An Auto Optimized Payment Service Requests Scheduling Algorithm via Data Analytics through Machine Learning

Abstract

Traditional customer payment service scheduling approaches cannot cope with the modern demand for timely, high-quality service due to the disruption of big data within small and medium-sized payment solution providers (SaMS-PSP). While many customers have access to modern technologies to lodge their service requests easily and fast, SaMS-PSPs do not have equally automated big data-driven capabilities to handle the growing demands of these service requests. To effectively improve SaMS-PSP’s customer payment service requests processing speeds, personnel optimization, throughput, and low latency scheduling, we have developed a new customer payment service request scheduling algorithm via matching request priority with the best personnel to handle the request based on data analytics through machine learning. Our experiments and testing have confirmed the merits of this new algorithm. We are also in the process of applying this new algorithm in real-world payment operations.