Machine Learning Applications in Financial Fraud Detection: A Hybrid Approach for Enhanced Security and Risk Management

The financial services industry faces increasingly sophisticated fraud attempts that pose significant risks to institutions and their customers. This paper presents an innovative hybrid machine learning framework for detecting fraudulent financial transactions and activities. By strategically combining complementary algorithms—Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU)—our approach capitalizes on the distinct advantages of each model to maximize detection capabilities. The comprehensive methodology encompasses multi-source data collection, advanced preprocessing techniques, domain-specific feature engineering, parallel model training, and weighted ensemble integration. Experimental results from a dataset of over 100,000 financial transactions demonstrate that our hybrid model achieves superior detection accuracy (96.8%) compared to individual algorithms (ranging from 89.3% to 94.1%), while significantly reducing false positives and exhibiting enhanced resilience against emerging fraud patterns. This research provides financial institutions with an implementable framework to strengthen their fraud detection infrastructure and improve overall risk management strategies.