Subject. This article discusses the method of dynamic modeling, namely the building of a scoring model with time-varying parameters. Objectives. The article aims to develop a scoring model with time-varying parameters for assessing credit risks using the data of a microfinance organization as a case study. Methods. For the study, I used a critical review of the literature on the use of dynamic modeling and a combination of classical logistic regression and time series models Results. Based on the analysis of data from a microfinance organization, the article confirms that changes in independent variables may contain a trend. The obtained confirmation is evidence of the temporal dependence of true parameters when using the developed model, which indicates the high efficiency of the scoring model in the framework of assessing the credit risks of a microfinance organization. Cyclical changes in the tested target and independent variables have been empirically proven, which makes it possible to conclude that the true parameters are time-dependent within the framework of the model used. Conclusions and Relevance. The empirical experiment shows that the developed method can significantly improve the efficiency of scoring models in the implementation of a credit risk analysis system in financial institutions. A scoring model with time-varying parameters can be used in the risk management system of any financial institutions.
Keywords: credit risk, microfinance organization, credit risk management, scoring model, time-varying parameter, time series forecasting
References:
Sorokin A.S. [Comparative analysis of the use of statistical modeling and machine learning for credit risk assessing in microfinance organizations]. Ekonomicheskii vestnik, 2024, vol. 3, no. 2, pp. 51–65. (In Russ.) URL: Link
Sorokin A.S. [Development of algorithms for the application of data mining models for managing credit risks of microfinance organizations]. Plekhanovskii nauchnyi byulleten', 2022, vol. 2, pp. 99–108. (In Russ.) URL: Link
Sorokin A.S. [Credit risk model based on logistic regression with time-varying parameters]. Matematicheskoe i komp'yuternoe modelirovanie v ekonomike, strakhovanii i upravlenii riskami, 2023, vol. 8, pp. 141–146. (In Russ.) URL: Link
Bansal G., Sinha A.P., Zhao H. Tuning Data Mining Methods for Cost-Sensitive Regression: A Study in Loan Charge-Off Forecasting. Journal of Management Information Systems, 2008, vol. 25, no. 3, pp. 315–336. URL: Link
Zhang H., Legro R.S., Zhang J., Zhang L. et al. Decision Trees for Identifying Predictors of Treatment Effectiveness in Clinical Trials and its Application to Ovulation in a Study of Women with Polycystic Ovary Syndrome. Human Reproduction, 2010, vol. 25, iss. 10, pp. 2612–2621. URL: Link
Smith L.D., Lawrence E.C. Forecasting Losses on a Liquidating Long-Term Loan Portfolio. Journal of Banking & Finance, 1995, vol. 19, iss. 6, pp. 959–985. URL: Link00065-B
Greenidge K., Grosvenor T. Forecasting Non-Performing Loans in Barbados. Journal of Business, Finance and Economics in Emerging Economies, 2010, vol. 5, pp. 80–108.
Abdou H.A.H., Pointon J. Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature. Intelligent Systems in Accounting, Finance & Management, 2011, vol. 18, no. 2-3, pp. 59–88. URL: Link
Darroch J.N., Ratcliff D. Generalized Iterative Scaling for Log-Linear Models. The Annals of Mathematical Statistics, 1972, vol. 43, iss. 5, pp. 1470–1480. URL: Link
Durand D. Risk Elements in Consumer Installment Financing. National Bureau of Economic Research, New York, NY, USA, 1941. URL: Link
Makowski P. Credit Scoring Branches Out. The Credit World, 1985, no. 75, pp. 30–37.
Angelini E., Di Tollo G., Roli A. A Neural Network Approach for Credit Risk Evaluation. The Quarterly Review of Economics and Finance, 2008, vol. 48, iss. 4, pp. 733–755. URL: Link
Henley W.E., Hand D.J. A k-Nearest-Neighbour Classifier for Assessing Consumer Credit Risk. Journal of the Royal Statistical Society. Series D (The Statistician), 1996, vol. 45, no. 1, pp. 77–95. URL: Link
Hurley M., Adebayo J. Credit Scoring in the Era of Big Data. Yale Journal of Law and Technology, 2017, vol. 18. URL: Link
Davis R.H., Edelman D.B., Gammerman A.J. Machine-Learning Algorithms for Credit-Card Applications. IMA Journal of Management Mathematics, 1992, vol. 4, iss. 1, pp. 43–51. URL: Link
Frydman H., Altman E.I., Kao D.L. Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress. The Journal of Finance, 1985, vol. 40, iss. 1, pp. 269–291. URL: Link
Zhou S.-R., Zhang D.-Y. A Nearly Neutral Model of Biodiversity. Ecology, 2008, vol. 89, iss. 1, pp. 248–258. URL: Link
Jensen H.L. Using Neural Networks for Credit Scoring. Managerial Finance, 1992, vol. 18, iss. 6, pp. 15–26. URL: Link
