From Fraud Prevention to Business Intelligence – How Risk Data Improves Retention

The teams that work on fraud and customer retention often work with the same customer at the same time. This could be when the customer is signing up, logging in, paying or asking for help.

When things go wrong, customers rarely describe it as fraud. They say they don’t trust the brand any more.

Online payment fraud is also a tax on growth. One small business resource estimates that merchants lost $44.3 billion globally to online payment fraud in 2024, with losses expected to reach $107 billion by 2029.

Why Risk Decisions Shape Lifetime Value

Every time a transaction is blocked, it’s a choice that involves risk. But every time a transaction is blocked without a good reason, it’s a choice that affects the customer’s experience. When a customer tries to do the right thing, a false decline can feel like a public “no”.

A survey of shopkeepers by Visa Acceptance Solutions and Verifi shows that insults from customers are still very common. It says that 14% of merchants think that the number of false positives is above 10%, while many think it is between 2.01% and 5%. Those percentages show the number of people who either leave without paying or come back to pay later.

That’s the retention connection. If getting a new customer costs 5–25 times more than keeping an existing one, it is rarely worth it in the long run to have rules that are too strict.

When Fraud Data Becomes Business Intelligence

Fraud data is not just a list of bad actors. It shows where users are having problems and where small problems can have a big effect on the product’s success.

If you use a fraud prevention solution well, it will do more than just protect you – it will also help you to measure behaviour, how reliable payments are and how safe accounts are. The best thing to do is to treat risk events like other products and analyse them in the same way you analyse onboarding, drop-off or subscription churn.

Your risk logs are hiding three retention wins.

1. Make it easier for “good users” without making it more likely that they will lose money. Start by measuring declines that later turn out to be real. If you can safely move some of that traffic from “hard decline” to “step up” verification, you will protect conversion and stop customers from leaving angry. A PYMNTS report says that 47% of retailers say that false declines have a big effect on customer satisfaction.
2. There is a spot churn that looks like payment issues. Not all churn is due to a problem with the product. If there are a lot of soft declines or if there are a lot of CVV retries or failed top-ups, it often means that the account will be cancelled before the user has had the chance to click unsubscribe. Risk data helps you to provide clearer payment messaging, alternative methods or support outreach.
3. Spot the signs of abuse that put off people who are using the site fairly. If people misuse the first-party system and ask for refunds too often, this can lead to higher prices, stricter policies and slower support for everyone. 62% of shopkeepers say that first-party misuse has risen by at least 5% over the past year, according to Checkout.com’s report on merchant research. Reducing abuse is about more than just saving money. It’s also about making sure that customers who you want to keep are treated fairly.

This change is important because it affects how loyal customers are. Sift says that 76% of consumers would stop shopping on a site if there was payment fraud and 80% would stop after their account was taken over.

Risk Metrics that Connect to Retention

It’s easier to turn risk data into business intelligence when teams share a small set of joint metrics. The table below shows how to turn fraud signals into ways to keep customers.

If your account is taken over, it will cost a lot of money. Equifax looked at information about merchant data and found that ATO chargeback losses were 76% higher than usual, at $576 per incident compared to $271. This kind of gap is why preventing takeovers is good for keeping customers: it means fewer losses and fewer customers who feel unsafe.

A Practical Workflow for Retention Focused Risk Data

If you want risk data to improve retention, not just reduce fraud, the workflow has to include product and support. Here is a simple model teams can use without creating a new department.

Make sure users are connected across different sessions by linking them based on their device, payment method and behaviour. Also, keep track of these connections.

To understand a friction ladder, you need to know what to do at each risk level. You can allow things at the first level or allow things but keep an eye on them at the second level. At the third level, you need to step in and review things manually. At the fourth level, you need to block things.

Finally, make sure you close the loop by feeding back confirmed chargebacks, confirmed good orders and support resolutions so that the system learns what legit looks like.

Every week, check the retention risk panel and include:

• Declines that aren’t real;
• Attempts to take over;
• Abuse of refunds;
• The most difficult points for each group.

Try to fix one small problem every week. This could be as easy as changing a message on the checkout page, tightening a promotion rule or improving recovery after lockouts.

The Retention Upside

The job of preventing fraud is often about reducing losses, but the most important thing is to keep people’s trust. If customers can log in easily, pay without problems and get back on track quickly if there’s any suspicious activity, they’ll stick around and complain less.

The information about risk is already in your systems. The companies that keep more customers are usually the ones that treat that data as something valuable – not just a security record.