Credit Card Fraud Detection System

Abstract

Abstract: Credit Cards are quite useful for day to day life. The main aim of this project is to detect fraud accurately. With the increase in fraud rates, researchers have started using different machine learning methods to detect and analyze frauds in online transactions. 'Fraud' in credit card transactions is unauthorized and unwanted usage of an account by someone other than the owner of that account. Fraud detection involves monitoring the activities of users in whole in order to estimate, perceive or avoid objectionable behavior, which consist of fraud, intrusion, and defaulting. The problem itself is more challenging with respect to data science since the number of valid transactions far outnumber fraudulent ones. Also, the transaction patterns often change their statistical properties over the course of time. However, the massive stream of payment requests is quickly scanned by automatic tools that determine which transactions to authorize. Also, Messages are generated to confirm with the owner about the transactions. Machine learning algorithms are employed to analyze all the authorized transactions and report the suspicious ones. These reports are investigated by professionals who contact the cardholders to confirm if the transaction was genuine or fraudulent. This project also designs and develops a novel fraud detection method for Streaming Transaction Data, with an objective, to analyze the past transaction details of the customers and extract the behavioral patterns. To name a few techniques which we are going to implement are Isolation Forest Algorithm, Random Forest Algorithm, Logistic Regression, Confusion Matrix and Sliding-Window method. Keywords: Credit card fraud, isolation forest, local outlier factor, random forest, confusion matrix