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

Wei Li, Florentina Paraschiv and Georgios Sermpinis

Papers from arXiv.org

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.

Date: 2021-07
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: A data-driven explainable case-based reasoning approach for financial risk detection (2022)
Working Paper: A data-driven explainable case-based reasoning approach for financial risk detection (2021)
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