Credit Card Fraud Detector for Lower Ranged Transactions using AI Algorithms

Financial institutions and cardholders are equally at risk from credit card fraud, which can lead to large financial losses and compromised data security. In this work, artificial intelligence (AI) techniques are used to demonstrate a sophisticated method of detecting credit card fraud. The suggested system uses anomaly detection, machine learning techniques, and data analysis to effectively and accurately identify fraudulent transactions. Data preprocessing, feature engineering, and a model that was trained on a sizable dataset of both honest and dishonest credit card transactions are the main parts of our system. While the machine learning model is built to identify anomalies and patterns suggestive of fraud, feature engineering works on extracting relevant information from transaction data. By implementing retraining processes and keeping an eye on developing patterns, the system continuously adjusts to new fraud strategies.