Fraud Detection In Financial Transactions A Machine Learning-Based Approach To Risk Mitigation

Abstract

 One of the most essential applications of banking data is financial fraud detection, but current rule-based systems cannot keep up with the ever-changing nature of financial fraud, and performance tends to lag rapidly, resulting in relatively high false positive rates. However, the existing systems are built on rigid rules that must be regularly modified when fraud trends evolve, resulting in ineffectiveness and low efficiency. In contrast, the above-mentioned system employs ML methods such as Random Forest (RF), Support Vector Machines (SVM), and Neural Networks (NN), which may be updated on a continuous basis to reflect evolving fraud trends. It achieves 94% accuracy, 92% precision, and 91% recall, which far outperforms any basic system. These improves accuracy, with a decreased false positive rate of ~3%, and helps uncover fraudulent activities. It also explore the study's implications for future use cases, such as the proposed system's real-time monitoring capabilities and flexibility, which could be a potential solution to reduce the ease with which the type of financial fraud occurs.