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A data-driven explainable case-based reasoning approach for financial risk detection

Wei Li, Florentina Paraschiv and Georgios Sermpinis

No 2021-010, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

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, 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.

Keywords: Case-based reasoning; Financial risk detection; Multiple-criteria decision-making; Feature scoring; Particle swarm optimization; Parallel computing (search for similar items in EconPapers)
JEL-codes: C51 C52 C53 C61 C63 D81 G21 G32 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cfn, nep-cmp, nep-cwa, nep-ore and nep-rmg
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Working Paper: A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection (2021) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2021010

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