Machine Learning Approach for Detection of Financial Fraud Using Value at Risk

As more people utilise online banking services, the large losses that banks and other financial institutions sustained because of new bank account (NBA) fraud are concerning. Machine learning (ML) models have faced significant challenges because to the intrinsic skewness and rarity of NBA fraud cases. This occurs when the number of non-fraud instances exceeds the number of fraud instances, causing the ML models to miss and mistakenly regard fraud as non-fraud instances. Customers' confidence and trust may be damaged by such mistakes. Existing research addresses the skewness of fraud datasets by considering fraud patterns rather than possible losses of NBA fraud risk characteristics. This study suggests NBA fraud detection in the framework of value-at-risk, a risk metric that views fraud cases as the worst-case situation. Value-at-risk models risk features as a skewed tail distribution and estimates possible losses of such attributes using historical simulation. ML was used to classify the risk-return characteristics derived from value-at-risk on the bank account fraud (BAF) dataset. The value-at-risk assigns weight to the skewed NBA fraud cases by managing the fraud skewness with an adjusted threshold probability range. The effectiveness of the fraud detection algorithm was assessed using a unique detection rate (DT) metric that takes risk fraud characteristics into account. A K-nearest neighbour with a true positive (TP) rate of 0.95 and a DT rate of 0.9406 is used to create an enhanced fraud detection model. Value-at-risk offers a clever way to create data-driven standards for fraud risk management within a reasonable loss tolerance in the banking industry.