Scalable Machine Learning Models Achieving Efficiency in Fraud Detection

Fraud detection has become an integral part of current business operations, particularly in financial institutions, e-commerce, and insurance. A fraud detection system would need to balance between accuracy, speed, and cost while maintaining optimal efficiency. However, existing machine learning (ML) approaches face challenges such as system latency, computational overhead, imbalanced datasets, and real-time processing constraints. Recent techniques, including deep learning models, ensemble methods, and hybrid approaches, have improved detection rates but they are prone to introduce scalability and interpretability concerns. For this work, we use real-world data in building, testing, and optimization of an ML-based fraud detection system. With production ML models there is always a need to monitor and continuously improve to maintain efficiency in detection. Running a series of experiments and analyzing data, we propose best practices for designing and deploying scalable fraud detection systems with high detection rates and minimal overhead in operations. We propose a hybrid model that combines the simplicity of linear regression with the depth of deep neural networks, offering significant improvements in fraud detection accuracy and speed. Experimental results demonstrate that the hybrid model achieves a 93.2% accuracy rate with enhanced precision and recall, reducing computational overhead compared to standalone models. This study highlights the potential of hybrid approaches to revolutionize scalable and cost-effective fraud detection solutions.