Machine Learning in Financial Fraud Detection: New Models for Predictive Analysis and Mitigating Business Risks

Abstract

Abstract This research investigates how machine learning (ML) algorithms can be applied to financial fraud detection as a way to improve predictive analysis and reduce business risk of fraudulent activities. Financial fraud is a major threat according to Association of Certified Fraud Examiners (2023) which may cost corporations and consumers more than $5 trillion. Many traditional fraud detection systems have relied on rule-based methods which can be restricted with predefined criteria and not able to adapt to evolving fraud patterns. For this research, we use advanced ML models such as Random Forest, Gradient Boosting, Neural Networks which are trained to analyze the large datasets for anomaly detection and to measure the predictive accuracy with real time analysis. In achieving such rates and with the objectives of accuracy, precision and recall met, we employ a dataset of over 1 million financial transactions from verified sources and apply algorithms which we then evaluate against the metrics. Most notably, we show that ML driven models integrate with conventional methods to reduce false positives by 30%, leading to operational efficiency and cost savings. In addition to providing academic knowledge by validating the robustness of ML techniques, this study provides actionable insights for financial institutions which are looking to implement scalable, data driven fraud prevention system. Through addressing technical and operation challenges, our research demonstrates the practicality of applying ML in lowering financial risk.