Through digital transformation, payments companies have dramatically accelerated the speed of online transactions. With this positive evolution, however, comes inevitable challenges. For instance, the data volumes payments companies need to process are rapidly and continually expanding, which complicates ensuring smooth operations for customers and users – not to mention satisfying compliance requirements with various regulatory bodies. Increasing the number of employees on Operations and Finance teams can help mitigate payments failures and optimize payments behavior, however the exaggerated overhead can quickly become untenable.
IT and application monitoring tools can serve as an invaluable resource for establishing and maintaining smooth payments operations, but with so many monitoring tools available, many payments companies struggle with sprawl and insufficient resources to adequately maintain their systems.
In an attempt to cope with the slew of false positives produced by different monitoring tools, companies often find themselves saddled with a massive, expensive network operations center (NOC), and constant alerts transform working conditions into noisy, unfocused, fragmented and siloed environments. What’s more, too often payments companies are forced to conduct their monitoring efforts retroactively. With a lack of real-time, actionable insights, many monitoring tools end up doing little to promote efficient and accurate payments processes.
These challenges are common and understandable, however the unfortunate reality remains: Each and every time a payment fails, payments companies lose revenue and possibly customers, too. With transaction volumes continuing to explode, now more than ever organizations can’t afford any processing errors. Payments companies also can’t afford a lack of comprehensive business-level visibility and control, as it results in longer mean time to recovery (MTTR) for customer experience, greater customer churn and a variety of revenue-related issues — all of which can lead to bad press and damaged brand reputation.
Additionally, when monitoring systems are incapable of processing critical payment transactions quickly and scalably enough for today’s realities, payments companies run the risk of failing to comply with service-level agreements and any federal/government regulations, which can lead to financial penalties and/or lawsuits.
A leader in digital payments experienced business-level visibility challenges firsthand
Digital payments company Payoneer experienced some of these processing and business-level visibility challenges firsthand. A global payment and commerce-enabling platform that powers growth for millions of small businesses, marketplaces and enterprises, including eBay, Amazon, Google and Walmart, Payoneer delivers a suite of services that include cross-border payments, working capital, tax solutions, risk management and payment orchestration for merchants. With more than five million customers worldwide, the company monitors millions of business and technical metrics to keep their payment gateway running smoothly.
Initially, Payoneer relied on traditional monitoring and log analysis solutions. However this manual, multi-system approach led to siloed monitoring, and a cumbersome and incomplete view of business processes. The burden fell on production services to configure alerting (as opposed to individual teams), incidents took at least 24 hours to resolve and high false positive rates drained resources. Overall, the company’s existing, resource-intensive process to integrate new data sources to maintain business-level visibility simply wasn’t sustainable.
User-friendly AI enabled increased visibility 3X and improved time to resolution by 90%
Payoneer quickly recognized the need for a new approach and began using AI to automate their business monitoring. With AI technology, Payoneer was able to integrate their business monitoring and data platform, enabling them to substantially improve how they leveraged their existing data to find and remediate issues that otherwise would have been missed. Manual monitoring and inefficient internal systems that had long overextended IT and Operations teams were replaced with a turn-key platform capable of autonomous monitoring and real-time anomaly detection. By providing access to cross-silo visibility, Payoneer’s AI implementation also allowed multiple teams across the company to work from one, cohesive monitoring platform and instantly identify incidents most applicable to them.
Most importantly, Payoneer was able to efficiently overcome its visibility challenges by choosing to automate their business monitoring with user-friendly AI technology that anyone in the company could use — not just engineers or data scientists. By leveraging accessible automation to monitor all service logs, detect any errors and false positives, and accurately identify root causes, more teams were able to take ownership of payments optimization and confidently maintain accountability. Operations and Finance teams, in particular, were able to use AI to efficiently handle reconciliation and boost payments approval rates, which ultimately contributed to greater success for the business through improved customer satisfaction and revenue loss prevention. To date, Payoneer’s user-friendly AI implementation has increased the company’s visibility by 3X and improved their time to resolution by 90%.
Industry relevance and success requires autonomous business monitoring
To manage monitoring system sprawl and gain real-time, actionable, business-level visibility, today’s leading payment companies need to incorporate integrated and accessible AI technology, i.e., AI that’s business-focused and intuitive for all employees, not just IT teams. By moving away from inefficient, manual monitoring, the speed of digital payment processes can be accelerated even further, transactional issues can be found and fixed as they occur, and OpEx can be streamlined.
With fewer disparate tools to manually maintain, payments companies can also gain the opportunity to free up valuable resources, refocus team capacity on innovation and improve their competitive market position. Furthermore, by embracing autonomous business monitoring, payment companies can eliminate unproductive work cultures with little confidence or accountability, improve customer experiences, and boost lifetime customer value and overall relationships.