Authors - Ruchita Borikar, Sakshi Thorat, A.S. Ingole, U.A. Kandare Abstract - Credit card fraud remains a significant issue in the financial industry, with increasing numbers of transactions being processed online and through digital platforms. Traditional fraud detection systems, relying on predefined rules and manual analysis, are no longer adequate to combat the growing complexity and scale of fraudulent activities. In response, machine learning (ML) has emerged as an effective solution for detecting fraud in real time, offering the ability to analyze large datasets and recognize patterns that may indicate suspicious behavior. This project aims to build a system for fraud using machine learning methods, based on a dataset sourced from Kaggle and guided by an IEEE paper. The project involves several key stages, starting from raw data preprocessing and feature selection, followed by training and evaluating machine learning models with algorithms like Decision Trees, Logistic Regression and Random Forest and Support Vector Machines (SVM). Additionally, the model is evaluated through 5-fold cross validation to ensure robustness. This system not only enhances fraud detection accuracy but also minimizes false positives, thereby improving overall efficiency.
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