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Regularization of Autoencoders for Bank Client Profiling Based on Financial Transactions

Author

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  • Andrey Filchenkov

    (Machine Learning Lab, ITMO University, 49 Kronverksky Pr., 197101 St. Petersburg, Russia
    These authors contributed equally to this work.)

  • Natalia Khanzhina

    (Machine Learning Lab, ITMO University, 49 Kronverksky Pr., 197101 St. Petersburg, Russia
    These authors contributed equally to this work.)

  • Arina Tsai

    (Computer Technologies Department, Formerly ITMO University, 49 Kronverksky Pr., 197101 St. Petersburg, Russia)

  • Ivan Smetannikov

    (Machine Learning Lab, ITMO University, 49 Kronverksky Pr., 197101 St. Petersburg, Russia)

Abstract

Predicting if a client is worth giving a loan—credit scoring—is one of the most essential and popular problems in banking. Predictive models for this goal are built on the assumption that there is a dependency between the client’s profile before the loan approval and their future behavior. However, circumstances that cause changes in the client’s behavior may not depend on their will and cannot be predicted by their profile. Such clients may be considered “noisy” as their eventual belonging to the defaulters class results rather from random factors than from some predictable rules. Excluding such clients from the dataset may be helpful in building more accurate predictive models. In this paper, we report on primary results on testing the hypothesis that a client can become a defaulter in two scenarios: intentionally and unintentionally. We verify our hypothesis applying data driven regularized classification using an autoencoder to client profiles. To model an intention as a hidden variable, we propose an especially designed regularizer for the autoencoder. The regularizer aims to obtain a representation of defaulters that includes a cluster of intentional defaulters and unintentional defaulters as outliers. The outliers were detected by our model and excluded from the dataset. This improved the credit scoring model and confirmed our hypothesis.

Suggested Citation

  • Andrey Filchenkov & Natalia Khanzhina & Arina Tsai & Ivan Smetannikov, 2021. "Regularization of Autoencoders for Bank Client Profiling Based on Financial Transactions," Risks, MDPI, vol. 9(3), pages 1-16, March.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:3:p:54-:d:519306
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    References listed on IDEAS

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    1. Desai, Vijay S. & Crook, Jonathan N. & Overstreet, George A., 1996. "A comparison of neural networks and linear scoring models in the credit union environment," European Journal of Operational Research, Elsevier, vol. 95(1), pages 24-37, November.
    2. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    3. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    4. Steenackers, A. & Goovaerts, M. J., 1989. "A credit scoring model for personal loans," Insurance: Mathematics and Economics, Elsevier, vol. 8(1), pages 31-34, March.
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