Fraud Detection in Banking Using Machine Learning Techniques

With the growth of internet banking and digital transactions, banking fraud has become a major threat.Sophisticated and changing fraud patterns are frequently missed by traditional fraud detection systems, which mostly use rule-based techniques.By learning from past transaction data and uncovering hidden patterns linked to fraudulent activity, machine learning (ML) approaches offer a flexible and reliable substitute.The use of several machine learning methods, such as logistic regression, decision trees, random forests, support vector machines, and neural networks, to efficiently identify banking fraud is examined in this work.We go over handling class imbalance, feature engineering, data pretreatment, and model evaluation metrics.Additionally, the study contrasts various machine learning models using performance metrics like F1-score, recall, and precision.Experimental results demonstrate that ensemble methods and deep learning models significantly outperform traditional techniques in terms of fraud detection accuracy.This work aims to provide a roadmap for implementing scalable and intelligent fraud detection systems using machine learning in the banking industry.