AI-Powered Fraud Detection in the Financial Services Sector: A Machine Learning Approach

Financial Fraud Detection is essential for securing financial transactions and mitigating fraudulent activities. This paper explores various fraud types, such as payment fraud and account takeover fraud, highlighting the urgency due to online payment fraud projected to exceed ${\$}$ 343 billion by 2027. We discuss methodologies like Machine Learning, Behavioral Analytics, Anomaly Detection, and Data Analytics for detecting fraud. Using a synthetic dataset of mobile money transactions, we evaluated three machine learning models: Logistic Regression, Decision Tree, and Multi-Layer Perceptron (MLP). While Logistic Regression struggled with imbalanced data, the Decision Tree showed balanced performance with better precision and recall. The MLP, though precise, had lower recall, missing significant fraud cases. These findings emphasize the need to fine-tune models for effective fraud detection. This research identifies threats in financial security and proposes adaptive strategies to protect businesses and consumers in a digital financial landscape.