Evaluating Supervised Learning Models for Fraud Detection: A Comparative Study of Classical and Deep Architectures on Imbalanced Transaction Data
2025-09-19
Summary
The article compares the effectiveness of four supervised learning models—Logistic Regression, Random Forest, Light Gradient Boosting Machine (LightGBM), and a Gated Recurrent Unit (GRU) network—on detecting fraud in highly imbalanced online transaction data. Random Forest and LightGBM exhibited strong overall and minority class performance, whereas Logistic Regression offered high interpretability with lower recall. The GRU model excelled in recall for fraud cases but had a higher false positive rate, highlighting the need to balance precision and recall in practical applications.
Why This Matters
Fraud detection is vital for industries like finance and e-commerce to prevent significant financial losses. This study provides insights into how different machine learning models can be effectively applied to detect rare but impactful fraudulent activities. By understanding the strengths and weaknesses of different models, organizations can choose the most suitable approach based on their specific risk tolerance and operational needs.
How You Can Use This Info
Working professionals can use this information to better design fraud detection systems by selecting models that align with their specific business objectives and constraints. For instance, if interpretability is crucial, Logistic Regression might be preferred, whereas Random Forest or LightGBM could be more suitable for complex environments where model accuracy is paramount. Additionally, understanding the trade-offs between precision and recall can help in setting up more effective monitoring and alert systems that balance security needs with operational efficiency.