Credit Card Fraud Detection Using AI (Python)

Abstract

Credit card fraud poses a formidable obstacle in the financial sector, resulting in considerable monetary damages for both individuals and institutions. The necessity for efficient fraud detection systems has become paramount due to the rising number of online transactions and the advancement of fraudulent methods. Machine learning methods have demonstrated potential in tackling this issue by utilizing past transaction data to detect fraudulent behavior. This study provides a thorough examination and evaluation of different machine learning techniques used in the detection of credit card fraud. The aim of this study is to assess and compare the efficacy of several machine learning algorithms in identifying fraudulent credit card transactions. The dataset utilized for experimentation is acquired from a prominent financial institution, including of both authentic and deceitful transactions. The dataset has been preprocessed to address missing values, outliers, and feature scaling. A variety of machine learning algorithms, such as “logistic regression, decision trees, random forests, support vector machines (SVM), and artificial neural networks (ANN),” are utilized and trained on the preprocessed information. The evaluation of each method is conducted using criteria like as “accuracy, precision, recall, and F1-score. In addition, several evaluation methods, such as k-fold cross-validation”, are used to assure the reliability of the findings. The empirical findings suggest that machine learning algorithms has the capability to accurately identify fraudulent credit card transactions. The algorithms demonstrate varying performance across different parameters, with certain algorithms displaying higher accuracy but worse precision or recall. The “Support Vector Machine (SVM)” algorithm gets the maximum accuracy rate of 98%, while the “Artificial Neural Network (ANN)” model displays the optimal balance between precision and recall..