Harnessing machine learning in payments
- January 22, 2016
- Posted by: PurpleFire
- Category: Technology
Opportunities to expand the use of machine learning in payments range from using Web-sourced data to more accurately predict borrower delinquency to using virtual assistants to improve customer service.
Machine learning is one of many tools in the advanced analytics toolbox, one with a long history in the worlds of academia and supercomputing. Recent developments, however, are opening the doors to its broad-scale applicability. Companies, institutions, and governments now capture vast amounts of data as consumer interactions and transactions increasingly go digital. At the same time, high-performance computing is becoming more affordable and widely accessible. Together, these factors are having a powerful impact on workforce automation. McKinsey Global Institute estimates that by 2030 47 percent of the US workforce will be automated.
Payments providers are already familiar with machine learning, primarily as it pertains to credit card transaction monitoring, where learning algorithms play important roles in near real-time authorization of transactions. Given today’s rapid growth of data capture and affordable high-performance computing, McKinsey sees many near- and long-term opportunities to expand the use of machine learning in payments. These include everything from using Web-sourced data to more accurately predict borrower delinquency to using virtual assistants to improve customer service performance
Machine learning: Major opportunities in payments
Rapid growth in the availability of big data and advanced analytics, including machine learning, will have a significant impact on virtually every part of the economy, including financial services (exhibit). Machine learning can be especially effective in cases involving large dynamic data sets, such as those that track consumer behavior. When behaviors change, it can detect subtle shifts in the underlying data, and then revise algorithms accordingly. Machine learning can even identify data anomalies and treat them as directed, thereby significantly improving predictability. These unique capabilities make it relevant for a broad range of payments applications.
Machine learning has already established a strong foothold in credit cards, particularly in fraud management. PayPal’s Braintree Auth payments tool, for example, uses PayPal’s consumer transaction data in conjunction with software developer Kount’s fraud detection capabilities to authorize high volumes of transactions and verifications in near real-time. Each credit card transaction or verification is analyzed in milliseconds using hundreds of fraud detection tests.