ASSESSMENT OF MACHINE LEARNING ALGORITHMS FOR FINANCIAL FRAUD DETECTION

Abstract

The rapid growth of digital financial services has significantly increased the complexity and frequency of financial fraud, posing serious challenges to financial institutions and regulatory bodies. Traditional rule-based fraud detection systems are increasingly ineffective against evolving fraud patterns due to their rigidity and inability to adapt to large-scale, high-dimensional transaction data. This paper presents a comprehensive assessment of machine learning algorithms for financial fraud detection, focusing on their effectiveness, scalability, and practical applicability. Supervised, unsupervised, and ensemble-based machine learning techniques are evaluated using transactional datasets characterized by class imbalance and non-linear patterns. The study analyzes algorithmic performance in terms of accuracy, precision, recall, F1- score, and computational efficiency. Experimental results indicate that ensemble and hybrid models outperform individual classifiers in detecting fraudulent activities while maintaining acceptable false- positive rates. The paper further discusses data preprocessing challenges, feature engineering strategies, and ethical considerations associated with automated fraud detection systems. The findings highlight the importance of adaptive, data-driven models in combating financial fraud and provide insights into the selection of appropriate machine learning techniques for real world deployment.