Balancing risk and opportunity with intelligent payments

Two of the major forces at play in financial services and commerce are risk and friction. The push to reduce friction in shopping experiences has resulted in increasing fraud rates, which conversely increases pressure to apply stricter authentication steps. As financial institutions (FIs) and merchants are aware the result is both desirable and undesirable. The steps to reduce fraud risk directly hit merchant revenues by creating sometimes unnecessary friction for the shopper. We can maximize the upside and minimize the downside of this dynamic through an ecosystem of interconnected products and services that optimize these inherent trade-offs.

This ecosystem, known as intelligent payments, hinges on the ability to leverage large volumes of data and modern advances in technology optimized on an ongoing basis.

Financial services and commerce are revolting!

That’s right, there’s a revolution going on in financial services and commerce, and the target of the revolt is friction. Supermarkets, department stores and high-street fashion retailers etc., all pay huge attention to creating environments and shopping experiences that convert the shopper through to making a purchase; in e-commerce A/B-testing of online user experiences to optimize conversion by reducing friction has spawned an entire industry around web-analytics; challenger banks and new financial services start-ups are winning increasing market share by creating lower friction ways for consumers to save, invest, trade, take loans and manage finances. Measuring and reducing friction then, is clearly an important aspect to enabling commerce and financial services organizations. Two other important factors are risk – risk that the loan might default or that the purchase be fraudulent, for example – and cost.

The e-trinity: conversion, risk and cost

It’s very easy to eliminate all fraud, just block all sales. Likewise, it would be possible to eliminate fraud, by asking each shopper on every transaction to perform an in-person ID-verification at a local police station with at least two forms of photo ID and a recent bank statement sent to their home address, but you might not convert many customers. It’s possible to convert all your loan applicants to approval, but your risk of default will increase. You well know the inherent trade-off between these concepts of conversion, risk and cost and intelligent payments technologies is all about managing these trade-offs, ultimately to maximize Return on Investment.

One of the practical challenges that organizations face in trying to optimize cost, risk and conversion is that the problem is often sub-divided. For example, an organization’s marketing spend is often optimized for maximizing page hits, without measuring how many of those page hits converted to a sale, or how many of those sales turned out to be fraudulent. Imagine an advertising campaign that specifically drew in fraudsters to help drive sales. By sheer numbers, this campaign would be considered a success, unless its impact on fraud outcomes was also tracked. Ultimately, we want to optimize conversion, risk and cost across the entire funnel – acquisition, conversion to sale, successful payment, not-fraudulent - rather than each step in the funnel independently.

Machine learning in intelligent payments

FIS is a very data rich organization, and this data affords a unique perspective into global and regional consumer/shopper spending behaviors, across all sectors and commerce verticals. The insights we generate from this unique perspective power our products and services. Machine learning is a key part of our toolset for intelligent payments, because it allows us to continually learn and adapt from:

• Historical data to better understand how to process new transactions -- for example, our FraudSight product learns from all the fraud transactions we see across all networks, issuers and merchants, and is therefore able to very accurately flag high risk transactions, and ultimately convert a higher proportion of good transactions)
• Changes in the behaviors of issuers, such as risk appetite and policy implementations
• Changes in the behaviors of merchants, such as adding new product lines
• Changes in the behaviors of shoppers, such as increased spending capacity

End-to-end, holistic optimization

If there’s one message to take away from this article, it’s that truly optimizing ROI for our clients means thinking holistically across the entire payments and banking ecosystem and optimizing the various stages of the shopper and payment lifecycle as an integral part of the end-to-end conversion funnel. The convergence of both advancing technologies and new market problems continues to drive broad holistic solutions across FIS, such as the new Ethos™ data ecosystem.