Construction of an AI-Driven Risk Management Framework for Financial Service Firms Using the MRDM Approach

Kuang-Hua Hu (), Fu-Hsiang Chen, Ming-Fu Hsu () and Gwo-Hshiung Tzeng ()
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Kuang-Hua Hu: School of Accounting, Finance and Accounting Research Center, Nanfang College, Guangzhou, Guangdong 510970, P. R. China
Fu-Hsiang Chen: Department of Accounting, College of Business, Chinese Culture University, 55, Hwa-Kang Road, Yang-Ming-Shan, Taipei 11114, Taiwan, R. O. China
Ming-Fu Hsu: English Program of Global Business, Chinese Culture University, 55, Hwa-Kang Road, Yang-Ming-Shan, Taipei 11114, Taiwan, R. O. China
Gwo-Hshiung Tzeng: Graduate Institute of Urban Planning, College of Public Affairs, National Taipei University, Taipei Campus, 67, Sec. 3, Ming-shen E. Road, Taipei 10478, Taiwan, R. O. China

International Journal of Information Technology & Decision Making (IJITDM), 2021, vol. 20, issue 03, 1037-1069

Abstract: The complex problem of risk factors has greatly increased globally due to the quick ever-changing digital era. The development of suitable techniques for facilitating the performance of risk management in the financial service domain is thus an urgent task, especially in today’s highly turbulent business environment. The development of such techniques involves many factors like the classical multiple criteria decision-making (MCDM) problem, but too many factors surrounding the users will confuse them and lead to improper judgments. To deal with this critical task, this study proposes a fusion multiple rule-based decision-making (MRDM) approach that integrates a rule-based technique [i.e., the fuzzy rough set theory (FRST) with particle swarm optimization (PSO)] into MCDM (i.e., DEMATEL, DANP, and modified-VIKOR) techniques that can help decision makers choose the optimal model necessary for achieving aspiration-level effects in a risk control strategy. The results indicate that the improvement priority, which runs in the order as (a) AI algorithm model, (c) AI regulatory and compliance, (d) AI conduct, and (b) AI technology based on the magnitude of the impact, can effectively improve the performance of AI-driven risk management for financial service firms.

Keywords: Artificial intelligence (AI); risk management (RM); fuzzy rough set theory (FRST); multiple criteria decision-making (MCDM); multiple rule-based decision-making (MRDM) (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0219622021500279

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