Citation:

Mahmoud Daneshmand, 2024. Financial Fraud Detection: A Comparative Analysis of AI and ML Techniques, ESP Journal of Engineering & Technology Advancements  4(2): 138-142.

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 Recognizing the drawbacks of outdated methods, the examination aims to analyze the current situation, closely examining the efficiency and limitations of ML and AI techniques while mapping out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. By analyzing assessment metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, this study introduces diverse ML and AI algorithms, including Random Forest, Support Vector Machines (SVM), and complex neural networks. This comparative analysis uncovers the strengths and weaknesses of distinct ML and AI systems, transcending performance measures to dive into real-world ramifications and possible avenues for refinement. The findings not only provide insights into current methodologies but also propose future directions in combating fraud effectively through innovative technologies.

References:

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Keywords:

Financial Fraud, Machine Learning, Artificial Intelligence, Fraud Detection, Supervised Learning, Unsupervised Learning, Algorithmic Approaches, Blockchain Technology.