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Deep Learning for Repayment Prediction in Leasing Companies

Author

Listed:
  • Marcin Hernes
  • Adrianna Kozierkiewicz
  • Marcin Maleszka
  • Artur Rot
  • Agata Kozina
  • Karolina Matenczuk
  • Jakub Janus
  • Ewelina Wrobel

Abstract

Purpose: This paper aims to improve repayment prediction in leasing companies using a deep learning model. Design/Methodology/Approach: In this work, we prepare some deep learning models and compare them with other solutions based on artificial intelligence like, multiple regression, decision tree, random forest, and bagging classifier. Findings: The developed model enables automatic analysis of large amounts of data that changes quickly and is often unstructured. Additionally, the input vectors consist of specific attributes related to leasing. The results of experiments allow us to conclude that the prediction accuracy of the developed model is higher than reference models used currently in leasing companies. Practical Implications: The developed model has recently been implemented in the Decision Engine system (a system used by leasing companies in Poland) developed by BI Technologies Sp. Z o.o. Company. Originality/Value: Financial institutions automate and simplify credit procedures, eliminating the analyst from the process and replacing him with automatic decision-making processes based on a scoring or similar models. However, to automatically analyze the significance of phenomena occurring in the environment of organizations that affect the assessment of customer's repayments, it is necessary to use artificial intelligence tools.

Suggested Citation

  • Marcin Hernes & Adrianna Kozierkiewicz & Marcin Maleszka & Artur Rot & Agata Kozina & Karolina Matenczuk & Jakub Janus & Ewelina Wrobel, 2021. "Deep Learning for Repayment Prediction in Leasing Companies," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 1134-1148.
    • Handle: RePEc:ers:journl:v:xxiv:y:2021:i:2:p:1134-1148
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    References listed on IDEAS

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    1. Cuiqing Jiang & Zhao Wang & Ruiya Wang & Yong Ding, 2018. "Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending," Annals of Operations Research, Springer, vol. 266(1), pages 511-529, July.
    2. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    3. Gratiela Georgiana Noja & Eleftherios Thalassinos & Mirela Cristea & Irina Maria Grecu, 2021. "The Interplay between Board Characteristics, Financial Performance, and Risk Management Disclosure in the Financial Services Sector: New Empirical Evidence from Europe," JRFM, MDPI, vol. 14(2), pages 1-20, February.
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    More about this item

    Keywords

    Repayment prediction; deep learning; Fintech; leasing companies; multi-layer neural networks.;
    All these keywords.

    JEL classification:

    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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