Measuring data performance

by Guest Contributor 2 min read January 20, 2010

Meat and potatoes
Data are the meat and potatoes of fraud detection. You can have the brightest and most capable statistical modeling team in the world. But if they have crappy data, they will build crappy models. Fraud prevention models, predictive scores, and decisioning strategies in general are only as good as the data upon which they are built.

How do you measure data performance?
If a key part of my fraud risk strategy deals with the ability to match a name with an address, for example, then I am going to be interested in overall coverage and match rate statistics. I will want to know basic metrics like how many records I have in my database with name and address populated. And how many addresses do I typically have for consumers? Just one, or many? I will want to know how often, on average, we are able to match a name with an address. It doesn’t do much good to tell you your name and address don’t match when, in reality, they do.

With any fraud product, I will definitely want to know how often we can locate the consumer in the first place. If you send me a name, address, and social security number, what is the likelihood that I will be able to find that particular consumer in my database? This process of finding a consumer based on certain input data (such as name and address) is called pinning. If you have incomplete or stale data, your pin rate will undoubtedly suffer. And my fraud tool isn’t much good if I don’t recognize many of the people you are sending me.

Data need to be fresh. Old and out-of-date information will hurt your strategies, often punishing good consumers. Let’s say I moved one year ago, but your address dataare two-years old, what are the chances that you are going to be able to match my name and address? Stale dataare yucky.

Quality Data = WIN
It is all too easy to focus on the more sexy aspects of fraud detection (such as predictive scoring, out of wallet questions, red flag rules, etc.) while ignoring the foundation upon which all of these strategies are built.

Related Posts

The Email Address as Your Most Powerful Identity Signal

The why behind Experian's acquisition of AtData What happens when a comprehensive email intelligence database joins a global leader in data, analytics and fraud prevention? The acquisition of AtData adds 25+ years of building a complete view of email as an identity signal. Financial institutions can recognize, engage and protect customers unlocking a new standard for the way their teams work and the customer experience. That's what Experian's acquisition of AtData delivers. How we got here Not all email addresses tell the same story. Some are newly created. Some exhibit bot-like patterns. Some are inconsistent with every other signal you have about that person. Imagine a real customer. You have a job. You shop online. You have a primary email from your employer, a personal Gmail you've used for 15 years, and an old Yahoo address you still use for shopping because you've been using it since college. You're an engaged customer who interacts with brands, makes purchases and pays bills on time. But each system sees a different version of you. When you apply for credit, the lender sees one email. When you shop, the retailer sees another. When you sign up for a service, you might use the third. For financial institutions: You slow down the approval process to manually verify identity or approve applicants without the full picture. For retailers: You can't tell which version of "customer" is the most engaged, so you either over-mail or under-serve. For fraud systems: Sees a new account created under one email and flags it as suspicious because it doesn't have the history. This was the original problem AtData was built to solve in 1999. Twenty-five years later, that problem didn’t go away, it became more complex. Email fragmentation and device sharing are more common, and identity theft is more sophisticated. Capabilities that now work together Experian has built sophisticated identity and fraud solutions backed by consumer data resources and decades of expertise in credit and risk. AtData brought the ability to assess whether an email address is trustworthy, reachable and consistent—at scale, in real time. Experian is now making email intelligence foundational, not optional. This matters for: Fraud prevention and risk management: Distinguishing a returning customer from a new threat. Knowing whether an email is newly created, exhibiting bot-like patterns or inconsistent with other identities is crucial. Compliance: Building audit trails that can explain identity decisions. Email data history and behavioral signals create the documentation needed to defend your decisions. Credit: Verifying identity in a world where traditional signals are shifting. Email signals provide a persistent, durable identifier that confirms who someone actually is. Marketing: Reaching the right person across email, mail and digital channels. Email intelligence reveals which addresses are actively engaged and reachable. Research shows email remains one of the highest-ROI marketing channels outperforming paid search and social advertising1. The problem every marketer faces: You end up burning budget on addresses that bounce, are unmonitored or are associated with users who never open mail. For credit marketing specifically, email enables faster, more targeted delivery of firm offers across channels, something that's increasingly important in a post-cookie world. "Email is a persistent identifier in a fragmented world. It's what connects a person's postal address, phones, devices, behaviors—the full picture of who they are. By embedding that into our infrastructure, we're not just adding another data point. We're fundamentally improving how businesses understand who their customers are."- Ashley Knight, Senior Vice President, Financial Services and Data Why now? AI is reshaping how decisions are made in every industry. Models are getting faster, more automated and more embedded in core workflows. But AI is only as effective as the data behind it. Fragmented data + fast models = faster, larger-scale misclassifications. In an era of synthetic identities, AI agents, deepfakes and AI-generated activity, the value of durable, persistent, real-world data signals has increased dramatically. Deloitte’s Center for Financial Services projects that generative AI could drive fraud losses in the U.S. up to $40 billion by 2027, a 32% growth rate since 2023. And email sits at the center of it with business email compromise already being one of the most common and costly fraud types. People change phones, move homes and swap devices, but they often hold onto their email for years. That's the signal that protects your business, and the one we've built into the core of how we help you make decisions with confidence. View the press release here

August 6, 2026 by Zohreen Ismail
Building Financial Opportunity Through Purpose-Driven Partnership

Discover how the National Urban League and Experian partner to expand financial literacy and create economic opportunity.

August 6, 2026 by Scarlet Nickel
2026 U.S. Identity and Fraud Report 

Explore key findings and insights from our newly released 2026 U.S. Identity and Fraud Report. Read more now!

August 5, 2026 by Laura Burrows

Subscribe to our Newsletter

Enter your name and email for the latest updates.

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Subscribe to our Newsletter

Don't miss out on the latest industry trends and insights!
Subscribe