Machine learning in estimating operational risk coverage capital of banks with a loss distribution Approach

Document Type : Original Article

Authors

1 M.A. Student in Financial Mathematics, University of Kharazmi, Tehran, Iran

2 Assistant Prof., Department of Financial Mathematics, Kharazmi University, Tehran, Iran

Abstract

Operational risk is one of the most important risks of financial institutions. After the approval of the Basel committee, it has started to be noticed all over the world. The increasing increase in operating losses in various business lines has caused the attention of financial institution managers to be directed to the area of operational risk. In this  research, a method for estimating the appropriate threshold for  operational loss data and also a method for classifying operational loss data is presented, and the capital required to cover operational risk by combining the intensity distribution function and frequency of operating loss data and Monte Carlo simulation are obtained. Also, the dependence between matrix cells of business lines and loss making events has also been investigated. For this research, operational loss data related to a banking group including several Asian, European and American banks have been used. The research results indicate that the loss distribution approach with the combination of critical value theory and machine learning algorithms (clustering), as well as the loss distribution approach with the combination of machine learning algorithms (classification), is more efficient than other methods.

Keywords

References

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Financial Management Perspective
Volume 13, Issue 42
July 2023
Pages 9-34

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