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

Li, W., Paraschiv, F. and Sermpinis, G. (2022) A data-driven explainable case-based reasoning approach for financial risk detection. Quantitative Finance, 22(12), pp. 2257-2274. (doi: 10.1080/14697688.2022.2118071)

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.

• Item Type: Articles
• Additional Information: This work acknowledges research support by COST Action “Fintech and Artificial Intelligence in Finance - Towards a transparent financial industry” (FinAI) CA19130.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sermpinis, Professor Georgios
• Authors: Li, W., Paraschiv, F., and Sermpinis, G.
• College/School: College of Social Sciences > Adam Smith Business School > Accounting and Finance
• Journal Name: Quantitative Finance
• Publisher: Taylor and Francis
• ISSN: 1469-7688
• ISSN (Online): 1469-7696
• Published Online: 28 September 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Quantitative Finance 22(12): 2257-2274
• Publisher Policy: Reproduced under a Creative Commons License