How GoML built an AI transaction monitoring tool to reduce fraud detection time by 82% for Miden

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How GoML built an AI transaction monitoring tool to reduce fraud detection time by 82% for Miden

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Miden, a fintech company handling payments and banking across Africa, deployed a generative AI-powered transaction monitoring tool that reduced fraud detection time by 82%, improved transaction processing scalability by 75%, and cut manual monitoring effort by 67%. GoML's Gen AI transaction monitoring tool is helping Miden ensure financial security at scale, with real-time, automated fraud detection available across all transaction channels enabling more proactive, timely interventions, demonstrating the power of AI for enterprise-level financial protection. Manual transaction monitoring is expensive, time-intensive, and highly dependent on specialist availability. For Miden, a surge in transaction volumes and limited monitoring capacity meant that fraud detection was significantly delayed. Traditional monitoring systems faced challenges including false positives, delayed alerts, and inefficiency at scale. "An AI-powered transaction monitoring tool wasn't a nice-to-have, it was a strategic imperative to deliver real-time fraud detection at scale without compromising accuracy or operational efficiency." Deployed via AWS infrastructure, the solution was integrated into Miden's existing banking infrastructure. The comprehensive Gen AI tool provides an AI-driven analytics dashboard enabling real-time insights into transaction patterns and anomalies across all channels, with automated anomaly detection powering instant fraud identification. The models classify transactions into risk categories — suspicious, normal, and flagged for review — within seconds, enabling targeted human-in-the-loop analysis and intervention. The technical architecture relies on AWS Lambda for fast, serverless computing for real-time processing, with data stored securely in AWS S3 and Postgres for structured management. The solution integrates with banking applications via containerized Docker deployment, with Python and RDBMS powering the core analytics engine. Integrated secure access ensures proper authentication for financial professionals. At scale, real-time alerts are delivered via dashboard to analysts and risk teams, with analytics enabling administrators to monitor transaction trends and fraud patterns continuously. The AI handles growing transaction volumes effortlessly, enabling more efficient, accurate, and scalable monitoring across all channels. The results demonstrate the power of AI for enterprise-level financial protection, and Miden now operates with significantly reduced operational costs and human dependency while maintaining high detection accuracy.