Predictive payments with Buy Now Pay Later solutions

Buy Now Pay Later providers can use machine learning and data analytics to reduce the risk of credit loss and generate customer loyalty, according to Ricardo Campos from ITSCREDIT

Guest contributor | 02 August 2023

Consumers demand solutions and services that are convenient, practical and offer good value for money, particularly when it comes to making payments. Buy Now Pay Later (BNPL) is a popular payment method for both in-store and online retailers because it allows customers to purchase products and pay for them gradually over time according to a personalised plan. With little to no interest rates and no fees unless they are failing to make payments on time, customers are increasingly choosing to make BNPL payments to retailers rather than using credit cards.

It’s not just customers that are benefiting from BNPL schemes, though. Retailers and merchants adopting BNPL schemes are also gaining from higher average sale sizes, increased conversion rates, a greater number of customers and more loyalty and engagement, all of which leads to more sales. Due to this, more retailers are choosing to work with BNPL providers. For example, Motley Fool’s 2020-2022 US Market report found that 67 per cent of BNPL users would opt to replace their credit cards with BNPL solutions, with 30 per cent of them trusting BNPL businesses more than credit card providers and banks for fair business practices.

To become the trusted BNPL provider of choice for retailers, providers should use a comprehensive model validation toolkit to identify risk drivers and predict credit defaults from customers. They can do this by implementing an early warning signs process that proactively identifies higher risk clients and engages with them to help boost their credit scores. Providers will be able to use data analysis to support the action that they choose to take on customers that are developing high levels of debt.

The successful BNPL providers will be the ones that can address regulation requirements, mitigate credit losses and adapt their business models to a new interest rates environment all at the same time. They will also be able to use data collected through machine learning tools to identify and reward customers that are regularly meeting payment requirements, which will in turn increase customer loyalty.

Ricardo Campos is CEO of ITSCREDIT

This article was originally published in the Summer 2023 issue of Technology Record. To get future issues delivered directly to your inbox, sign up for a free subscription