Credit Card Batch Processing in Banking System

Abstract

Batch payments, involving a single transaction to multiple recipients via a single bank account, offer an efficient alternative to individual payments. This method condenses multiple payments into a single debit entry on bank statements, accelerating payment processing and enhancing business efficiency. Utilizing bank wire transfers is a prevalent means of initiating batch payments.

In contrast to real-time processing, where transactions are handled immediately, batch processing entails aggregating authorized credit card transactions by merchants for submission to their credit card processors at the close of each business day or at scheduled intervals [1]. Merchant-authorized credit card transactions are compiled and sent to customers’ banks, seeking authorization. Upon approval, funds are transferred to the business’s bank account.

Our objective is to identify fraudulent credit card transactions to safeguard credit card customers from erroneous charges for unauthorized purchases. The model employed aims to be both swift and adept at detecting anomalies, swiftly classifying potentially fraudulent transactions.

The study involved the evaluation of multiple classifiers such as Gradient-Boosted Tree (GBT) and Random Forest, revealing that while the GBT classifier exhibited exceptional precision, the Random Forest classifier emerged as the preferred choice for our dataset. The selection was based on practical considerations, including efficiency and ease of implementation.