CREDIT CARD FRAUD DETECTION USING ISOLATION FOREST AND LOCAL OUTLIER FACTOR

Abstract

Nowadays, card fraud is increasing due to the prevalence of modern technology. Thus, Automatic systems to detect and prevent against card fraud are a significant tool in the financial industries battle against card crime. Machine learning and novelty detection techniques approaches are taken in Credit Card Fraud Detection field, since they are effective technologies and methodologies and easy to apply at the same time. It is used in order to reduce fraud activities. The main aim of this project was to create a program that detects and Identifies potentially fraudulent credit card transactions from a given data set, and evaluate its performance to be compared with other classifiers with evaluation metric. The program was trained with the given data set, based on the some of the most popular Machine Learning and Deep Learning Classification algorithms which are; Random Forest, Isolation Forest and Neural Networks Algorithm Load Balancing and Feature Selection were maintained throughout the project, and it was implemented using Python programming language. However, there were no autonomous system that will be able to categorically define a transaction as fraud. The objective was to highlight those transactions that have a high probability of being fraudulent based on some criteria, known or otherwise learnt. Key Words: isolation forest and local outlier factor, Pandas, Json, Matplotlib and seaborn.