Cutting-Edge Novel Method for Credit Card Fraud Detection: Using Data Science Techniques and Machine Learning Algorithms

Abstract

Credit card fraud is a common vice that affects not only the financial institutions issuing credit cards but the card holders themselves hence the need to address issues related to it by having proper detection measures in place. Thus, in this project, we seek to carry out the following analysis: We look critically at the machine learning techniques that can be used for credit card fraud detection in order to assess the effectiveness of using various machine learning techniques in enhancing precision and speed of the detection process. The technique of Data preprocessing have also used. This step is used to address problems related to missing values, outliers and class imbalance. We then go onto building up a machine learning pipeline that includes various classifiers such as Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, Random Forests, and the Stochastic Gradient Descent Classifier. In our testing approach, which involves repeating the exploration phase several times with a different classifier, we can also measure its performance based on the provided set of generating metrics including precision, recall, F1-scores, ROC-AUC scores, and accuracy. To counter this we use techniques such as Synthetic Minority Over-sampling Technique (SMOTE) for creating synthetic samples of the distributions of the samples categorized as the minority class. Moreover, hyperparameters for each feature selection method are optimized using grid search with cross-validation since proper tuning improves the model’s performance. The major goal of the study is to compare and analyze the comparative efficacy of different classifiers to arrive at an optimal approach for credit card fraud detection through the application of data science machine learning models. From this perspective, our paper is intended to offer insights into the advantages and disadvantages of deploying specific techniques which will help the stakeholders to make right decision on utilizing the fraud detection systems. Using the latest machine learning approaches and careful assessment strategies, we aim at improving the effectiveness of fraud detection tools, minimizing the adverse financial impacts of fraudulent activities, and maintaining the public’s confidence in automated security systems. Our work has significant implications for the state-of-the-art of fraud detection in the financial sector, and provides valuable information and insights into the fight against increasingly popular credit card.