Importance This article examines the current state of research in machine learning and data mining, which computational methods get combined with conventional lending models such as scoring, for instance. Objectives The article aims to classify the modern methods of credit scoring and describe models for comparing the effectiveness of the various methods of credit scoring. Methods To perform the tasks, we have studied relevant scientific publications on the article subject presented in Google Scholar. Results The article presents a classification of modern data mining techniques used in credit scoring. Conclusions and Relevance Credit scoring models using machine learning procedures and hybrid models using combined methods can provide the required level of efficiency in the modern environment.
Ключевые слова: loan scoring, credit score, machine learning, data mining
Durand D. Risk Elements in Consumer Installment Financing. New York, National Bureau of Economic Research Books, 1941, 163 p.
Hand D.J., Henley W.E. Statistical Classification Methods in Consumer Credit Scoring: A Review. Journal of the Royal Statistical Society: Series A (Statistics in Society), 1997, vol. 160, iss. 3, pp. 523–541. URL: Link
García V., Marqués A.I., Sánchez J.S. An Insight into the Experimental Design for Credit Risk and Corporate Bankruptcy Prediction Systems. Journal of Intelligent Information Systems, 2015, vol. 44, iss. 1, pp. 159–189. URL: Link
Lessmann S., Seow H.-V., Baesens B., Thomas L.C. Benchmarking State-of-the-Art Classification Algorithms for Credit Scoring: An Update of Research. European Journal of Operational Research, 2015, vol. 247, iss. 1, pp. 124–136. URL: Link
Hand D.J., Kelly M.G. Superscorecards. IMA Journal of Management Mathematics, 2002, vol. 13, iss. 4, pp. 273–281.
Yap B.W., Ong S.H., Husain N.H.M. Using Data Mining to Improve Assessment of Credit Worthiness via Credit Scoring Models. Expert Systems with Applications, 2011, vol. 38, iss. 10, pp. 13274–13283. URL: Link
Pavlidis N.G., Tasoulis D.K., Adams N.M., Hand D.J. Adaptive Consumer Credit Classification. Journal of the Operational Research Society, 2012, vol. 63, iss. 12, pp. 1645–1654. URL: Link
Khemais Z., Nesrine D., Mohamed M. Credit Scoring and Default Risk Prediction: A Comparative Study between Discriminant Analysis & Logistic Regression. International Journal of Economics and Finance, 2016, vol. 8, iss. 4, pp. 39–53. URL: Link
Louzada F., Anacleto-Junior O., Candolo C., Mazucheli J. Poly-bagging Predictors for Classification Modelling for Credit Scoring. Expert Systems with Applications, 2011, vol. 38, iss. 10, pp. 12717–12720. URL: Link
Li Z., Tianb Y., Li K. et al. Reject Inference in Credit Scoring Using Semi-supervised Support Vector Machines. Expert Systems with Applications, 2017, vol. 74, pp. 105–114. URL: Link
Fisher R.A. The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 1936, vol. 7, iss. 2, pp. 179–188. URL: Link
Eisenbeis R.A. Problems in Applying Discriminant Analysis in Credit Scoring Models. Journal of Banking & Finance, 1978, vol. 2, iss. 3, pp. 205–219. URL: Link90012-2
Mylonakis J., Diacogiannis G. Evaluating the Likelihood of Using Linear Discriminant Analysis as a Commercial Bank Card Owners Credit Scoring Model. International Business Research, 2010, vol. 3, no. 2, pp. 9–20. URL: Link
Akkoç S. An Empirical Comparison of Conventional Techniques, Neural Networks and the Three Stage Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Model for Credit Scoring Analysis: The Case of Turkish Credit Card Data. European Journal of Operational Research, 2012, vol. 222, iss. 1, pp. 168–178. URL: Link
Falangis K., Glen J.J. Heuristics for Feature Selection in Mathematical Programming Discriminant Analysis Models. Journal of the Operational Research Society, 2010, vol. 61, no. 5, pp. 804–812. URL: Link
Breiman L., Friedman J., Stone C.J., Olshen R.A. Classification and Regression Trees. Monterey, CA, Wadsworth & Brooks/Cole Advanced Books & Software, 1984, 368 p.
Loh W.-Y. Fifty Years of Classification and Regression Trees. International Statistical Review, 2014, vol. 82, iss. 3, pp. 329–348. URL: Link
Finlay S. Multiple Classifier Architectures and Their Application to Credit Risk Assessment. European Journal of Operational Research, 2011, vol. 210, iss. 2, pp. 368–378. URL: Link
Zhang D., Zhou X., Leung S.C.H., Zheng J. Vertical Bagging Decision Trees Model for Credit Scoring. Expert Systems with Applications, 2010, vol. 37, iss. 12, pp. 7838–7843. URL: Link
Hu Q., Che X., Zhang L. et al. Rank Entropy-Based Decision Trees for Monotonic Classification. IEEE Transactions on Knowledge and Data Engineering, 2012, vol. 24, iss. 11, pp. 2052–2064. URL: Link
Hayashi Y., Tanaka Y., Takagi T. et al. Recursive-Rule Extraction Algorithm with J48graft and Applications to Generating Credit Scores. Journal of Artificial Intelligence and Soft Computing Research, 2016, vol. 6, iss. 1, pp. 35–44. URL: Link
Vapnik V.N. Statistical Learning Theory. New York, John Wiley, 1998, 768 p.
