Machine learning in banking is certainly not new. However, amazing advances in the speed, volume, affordability, and sheer power of data processing capabilities are opening up exciting new use cases, including improving how banks assess creditworthiness, make lending decisions, and price loan contracts.
Banking and big data go hand in hand. And that is exactly why machine learning fits like a glove across many areas within financial services, especially credit risk modeling.
Machine learning’s advanced algorithms are able to sift through enormous amounts of structured and unstructured data to provide insights that enable better credit risk decisions, improve data monitoring, provide alerts on potential problems, detect fraud, and enable better forecasting with predictive analytics. Such information arms banks with active intelligence for credit risk management, as well as many other areas.
Many fintechs seized this opportunity early on, wasting no time embracing machine learning to invade such traditional banking turf as lending. A TransUnion study shows fintech lenders dramatic rise in market penetration in the personal loan space. In 2017, fintechs represented 32 percent of all personal loan balances, up from just 4 percent in 2012.
To keep pace, many large banks have followed suit, welcoming machine learning, albeit at a slightly more cautious pace than their nimble competitors. Artificial intelligence (AI) market research firm TechEmergence’s analysis of the seven leading commercial banks in the United States showed that most have invested in machine learning and some in significant sums. Their initiatives run the gamut from launching virtual chatbots to interacting with customers and employees to using machine learning algorithms to detect fraud and cyber breaches.
Machine learning clearly already plays an integral role in finance. And as the technology continues to advance, it is poised to accumulate many more valuable uses. Now is the time for mid-sized banks to jump on the machine learning bandwagon.
At KPMG, we think credit risk modeling is an area ripe for machine learning adoption and is therefore the first area where mid-market banks should focus their technology enablement efforts. Precise credit risk modeling depends on the complex analysis of tremendous volumes of data from a variety of sources. Using more traditional modeling techniques, this is tedious, time-consuming, and often prone to error. But it is the bread and butter of machine learning algorithms.
This paper will help mid-market banks embed machine learning algorithms into the internal processes involved in assessing credit default risk, pricing loans, and making credit decisions. Read on to find out how adopting machine learning can help banks drive increased accuracy, speed, and efficiency of lending decisions while reducing credit risk—and how to get started.