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

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: IRTG 1792 Discussion Paper ; No. 2021-010

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Model Construction and Estimation ; Model Evaluation, Validation, and Selection ; Forecasting Models; Simulation Methods ; Optimization Techniques; Programming Models; Dynamic Analysis ; Computational Techniques; Simulation Modeling ; Criteria for Decision-Making under Risk and Uncertainty ; Banks; Depository Institutions; Micro Finance Institutions; Mortgages ; Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
• Thema: Case-based reasoning ; Financial risk detection ; Multiple-criteria decision-making ; Feature scoring ; Particle swarm optimization ; Parallel computing

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Li, Wei ; Paraschiv, Florentina ; Sermpinis, Georgios
• Ereignis: Veröffentlichung
• (wer): Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
• (wo): Berlin
• (wann): 2021

Weitere Informationen

• Handle: http://hdl.handle.net/10419/235589
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