An AI-powered Smart Routing Solution for Payment Systems (2111.00783v1)

Abstract: In the current era of digitization, online payment systems are attracting considerable interest. Improving the efficiency of a payment system is important since it has a substantial impact on revenues for businesses. A gateway is an integral component of a payment system through which every transaction is routed. In an online payment system, payment processors integrate with these gateways by means of various configurations such as pricing, methods, risk checks, etc. These configurations are called terminals. Each gateway can have multiple terminals associated with it. Routing a payment transaction through the best terminal is crucial to increase the probability of a payment transaction being successful. Machine learning (ML) and AI techniques can be used to accurately predict the best terminals based on their previous performance and various payment-related attributes. We have devised a pipeline consisting of static and dynamic modules. The static module does the initial filtering of the terminals using static rules and a logistic regression model that predicts gateway downtimes. Subsequently, the dynamic module computes a lot of novel features based on success rate, payment attributes, time lag, etc. to model the terminal behaviour accurately. These features are updated using an adaptive time decay rate algorithm in real-time using a feedback loop and passed to a random forest classifier to predict the success probabilities for every terminal. This pipeline is currently in production at Razorpay routing millions of transactions through it in real-time and has given a 4-6\% improvement in success rate across all payment methods (credit card, debit card, UPI, net banking). This has made our payment system more resilient to performance drops, which has improved the user experience, instilled more trust in the merchants, and boosted the revenue of the business.

• The paper’s main contribution is an AI-powered routing system that uses logistic regression and random forest to dynamically select the best-performing payment gateway.
• The methodology leverages both historical and real-time data to improve transaction success rates by 4-6%, enhancing overall payment reliability.
• The study highlights potential future enhancements, including advanced ML models and reinforcement learning, to further optimize digital payment processes.

Enhancing Payment System Efficiency through AI-powered Smart Routing

Introduction

The field of digital payments is witnessing an unprecedented scale of growth, fueled by the global push towards digitization. Amidst this growth, the efficiency of payment processing systems has become a focal point for businesses, given its direct impact on revenue. Recognizing the critical role of payment gateways in the transaction process, a comprehensive paper by a team from Razorpay introduces a novel AI-powered smart routing solution aimed at optimizing payment success rates.

The Core Problem

The foundation of the paper lies in addressing the inherent unpredictability of payment gateway performance. Gateways may suffer from downtimes or performance degradation due to a variety of reasons, leading to transaction failures. Traditional methods of routing payment transactions often do not account for these dynamic changes, relying instead on static rules or configurations. The challenge, therefore, is to dynamically predict the most suitable gateway for processing a payment, thereby maximizing the probability of successful transactions.

The Proposed Solution

The Razorpay team's solution involves the development of a smart routing algorithm that dynamically selects the best-performing terminal for any given transaction. This selection is based on a comprehensive analysis of historical and real-time data concerning gateway performance, transaction attributes, and various other factors. The proposed system consists of two main components:

• Static Module: This module applies merchant-specific rules and employs logistic regression to predict gateway downtimes, thereby pre-filtering terminals that are likely to underperform.
• Dynamic Module: Leveraging a Random Forest classifier, this module predicts the success probability of each terminal in real-time. It computes a range of features, including success rate, payment attributes, and time, which are continually updated via a feedback loop to reflect the most current terminal behavior accurately.

Implementation and Results

Implemented in a production environment at Razorpay, the smart routing system processes millions of transactions, demonstrating a 4-6% improvement in success rates across various payment methods. This improvement is not trivial; it translates into a significant increase in successful transactions, enhancing the user experience, reinforcing trust among merchants, and ultimately boosting revenue.

Implications and Future Directions

The practical implications of such a system are vast. By making payment systems more resilient to gateway performance fluctuations, businesses can ensure higher transaction success rates, which is critical in the competitive e-commerce landscape. On a theoretical level, this paper contributes to the ongoing discussion on the application of ML and AI in optimizing digital payment processes.

Looking ahead, there are several avenues for further research. Exploring more complex ML models, such as sequence models, could potentially uncover deeper insights into terminal performance patterns. Additionally, integrating reinforcement learning into the feedback mechanism might offer a more nuanced understanding of dynamic system behavior, leading to even higher success rates.

Conclusion

In an era where digital transactions are becoming the norm, the efficiency of payment gateways is paramount. The smart routing system developed by the Razorpay team represents a significant step forward in addressing this challenge. By intelligently routing transactions to the best-performing terminals in real-time, the system not only enhances transaction success rates but also opens new frontiers in the application of AI and ML within the financial technology sector. As digital payment ecosystems continue to evolve, such innovations will be crucial in ensuring their scalability, reliability, and efficiency.