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2020 | 14 | nr 2 | 193--208
Tytuł artykułu

Credit Risk Management Using Automatic Machine Learning

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article presents the basic techniques of data mining implemented in typicalcommercial software. They were used to assess the risk of credit card debt repayment. Thearticle assesses the quality of classification models derived from data mining techniques andcompares their results with the traditional approach using a logit model to assess credit risk.It turns out that data mining models provide similar accuracy of classification compared tothe logit model, but they require much less work and facilitate the automation of the processof building scoring models.(original abstract)
Rocznik
Tom
14
Numer
Strony
193--208
Opis fizyczny
Twórcy
  • AGH University of Science and Technology Kraków, Poland
  • AGH University of Science and Technology Kraków, Poland
Bibliografia
  • Altman, E.I., 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy.The Journal of Finance, 23(4), pp. 589-609.
  • Edjlali, R., Ronthal, A., Greenwald, R., Beyer, M., Feinberg, D., 2017.Magic Quadrant for Data Management Solutions for Analytics. Gartner, https://www.gartner.com/home
  • Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F., 2012. A review on ensem-bles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Systems, Man, and Cybernetics Society, 42(4), pp. 3358-3378.
  • Górecki, B.R., 2010.Ekonometria. Podstawy teorii i praktyki. Wydawnictwo Key Text, Warszawa.
  • Han, J., Kamber, M., Pei, J., 2012. Data Mining: Concepts and Techniques. Elsevier, San Francisco.
  • He, H., Garcia, E.A., 2009. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), pp.1263-1284.
  • Holdaway, K., 2014.Harness Oil and Gas Big Data with Analytics: Optimize Explorationand Production with Data Driven Models. John Wiley & Sons, New Jersey.
  • Hosmer, D.W., Lemeshow, S., 2000. Applied Logistic Regression. John Wiley & Sons, New York.
  • Jagiełło, R., 2013. Analiza dyskryminacyjna i regresja logistyczna w procesie oceny zdolności kredytowej przedsiębiorstw. Materiały i Studia NBP, No. 286.
  • Jonc, A., Kraska, M., 2001. Credit-scoring. Nowoczesna metoda oceny zdolności kredytowej. Zarządzanie i Finanse, Warszawa.
  • Kawa, P., Wajda-Lichy, M., Fijorek, K., Denkowska, S., 2020. Do Finance and Trade Foster Economic Growth in the New EU Member States: Granger Panel Bootstrap Causality Approach. Eastern European Economics, 58(6), pp. 458-477.
  • Keramati, A., Yousefi, N., 2011. A Proposed Classification of Data Mining Techniques in Credit Scoring. Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management. Kuala Lumpur, Malaysia, January 22-24.
  • Kufel, T., 2020. Gretl. http://www.kufel.torun.pl/ [20.11.2020].
  • Laitinen, E.K., 1991. Financial ratios and different failure processes. Journal of Business Finance&Accounting, 18, pp. 649-673.
  • Lantz, B., 2013. Machine Learning with R. Packt Publishing, Birmingham.
  • Larose, D., 2006. Odkrywanie wiedzy z danych. Wydawnictwo Naukowe PWN, Warszawa.
  • Li, M.Y.L., Miu, P., 2010. A hybrid bankruptcy prediction model with dynamic loadingson accounting-ratio-based and market-based information: A binary quantile regression approach. Journal of Empirical Finance, 17, pp. 818-833.
  • Linden, A., Krensky, P., Hare, J., Idoine, C., Sicular, S., Vashisth, S., 2017. Magic Quadrant for Data Science Platforms, Gartner, https://www.gartner.com/home.
  • Maalouf, M., Trafalis, T., 2011. Rare events and imbalanced datasets: an overview. International Journal of Data Mining, Modelling and Management, 3(4), pp. 375-388.
  • Machine Learning Repository, 2020, https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171655634

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