A Deep Reinforcement Learning Framework for Detecting Fraudulent Bank Account Openings

Abdul Qayoom; Wu Yadong; Wang Song; Sammar Abbas; Nadeem Ghafoor

doi:10.33317/ssurj.653

Authors

Abdul Qayoom 1- School of Computer Science and Technology, Southwest University of Science and Technology, 2- Department of Computer Science, Lasbela University of Agriculture, Water and Marine Sciences, Uthal, Lasbela, Balochistan, Pakistan https://orcid.org/0000-0002-5510-6078
Wu Yadong 1- School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China. , 2- School of Computer Science and Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan, China
Wang Song School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, Sichuan, China
Sammar Abbas School of Information Engineering, SouthWest University of Science and Technology, Mianyang, Sichuan, China
Nadeem Ghafoor School of Computer Science and Technology, Southwest University of Science and Technology https://orcid.org/0009-0003-4871-650X

DOI:

https://doi.org/10.33317/ssurj.653

Keywords:

Artificial Intelligence in Banking, Deep Reinforcement Learning, Bank Account Fraud Detection, Deep Q-networks, Financial Fraud Detection

Abstract

Online banking has become more popular and widespread. A huge number of customers open new bank accounts every day, which leads to a rise in fraudulent account openings. Many fraudulent activities related to this have been reported over time. It has remained a difficult task to detect fraudulent accounts efficiently. Artificial Intelligence (AI) is performing well in all the domains in the real world. So, the objective of this research is to build the most effective model to detect electronic financial transaction fraud in bank account opening. This research work aims to solve this problem (1) how to use deep reinforcement learning (DRL) for the detection of fraudulent bank accounts and (2) develop a Deep Q-Networks (DQN) based solution architecture for the detection of bank account opening fraud in an efficient way. Our proposed model achieved the highest fraud detection accuracy with 97% using the DQN algorithm with the benchmark Kaggle dataset for bank account fraud detection.

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