Advanced Big Data Analysis Approach for Credit Card Fraud Detection

Fraud and default payments are two major anomalies in credit card transactions. Experts have been hard at work developing solutions, and one idea is to use data mining techniques. Credit card data, on the other side, can be tough for researchers to work with. This is related to the data qualities listed below: we have an unequal class distribution, and and redundant class samples. Both of these characteristics lead to low detection rates for minor anomalies in the data. Furthermore, defects in general learning algorithms contribute to the difficulty in categorising anomalies because the algorithms favour the majority class samples m. Each neural network configuration's findings are displayed and explained. To proposed advance big data classification technique based on fusion machine learning technique. The best method detects 98.81 percent of total fraud cases while minimising alarms by 30.32 percent.