Banking fraud has become an important concern in India with the rapid growth of digital banking, electronic payments, and online financial transactions. Machine learning techniques are increasingly being applied to detect fraudulent transactions, but existing studies report different levels of performance because they use different datasets, algorithms, preprocessing methods, class-imbalance treatments, and evaluation measures. This study evaluates the effectiveness of machine learning techniques in detecting banking fraud in India using secondary empirical evidence. Relevant studies were examined and compared with focus on machinelearning techniques, data characteristics, class-imbalance treatment, and fraud-detection performance measures such as precision, recall, F1-score, and AUC. The findings indicate that machine learning demonstrates strong potential for banking fraud detection in India. Random Forest shows strong performance in Indian UPI research, while recent research using data described as actual Indian bank transactions reports strong performance for TabNet. However, the evidence does not support the conclusion that any single machine-learning technique is universally superior. Model effectiveness depends on the nature and quality of the data, preprocessing methods, class imbalance, validation procedures, and evaluation criteria. The study concludes that Indian banks should evaluate machine-learning models using multiple performance measures and consider practical factors such as false positives, interpretability, scalability, and operational requirements. Machine learning should therefore be viewed as an important component of broader banking fraud-risk management rather than as a standalone solution.

Tushar Kishan Pawar, Dr. Ujwala Narkhed, "Evaluating the Effectiveness of Machine Learning Techniques in Detecting Banking Fraud in India" International Journal of Scientific Research and Engineering Development, V9(5): Page(613-621) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.