West D. Neural Network Credit Scoring Models. Computers & Operations Research, 2000, vol. 27, issues 11–12, pp. 1131–1152. URL: Link00149-5
West D., Dellana S., Qian J. Neural Network Ensemble Strategies for Financial Decision Applications. Computers & Operations Research, 2005, vol. 32, iss. 10, pp. 2543–2559. URL: Link00069-3
Finlay S. Are We Modelling the Right Thing? The Impact of Incorrect Problem Specification in Credit Scoring. Expert Systems with Applications, 2009, vol. 36, iss. 5, pp. 9065–9071. URL: Link
Kamalloo E., Saniee Abadeh M. Credit Risk Prediction Using Fuzzy Immune Learning. Advances in Fuzzy Systems, 2014, vol. 2014, pp. 1–11. URL: Link
Dietterich T.G. Machine-Learning Research. AI Magazine, 1997, vol. 18, no. 4, p. 97. URL: Link
Huang Z., Chen H., Hsu C.-J. et al. Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study. Decision Support Systems, 2004, vol. 37, iss. 4, pp. 543–558. URL: Link00086-1
Zhu Y., Xie C., Wang G.-J., Yan X.-G. Comparison of Individual, Ensemble and Integrated Ensemble Machine Learning Methods to Predict China’s SME Credit Risk in Supply Chain Finance. Neural Computing and Applications, 2017, vol. 28, suppl. 1, pp. 41–50. URL: Link
Opitz D., Maclin R. Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research, 1999, vol. 11, pp. 169–198. URL: Link
Volkova E.S., Gisin V.B., Solov’ev V.I. [Data mining techniques: Modern approaches to application in credit scoring]. Finansy i kredit = Finance and Credit, 2017, vol. 23, iss. 34, pp. 2044–2060. (In Russ.) URL: Link
Shirobokova M.A., Letchikov A.V. [Application of a survival random forest to dynamic evaluation of credit risk]. Matematicheskoe i komp'yuternoe modelirovanie v ekonomike, strakhovanii i upravlenii riskami, 2019, no. 4, pp. 113–118. (In Russ.) URL: Link
Isaev D.V. [Dynamic ensemble learning for assessing creditworthiness]. Innovatsii i investitsii = Innovation and Investment, 2022, vol. 3, pp. 74–79. URL: Link (In Russ.)
Shirobokova M.A. [Model of evaluating the default credit risk throughout the whole life of the loan]. Vestnik Udmurtskogo universiteta. Seriya: Ekonomika i pravo = Bulletin of Udmurt University. Series: Economics and Law, 2018, vol. 28, iss. 2, pp. 228–233. URL: Link (In Russ.)
Grishin A.A., Stroev S.P. [Development of behavioral scoring model using methods of gradient boosting]. Nauchno-tekhnicheskii vestnik Povolzh'ya = Scientific and Technical Volga Region Bulletin, 2018, no. 9, pp. 93–98. (In Russ.) URL: Link
D’yakov O.A. [The specific features of using data mining techniques in scoring solutions for commercial banks]. Nauchnye zapiski molodykh issledovatelei = Scientific Notes of Young Scientists, 2017, no. 3, pp. 5–11. (In Russ.) URL: Link
Carol Alexander, Yang Han, Xiaochun Meng. Static and Dynamic Models for Multivariate Distribution Forecasts: Proper Scoring Rule Tests of Factor-Quantile vs. Multivariate GARCH Models. International Journal of Forecasting, 2022. URL: Link
Jayanti D., Sadik K., Afendi F.M. Multivariate Generalized Autoregressive Score Model (Case Study: Vegetable Oils and Crude Oil Price Data). Journal of Physics: Conference Series, IOP Publishing, 2021, vol. 1863, no. 1, pp. 1–18. URL: Link
Schneider W. Systems of Seemingly Unrelated Regression Equations with Time Varying Coefficients – An Interplay of Kalman Filtering, Scoring, EM- and MINQUE-Method. Computers & Mathematics with Applications, 1992, vol. 24, issues 8–9, pp. 1–16. URL: Link90183-i
Bitto A., Frühwirth-Schnatter S. Achieving Shrinkage in a Time-Varying Parameter Model Framework. Journal of Econometrics, 2019, vol. 210, iss. 1, pp. 75–97. URL: Link
Chan J.C.C., Eisenstat E. Bayesian Model Comparison for Time-Varying Parameter VARs with Stochastic Volatility. Journal of Applied Econometrics, 2018, vol. 33, iss. 4, pp. 509–532. URL: Link
Kalli M., Griffin J.E. Time-Varying Sparsity in Dynamic Regression Models. Journal of Econometrics, 2014, vol. 178, no. 2, pp. 779–793. URL: Link
Orlando G., Pelosi R. Non-Performing Loans for Italian Companies: When Time Matters. An Empirical Research on Estimating Probability to Default and Loss Given Default. International Journal of Financial Studies, 2020, vol. 8, no. 4, p. 68. URL: Link
Aslan A., Poppe L., Posch P. Are Sustainable Companies More Likely to Default? Evidence from the Dynamics between Credit and ESG Ratings. Sustainability, 2021, vol. 13, no. 15, 8568. URL: Link
Orlova E.V. Methodology and Models for Individuals’ Creditworthiness Management Using Digital Footprint Data and Machine Learning Methods. Mathematics, 2021, vol. 9, no. 15, 1820. URL: Link
Moiseev N., Sorokin A., Zvezdina N. et al. Credit Risk Theoretical Model on the Base of DCC-GARCH in Time-Varying Parameters Framework. Mathematics, 2021, vol. 9, no. 19, 2423. URL: Link