Machine Learning Technique For Detecting Banking Fraud Based on Big Data Analysis

Abstract

Credit cards are an essential part of every person's daily life in the modern world. Customers use their credit cards and online transactions to make purchases for their requirements. To reduce the risk of defaulters, banks and other financial institutions consider customer credit applications. Credit risk is the increase in debt owed by a customer who repeatedly misses payments on their bills. The project's goal is to find ways to cut down on the number of defaulters on the list of clients, conduct a background check to determine whether or not to grant a loan and identify clients who show promise. Because they would increase their awareness of their possible default rate, these predictive models would be advantageous to both the lending institutions and the clients. Whether a customer will be in default on their payment for the following month is the issue. Due to the imbalance in the dataset. The precision and recall measures received greater attention than the metrics for accuracy. Using the confusion metrics' False Negative value, logistic regression is best model after comparison with the precision-recall curve. Additionally, a GUI (Graphical User Interface) was built after altering the logistic regression's threshold value and it successfully forecasted whether a customer was a defaulter or not.