Cost-Benefit Analysis of AWS Lambda in Fraud Management: Processing $2 Billion in Fraud Claims with Minimal Overhead

Fraud detection in financial systems is complex and requires cost-effective, efficient, and scalable solutions. Models of fraud detection that identify potentially fraudulent cases have substantial infrastructure costs, resulting in inefficient use of resources. This study explores whether it is possible to process $2 billion in Fraud claims using an on-demand serverless computing framework called AWS Lambda, without incurring substantial costs and with minimal overhead. As part of the analysis, the fraud detection capabilities of the AWS Lambda model were also assessed in relation to traditional infrastructure costs, scalability, and efficiency in data processing. The evidence indicated that AWS Lambda significantly reduced costs, improved operational flexibility, and successfully identified possibly fraudulent cases. This study also indicated challenges associated with the processing limitations for larger fraud detection workloads, cold start times, and administrative burdens related to integration of AWS Lambda. The findings reveal important implications but also limitations of the potential of serverless computing for succeeding in more scalable fraud detection solutions needed for large-scale financial services.