5 areas in the payments value chain where machine learning is adding significant value

71
machine learning
machine learning

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.

There are several other areas in the payments value chain where machine learning is adding significant value:

  1. Product sales: Machine learning can be a powerful tool for developing deeper insights about customers and sales prospects because it can draw upon a wider variety of internal and external data than marketers have traditionally used. It can more accurately cluster customers and prospects into segments according to their profiles and probable needs. This deeper insight can reveal new opportunities for cross-selling and up-selling among both customers and prospects. McKinsey finds that with machine learning payments providers can increase revenue from existing customers by 10 to 15 percent.
  2. Customer retention: Companies typically monitor and forecast customer churn based on changes in account status; when churn rates rise they take steps to address the problem. Now, through machine learning, they can identify those customers they are at risk of losing and act quickly to retain valuable customers. For example, 47Lining, an Amazon Web Services partner, uses a combination of site behavior, demographics, and media-sentiment measures to predict customer churn with 71 percent accuracy. Companies using machine learning to address customer churn have achieved reductions of as much as 25 percent.
  3. Collections: Collection practices and debt restructuring work best when closely aligned with borrowers’ changing circumstances and propensity to pay. Machine learning can help companies build robust dynamic models that are better able to segment delinquent borrowers, and even identify self-cure customers (that is, customers that proactively take action to improve their standing). This enables them to better tailor their collection strategies and improve their on-time payment rates. TrueAccord’s HeartBeat, for instance, is a machine learning tool that helps lenders customize personal interactions in real time, based on its ability to detect why a customer’s payments are late. Companies using machine learning have been able to reduce their bad debt provision by 35 to 40 percent.
  4. Treasury pricing: In commercial payments, companies can capture 10 to 15 percent more revenue through optimized treasury pricing. In the near term, advanced analytics can identify quick-win opportunities to reduce price leakage (such as discounts exceeding authorized limits) and billing errors. Over the long term, clustering techniques built on machine learning can significantly improve customer segmentation and lead to more appropriate pricing models.
  5. Customer care: Over time, McKinsey expects to see a gradual increase in the automation of many customer services. This is an area in which the cognitive intelligence capabilities of machine learning are particularly well suited. Among the benefits are: lower servicing costs, enhanced agent performance, more efficient capacity management, improved digital customer experience, reduced risk, and elimination of waiting times. A variety of relevant applications are already available, including virtual assistants that use natural language processing, deep insight tools like IBM’s Watson, and cognitive engines that can do things presently handled by humans, such as IPSoft’s Amelia, which can understand and interact with people