Accuracies of Model Risks in Finance using Machine Learning

There is increasing interest in using Artificial Intelligence (AI) and machine learning techniques to enhance risk management from credit risk to operational risk. Moreover, recent applications of machine learning models in risk management have proved efficient. That notwithstanding, while using machine learning techniques can have considerable benefits, they also can introduce risk of their own, when the models are wrong. Therefore, machine learning models must be tested and validated before they can be used. The aim of this work is to explore some existing machine learning models for operational risk, by comparing their accuracies. Because a model should add value and reduce risk, particular attention is paid on how to evaluate it's performance, robustness and limitations. After using the existing machine learning and deep learning methods for operational risk, particularly on risk of fraud, we compared accuracies of these models based on the following metrics: accuracy, F1-Score, AUROC curve and precision. We equally used quantitative validation such as Back-testing and Stress-testing for performance analysis of the model on historical data, and the sensibility of the model for extreme but plausible scenarios like the Covid-19 period. Our results show that, Logistic regression out performs all deep learning models considered for fraud detection