A Review on Financial Fraud Detection using AI and Machine Learning

Abstract

This study thoroughly explores advanced approaches for addressing financial fraud, focusing on the effectiveness of Machine Learning (ML) and Artificial Intelligence (AI). Recognizing the drawbacks of outdated methods, the examination aims to analyze the current situation, closely examining the efficiency and limitations of ML and AI techniques while mapping out intricate directions for future research. We delve into the intricate history of financial fraud, uncovering the inherent constraints embedded in conventional rule-based and manual detection approaches. Then, machine learning (ML) and artificial intelligence (AI) are discussed, highlighting significant research and successful implementations that have transformed the field of fraud detection. While analyzing the assessment metrics, we use various measures such as accuracy, precision, recall, F1 score, and the enigmatic ROC-AUC. Then, diverse ML and AI algorithms are introduced, including the mysterious Random Forest, the reliable Support Vector Machines (SVM), and the complex neural networks. As comparative analysis unfurls, uncovering the strengths and weaknesses inherent in distinct ML and AI systems. Beyond the limits of performance measures, our interpretation transcends, diving into the real-world ramifications and the labyrinth of possible routes for refinement and advancement.