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A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection

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  • Wei Li
  • Florentina Paraschiv
  • Georgios Sermpinis

Abstract

The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a natural economic interpretation of results. Indeed, the empirical results emphasize the interpretability of the CBR system in predicting financial risk, which is essential for both financial companies and their customers. In addition, our results show that the proposed automatic design CBR system has a good prediction performance compared to other artificial intelligence methods, overcoming the main drawback of a standard CBR system of highly depending on prior domain knowledge about the corresponding field.

Suggested Citation

  • Wei Li & Florentina Paraschiv & Georgios Sermpinis, 2021. "A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection," Papers 2107.08808, arXiv.org.
    • Handle: RePEc:arx:papers:2107.08808
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    References listed on IDEAS

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    1. H G Li & D J Hand, 2002. "Direct versus indirect credit scoring classifications," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(6), pages 647-654, June.
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    5. Trivedi, Shrawan Kumar, 2020. "A study on credit scoring modeling with different feature selection and machine learning approaches," Technology in Society, Elsevier, vol. 63(C).
    6. Stevenson, Matthew & Mues, Christophe & Bravo, Cristián, 2021. "The value of text for small business default prediction: A Deep Learning approach," European Journal of Operational Research, Elsevier, vol. 295(2), pages 758-771.
    7. Brenna O'Roarty & Stanley McGreal & Alastair Adair & David Patterson, 1997. "Case-based reasoning and retail rent determination," Journal of Property Research, Taylor & Francis Journals, vol. 14(4), pages 309-328, January.
    8. Sermpinis, Georgios & Tsoukas, Serafeim & Zhang, Ping, 2018. "Modelling market implied ratings using LASSO variable selection techniques," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 19-35.
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    Cited by:

    1. Konstantin Häusler & Hongyu Xia, 2022. "Indices on cryptocurrencies: an evaluation," Digital Finance, Springer, vol. 4(2), pages 149-167, September.
    2. Wei Li & Wolfgang Karl Hardle & Stefan Lessmann, 2022. "A Data-driven Case-based Reasoning in Bankruptcy Prediction," Papers 2211.00921, arXiv.org.

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    More about this item

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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