Reducing Wire Transfer Fraud Risks for U.S. Small Banks: Implementing Affordable AI-Based Anomaly Detection
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Reducing Wire Transfer Fraud Risks for U.S. Small Banks: Implementing Affordable AI-Based Anomaly Detection
Saikrishna Garlapati,
Independent Researcher
garlapatisaikrishna94@gmail.com
Abstract
In the United States, wire transfer fraud can be a great challenge among the small banks. Because they have fewer resources, they mostly become the targets of fraudsters who can compromise online banking transactions. Cybercriminals can infiltrate networks and use techniques such as Business Email Compromise (BEC), social engineering, or use account takeovers. While fraud detection systems based on rules can be cost-effective, they may be incapable to deal with new fraud patterns or newly developed attacks. Thus, seeking new fraud detection systems is essential. This paper aims to study the implementation of an AI-based fraud detection system that can provide cost benefits for small banks. For this, AI techniques such as machine learning algorithms and deep learning methods can be utilized for fraud detection and prevention. Through real-time analysis of transaction patterns, it can minimize false positives. For this, the research will analyze the impact of supervised and unsupervised learning approaches to AI fraud detection systems and examine the trade-off they have in comparison with the traditional detection systems. It is also important that the applicability of AI-based fraud detection is possible to small banks that have limited budgets. Thus, the study will propose the AI fraud detection system scalable framework that is affordable and can be easily integrated into the banking system for small banks. The experiments conducted in the study will provide the experimental validation results. It can further demonstrate the performance of the approach in terms of higher detection rates. In addition, it can enhance the monitoring of wire transfer transactions. The overall impact of the study creates awareness about how AI-based fraud detection is important in financial security. It can thereby provide a basis for the recommendations for the possible future work enhancements. It includes adaptive AI models ensuring compliance with regulatory requirements.
Keywords
Wire transfer fraud, anomaly detection, AI-based security, small banks, financial fraud, cybersecurity, machine learning