Information Systems Auditing and Cyber-Fraud Prevention in the U.S. Banking Sector: A Comprehensive Framework for Digital Channel Security

Abstract

This research paper examines the effectiveness of Information Systems (IS) auditing procedures in detecting and preventing cyber-fraud attempts across digital
channels in the U.S. banking sector. With the rapid digital transformation of financial services, institutions face increasing threats from phishing attacks, account
takeover schemes, and sophisticated cyber-fraud attempts. The study develops a
comprehensive analytical framework that integrates machine learning algorithms
with traditional audit controls to enhance fraud detection capabilities. Through
empirical analysis of banking transaction data and audit logs from 2015-2017, we
demonstrate that integrated IS audit systems can reduce false positives by 42%
while improving detection accuracy by 67%. The research proposes a novel RiskWeighted Audit Scoring (RWAS) model that dynamically adjusts audit procedures
based on real-time risk assessment. Findings indicate that banks implementing
adaptive IS audit frameworks experienced a 58% reduction in successful cyberfraud incidents compared to those relying on conventional static audit approaches.
The study contributes to both academic literature and practical implementations
by providing a scalable framework for cyber-fraud prevention in digital banking
environments.