Predicting Credit Card Fraud Detection Using Machine Learning

Identification of bank cards fraud remains a serious worry for financial companies because of how sophisticated criminal behavior is becoming.. This study addresses this challenge by leveraging machine learning algorithms to enhance the accuracy and efficiency of fraud detection systems. Utilizing a dataset from Kaggle, we implement and compare five distinct algorithms: LSTM networks, neural networks based on CNN, Decision Trees, Random Forests, and a Stacking Classifier. CNNs are employed to capture intricate patterns in transaction data, while LSTM address sequential dependencies and temporal dynamics. Decision Trees and Random Forests provide robust classification through hierarchical decision-making and ensemble learning. Additionally, a Stacking Classifier integrates the strengths of these algorithms to potentially improve overall performance. The comparative analysis of these methods tries to determine the best method for real-time identification of fraud. The results are expected to contribute significantly to the development of more secure credit card transaction systems, thereby mitigating financial losses and enhancing consumer trust.

Keywords: Fraud detection, machine learning, LSTM, CNN, Decision Trees, Random Forests.