Credit Card Fraud Detection Using Random Forest Machine Learning
Author(s):
Akash, Neeraj Arya
Keywords:
Abstract
Credit card hoax exposure is the process of identifying fraudulent transactions made using credit cards. With the growing admiration of online shopping and the prevalent practice of credit cards, falsified activities have become a substantial apprehension for businesses and consumers alike. Fraudulent happenings can generate consequence in financial losses, impairment to the reputation of businesses, and can compromise the personal information of customers. Therefore, it is grave to distinguish and thwart fraudulent transactions. There are several approaches used to perceive credit card fraud, including rule-based systems, statistical representations, machine learning algorithms, and deep learning techniques. Rule-based systems use a set of predefined directions to detect apprehensive transactions based on explicit criteria. Arithmetical models use historical data to categorize patterns and incongruities in transaction behavior. Machine learning algorithms, like logistic regression, decision trees, and support vector machines, use historical data to create predictive models that can detect fraudulent activities. Moreover, this research paper deals with how fraud detection methods have become inadequate in the face of the sophisticated techniques used by fraudsters and we can use different Machine Learning algorithm in order to resolve the problem of credit card deception, worldwide. We discovered efficiency of many machine learning algorithms in perceiving credit card fraud, and the results demonstrate that ensemble models such as Random Forest and Gradient Boosting can achieve high accuracy and detection rates. Furthermore, we discovered that incorporating outlier detection techniques such as Local Outlier Factor and Isolation Forest can improve the performance of our models. Overall, our findings suggest that machine learning can be a powerful tool in combating credit card fraud and can help financial institutions better protect their customers' assets.
Article Details
Unique Paper ID: 161059

Publication Volume & Issue: Volume 10, Issue 2

Page(s): 484 - 489
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