Machine learning has become the buzzword of 2018. It’s applications are far-reaching, though are just beginning to peer over the precipice of machine learning opportunities in payments. As companies, organizations, and governments undergo digital transformation, they are capturing more data than ever. Simultaneously, machine learning is becoming more accessible to these entities - and more affordable. The result is sure to be increased automation across all industries and sectors. In fact, McKinsey Global Institute posits that 47 percent of the US workforce will be automated by 2030.
We’ve already seen applications of machine learning in payments as it relates to transaction monitoring, where algorithms help facilitate near real-time authorization of purchase transactions.This is just the beginning of how high-powered computing can impact the payments space.
Big data has become ubiquitous, fueling advanced analytics and feeding the machine learning revolution. Advanced analytics are table stakes for any organization that wants to remain on the competitive forefront, and this extends into the payment processing side of business operations. One of the biggest use cases for machine learning is the ability to identify, track, and learn consumer behaviors and patterns. Machines can detect anomalies in identified patterns, either revising algorithms (if shifts occur on a massive scale), or alerting humans of potential issues (in the case of potential fraud). This inherent predictability facilitates streamlined —and more secure—payments for merchants across industries.
Preventing card-not-present/online fraud is one of the more popular applications of machine learning and big data. As internet bad actors become more sophisticated in using advancing technology to defraud companies, machine learning offers those companies an ability to fight back…faster. Machine learning has the ability to crunch big data faster than fraudsters can say “gotcha”, allowing companies to identify fraud attempts—and stop them—in near real-time. Older methods relied on analysts to review and dissect information via static rules-based systems, including hundreds of factors, in order to properly flag fraud attempts. The sheer force of machine learning facilitates data-crunching at infinitely faster speeds, and more accurately and inexpensively, to put a stop to fraud.
Companies have largely dealt with churn by addressing it after it’s too late; as customer defect at a higher rate, a solution is implemented to stop the bleed. Machine learning enables better forecasting of churn and, more importantly, faster action to prevent it. Machine learning can analyze a wide range of factors (site visits/behavior, geolocation, demographics) to forecast which customers are at risk to churn with great accuracy. Additionally, the integration of conversational commerce via voice and chatbots has improved omnichannel customer experience, increasing the overall customer satisfaction for brands. These “always-on” customer engagement channels replicate human-to-human conversation to automate the resolution of customer services issues and to securely process payments on customers’ preferred channels.
Payments companies, in particular, stand to benefit from the digital transformation supported by machine learning. An obvious evolution here is the reduction of human error and automation of business processes. This can reduce processing times, and aid in compliance. Looking again to big data, machine learning can provide payments companies with greater user insights, better predictive tools, and the ability to quickly produce financial reports from huge volumes of data. Automating some of the granular tasks and removing them from the human workforce results in great cost reductions and simplification of processes.
As is evident, machine learning relies on data for maximum efficacy. The same holds true when it comes to its application in payments security. As more devices become connected and as more channels become sales platforms, organizations need a way to track and analyze patterns and trends across media and devices to identify threats that extend beyond purchase fraud and into data breach territory. This requires the ability to quickly analyze near- and long-term trends from the past, in the present, and for the future. This means agile data modeling that is not hampered by existing static analyses. Data agnostic systems that can create new data models fluidly will be the way of the future in terms of identifying mass security threats. In this way, risk engines can evolve, rather than be broken down and rebuilt, to accurately make decisions based on information from multiple data sources with a variety of signals.
The future of payments looks bright when we take the massive scale of capabilities machine learning encompasses. Not only will we have better mechanisms to fight fraud, but we will have agile tools to better serve customers, improve internal business processes and operations, and more intelligence for security. In a constantly evolving landscape, the good guys are finally equipped to stay ahead of the bad guys, streamlining the payments experience for every player in the ecosystem.