Fraud Detection in Banking Using Machine Learning

Fraud detection in banking is a critical issue for financial institutions, with increasing sophistication offraudulent activities posing significant risks to security and financial stability.This paper explores theapplication of machine learning (ML) techniques to enhance the accuracy and efficiency of fraud detectionsystems.By leveraging supervised and unsupervised learning algorithms, we propose a robust framework for detectingfraudulent transactions in real-time.The study examines several ML models, including decision trees, supportvector machines, and deep learning networks, comparing their performance based on key metrics such asprecision, recall, and F1-score.Additionally, we highlight the importance of feature engineering, datapreprocessing, and imbalanced dataset handling to improve model performance.Our results demonstrate thepotential of ML-driven fraud detection systems in identifying and mitigating fraud risks, offering financialinstitutions a scalable and adaptive solution to combat emerging threats.