The Role of Machine Learning in Payment Card Transaction Processing by Chandra Mouli Yalamanchili

 Chandra Mouli Yalamanchili's journey through the world of payment systems and machine learning is marked by a series of deliberate choices. With a Master's degree in Data Science, he has applied the skills learned in his academic years to real-world challenges, particularly in the realm of payment card lifecycle management.

In the world of artificial intelligence and machine learning, at Fiserv, Yalamanchili played an important role in integrating a machine learning-based fraud detection engine into a large-scale payment platform. The integration leverages machine learning techniques to analyze historical transaction trends and suggest adaptive fraud rule modifications. 

These suggestions were designed to improve the system's fraud hit rate by accurately capturing true fraud while minimizing disruptions to legitimate cardholder activity.

The implemented system reduced fraud liability for issuing banks significantly while decreasing false positive alerts. This led to improved customer satisfaction and a noticeable drop in call center volume due to fewer cardholder disputes.

One of his notable projects involved leading the development of an authorization orchestration service capable of handling approximately 100 million transactions. This service maintained an operating SLA of 25 milliseconds and a response time SLA ceiling of 250 ms, ensuring rapid and reliable transaction processing. By facilitating secure communication between the authorization system and external ML fraud products, Yalamanchili ensured that the system remained responsive and accurate, even under peak load conditions.

The solution uses raw socket-based persistent connections to maintain high throughput and minimal latency, ensuring seamless integration into the transaction flow. He also contributed to defining integration patterns and fallback strategies to handle real-time inference failures gracefully and supported post-integration analytics by collaborating on dashboards and reports to monitor model performance, track drift, and establish retraining cadences.

The authorization orchestration service handled around 100 million transactions per day, ensuring consistent performance even under peak load conditions.

To come upon these desired results, he had to address certain challenges. He addressed challenges such as integrating complex ML pipelines into high-throughput transactional systems without introducing latency. By implementing custom-coded communication using raw socket-based persistent connections, he eliminated the overhead of traditional communication protocols. Additionally, he tackled the significant compute and memory demands of simulating new fraud rules against historical data by provisioning infrastructure with higher memory and CPU configurations.

Further, the fraud product supports dynamic scoring using client-provided PMML models, introduces added latency and potential points of failure within fraud detection workflows. They overcame this by implementing strong error-handling mechanisms and establishing strict timeout thresholds for PMML execution, ensuring stability and responsiveness even under high-load conditions.

Beyond his professional commitments, his scholarly work includes the publication of "Credit Card Fraud Detection Using Data Science" and "Hybrid AI on IBM Z: Options and Technical Insights,” which underscores the potential of machine learning models in enhancing fraud detection systems. 

Looking at the current trends, Yalamanchili emphasizes the evolving role of machine learning in payment systems. He tells us that ML is no longer just a backend analytics tool—it is evolving into a real-time decision engine deeply embedded in the transaction path.

He also notes a growing trend where clients are interested in deploying custom models to dynamically generate fraud scores, reflecting a shift toward greater control and customization in fraud detection workflows. He also highlights advancements like IBM's Telum processor, which brings AI capabilities directly to mainframe platforms, enabling real-time inferencing within transaction flows.

Yalamanchili's perspective is clear: while machine learning has already brought significant efficiency gains to the financial services domain, its full potential is yet to be realized. From analyzing customer trends to designing new, tailored credit card products, data science can be a cornerstone for customer-centric innovation.

“At the same time, the power of machine learning comes with a responsibility,” he tells us. “As automated decision-making becomes more prevalent, the importance of responsible AI practices and explainability cannot be overstated”.

As AI and ML technologies continue to mature, he is committed to contributing to innovations that will shape the current and future of secure, efficient, and intelligent payment systems.