between the world of Islamic finance that of AI, and the debt embedded in the ex nihilo monetary creation system induced by bank loans with ribā. As a result, by making excessive use of AI under the pretext of the need to adopt disruptive innovations that generate exponential growth, Islamic finance feeds this system. This raises a paradox: Islamic finance, which is reputed to be ribā-free, uses AI, which feeds on ribā, to grow exponentially in a context of chip war between t

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40

Pao, Hsiao-Tien, and Yao-Yu Chih. "Comparison of Linear and Nonlinear Models for Panel Data Forecasting: Debt Policy in Taiwan." Review of Pacific Basin Financial Markets and Policies 08, no. 03 (2005): 525–41. http://dx.doi.org/10.1142/s0219091505000488.

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This paper discusses the time-series cross-sectional (TSCS) regression and the prediction ability of the artificial neural network (ANN) by examining the panel data of debt ratios of the high tech industry in Taiwan. We build models with these two methods and eight determinants of debt ratio and compare the forecast performances of five models, two ANN nonlinear models and three traditional TSCS linear models. The results show that the sign of each determinant in linear models is the same as that in ANN models. In addition, the insignificant determinants in linear models have low relative sens

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BARR, JOSEPH R., and W. KURT DOBSON. "FINDING PROBLEM SPECIFIC SHANNON INFORMATION IN HIGH DIMENSIONAL INPUT SPACE FOR ARTIFICIAL NEURAL NETWORKS." International Journal of Semantic Computing 07, no. 02 (2013): 147–55. http://dx.doi.org/10.1142/s1793351x13400060.

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Artificial neural networks, due to their ability to find the underlying model even in complex highly nonlinear and highly coupled problems, have found significant use as prediction engines in many domains. However, in problems where the input space is of high dimensionality, there is the unsolved problem of reducing dimensionality in some optimal way such that Shannon information important to the prediction is preserved. The important Shannon information may be a subset of total information with an unknown partition, unknown coupling and linear or nonlinear in nature. Solving this problem is a

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Ourdani, Nabil, Mohamed Chrayah, and Noura Aknin. "Towards a new approach to maximize tax collection using machine learning algorithms." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 737. http://dx.doi.org/10.11591/ijai.v13.i1.pp737-746.

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<p>Efficient tax debt collection is a challenge for Moroccan local tax authorities. This article explores the potential of machine learning techniques and novel strategies to enhance efficiency in this process. We present a practical use case demonstrating the application of machine learning for taxpayer segmentation, improving accuracy in identifying high-risk debtors. Using a comprehensive dataset of tax payment behavior, we showcase the effectiveness of machine learning algorithms in segmenting taxpayers based on their likelihood of non-compliance or debt accumulation. We also investi

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Gil-Cordero, Eloy, and Juan-Pedro Cabrera-Sánchez. "Private Label and Macroeconomic Indexes: An Artificial Neural Networks Application." Applied Sciences 10, no. 17 (2020): 6043. http://dx.doi.org/10.3390/app10176043.

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Retail companies operate with a private label assortment of 40–45% of their total assortment, which has led to a significant growth of private labels in recent years in their countries of origin; however, when retail companies decide to internationalize, it is important to know which macroeconomic indicators are more relevant when entering a new country or continent. For that reason, in this study we have as a main objective to establish which are the most transcendental macroeconomic variables for the volume and value of the private label. For this purpose, we have analyzed a total of 1400 sa

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Qu, Yubin, Tie Bao, Xiang Chen, et al. "Do we need to pay technical debt in blockchain software systems?" Connection Science 34, no. 1 (2022): 2026–47. http://dx.doi.org/10.1080/09540091.2022.2067125.

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Deeb, Samir, Mrwan BenIdris, Hany Ammar, and Dale Dzielski. "Refactoring Cost Estimation for Architectural Technical Debt." International Journal of Software Engineering and Knowledge Engineering 31, no. 02 (2021): 269–88. http://dx.doi.org/10.1142/s021819402150008x.

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Paying-off the Architectural Technical Debt by refactoring the flawed code is important to control the debt and to keep it as low as possible. Project Managers tend to delay paying off this debt because they face difficulties in comparing the cost of the refactoring against the benefits gained. These managers need to estimate the cost and the efforts required to conduct these refactoring activities as well as to decide which flaws have higher priority to be refactored. Our research is based on a dataset used by other researchers that study the technical debt. It includes more than 18,000 refac

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Lelis, Christhoffer P., and Neil Patrick S. Muega. "A Neural Network Analysis of Accounting Variables and Stock Price: The Case of Real Estate Companies in the Philippines." American Journal of Economics and Business Innovation 3, no. 3 (2024): 95–101. http://dx.doi.org/10.54536/ajebi.v3i3.3280.

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This research examines the importance of various accounting variables in predicting stock price changes for the top three real estate companies on the 2024 Philippine Stock Exchange. The study uses artificial neural network (ANN) analysis to focus on the current ratio, return on assets, return on equity, net profit margin, operating profit margin, and debt-to-equity ratio. Quarterly financial reports and stock prices from 2018 to 2023 were analyzed with a feedforward back-propagation ANN model in SPSS 25. Results show that the net profit margin is the most significant predictor of stock prices

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Sivamayilvelan, Keerthana, Elakkiya Rajasekar, Subramaniyaswamy Vairavasundaram, Santhi Balachandran, and Vishnu Suresh. "Building explainable artificial intelligence for reinforcement learning based debt collection recommender system using large language models." Engineering Applications of Artificial Intelligence 159 (November 2025): 111622. https://doi.org/10.1016/j.engappai.2025.111622.

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Tellez Gaytan, Jesus Cuauhtemoc, Karamath Ateeq, Aqila Rafiuddin, et al. "AI-Based Prediction of Capital Structure: Performance Comparison of ANN SVM and LR Models." Computational Intelligence and Neuroscience 2022 (September 19, 2022): 1–13. http://dx.doi.org/10.1155/2022/8334927.

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Capital structure is an integral part of the corporate finance that sources the funds to finance growth and operations. Managers always have to maintain value of the firm to be higher than the cost of capital in order to maximize the shareholders wealth. Empirical studies have used sources of finance like debt and equity as variables of capital structure. A choice between debt and equity finance analyzes the firm’s ability to perform under the financially constrained environment to attain the sustainable growth. Therefore, it gives rise to a dire need to estimate the cost of capital precisely.

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Tenny Enoch Devadas. "A Scalable AI-Driven Ecosystem for National Debt Intervention: Integrating Predictive Analytics and Behavioral Segmentation for Financial Wellness." Journal of Computer Science and Technology Studies 7, no. 6 (2025): 888–96. https://doi.org/10.32996/jcsts.2025.7.105.

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Personal debt in the United States has reached critical levels, creating widespread economic strain and limiting opportunities for financial mobility. This article presents a comprehensive AI-driven ecosystem designed to proactively identify financially distressed individuals and connect them with personalized debt relief resources through advanced machine learning and real-time data engineering. The framework integrates multiple AI models, including risk classification algorithms, propensity scoring systems, natural language processing for intent detection, and recommender systems for tailore

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Abdou, Hussein A., Shatarupa Mitra, John Fry, and Ahmed A. Elamer. "Would two-stage scoring models alleviate bank exposure to bad debt?" Expert Systems with Applications 128 (August 2019): 1–13. http://dx.doi.org/10.1016/j.eswa.2019.03.028.

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