Bellotti T., Crook J. Support Vector Machines for Credit Scoring and Discovery of Significant Features. Expert Systems with Applications, 2009, vol. 36, iss. 2-2, pp. 3302–3308. URL: Link
Chen W., Ma C., Ma L. Mining the Customer Credit Using Hybrid Support Vector Machine Technique. Expert Systems with Applications, 2009, vol. 36, iss. 4, pp. 7611–7616. URL: Link
Ling Y., Cao Q., Zhang H. Credit Scoring Using Multi-Kernel Support Vector Machine and Chaos Particle Swarm Optimization. International Journal of Computational Intelligence and Applications, 2012, vol. 11, iss. 3, pp. 12500198:1–12500198:13.
Friedman N., Geiger D., Goldszmidt M. Bayesian Network Classifiers. Machine Learning, 1997, vol. 29, iss. 2-3, pp. 131–163. URL: Link
Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988, 552 p.
Giudici P. Bayesian Data Mining, with Application to Benchmarking and Credit Scoring. Applied Stochastic Models in Business and Industry, 2001, vol. 17, iss. 1, pp. 69–81. URL: Link
Gemela J. Financial Analysis Using Bayesian Networks. Applied Stochastic Models in Business and Industry, 2001, vol. 17, iss. 1, pp. 57–67. URL: Link
Antonakis A.C., Sfakianakis M.E. Naïve Bayes as a Means of Constructing Application Scorecards. In: L. Moutinho and K.-H. Huarng (eds), Advances in Doctoral Research in Management. Singapore, World Scientific Publishing Co. Pte. Ltd, 2008, vol. 2, pp. 47–62.
Antonakis A.C., Sfakianakis M.E. Assessing Naïve Bayes as a Method for Screening Credit Applicants. Journal of Applied Statistics, 2009, vol. 36, iss. 5-6, pp. 537–545. URL: Link
Wu W.-W. Improving Classification Accuracy and Causal Knowledge for Better Credit Decisions. International Journal of Neural Systems, 2011, vol. 21, iss. 4, pp. 297–309. URL: Link
Zhu H., Beling P.A., Overstreet G.A. A Bayesian Framework for the Combination of Classifier Outputs. Journal of the Operational Research Society, 2002, vol. 53, iss. 7, pp. 719–727. URL: Link
West D. Neural Network Credit Scoring Models. Computers & Operations Research, 2000, vol. 27, iss. 11-12, pp. 1131–1152. URL: Link00149-5
Ong C.-S., Huang J.-J., Tzeng G.-H. Building Credit Scoring Models Using Genetic Programming. Expert Systems with Applications, 2005, vol. 29, iss. 1, pp. 41–47. URL: Link
Breiman L. Bagging Predictors. Machine Learning, 1996, vol. 24, iss. 2, pp. 123–140. URL: Link
Vukovic S., Delibašić B., Uzelac A., Suknovic M. A Case-Based Reasoning Model That Uses Preference Theory Functions for Credit Scoring. Expert Systems with Applications, 2012, vol. 39, iss. 9, pp. 8389–8395. URL: Link
Marqués A.I., García V., Sánchez J.S. Two-Level Classifier Ensembles for Credit Risk Assessment. Expert Systems with Applications, 2012, vol. 39, iss. 12, pp. 10916–10922. URL: Link
Hoffmann F., Baesens B., Mues C. et al. Inferring Descriptive and Approximate Fuzzy Rules for Credit Scoring Using Evolutionary Algorithms. European Journal of Operational Research, 2007, vol. 177, iss. 1, pp. 540–555. URL: Link
Ignatius J., Hatami-Marbini A., Rahman A. et al. A Fuzzy Decision Support System for Credit Scoring. Neural Computing and Applications, 2016, vol. 27, no. 1, pp. 1–17. URL: Link
Lahsasna A., Ainon R.N., Wah T.Y. Credit Risk Evaluation Decision Modeling Through Optimized Fuzzy Classifier. Proc. International Symposium on Information Technology, 2008. IEEE, 2008, vol. 1, pp. 1–8.
Kaur A. et al. Fuzzy Rule-based Expert System for Evaluating Defaulter Risk in Banking Sector. Indian Journal of Science and Technology, 2016, vol. 9, iss. 28, pp. 1–6. URL: Link
Malhotra R., Malhotra D.K. Differentiating Between Good Credits and Bad Credits Using Neuro-Fuzzy Systems. European Journal of Operational Research, 2002, vol. 136, iss. 1, pp. 190–211. URL: Link00052-2
Clemen R.T., Murphy A.H., Winkler R.L. Screening Probability Forecasts: Contrasts Between Choosing and Combining. International Journal of Forecasting, 1995, vol. 11, iss. 1, pp. 133–145. URL: Link02007-C
DeGroot M.H., Fienberg S.E. The Comparison and Evaluation of Forecasters. Journal of the Royal Statistical Society. Series D (The Statistician), 1983, vol. 32, no. 1/2, pp. 12–22. Stable URL: Link
DeGroot M.H., Eriksson E.A. Probability Forecasting, Stochastic Dominance, and the Lorenz Curve. J.M. Bernardo, M.H. DeGroot, D.V. Lindley and A.F.M. Smith (eds). Amsterdam, North-Holland, Bayesian Statistics, 1985, vol. 2, pp. 99–118.