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Generic fraud score – best practice, Part 1

Published: October 9, 2009 by Guest Contributor

By: Kennis Wong

In this blog entry, we have repeatedly emphasized the importance of a risk-based approach when it comes to fraud detection. Scoring and analytics are essentially the heart of this approach.

However, unlike the rule-based approach, where users can easily understand the results, (i.e. was the S.S.N. reported deceased? Yes/No; Is the application address the same as the best address on the credit bureau? Yes/No), scores are generated in a black box where the reason for the eventual score is not always apparent even in a fraud database.

Hence more homework needs to be done when selecting and using a generic fraud score to make sure they satisfy your needs. Here are some basic questions you may want to ask yourself:

What do I want the score to predict?
This may seem like a very basic question, but it does warrant your consideration. Are you trying to detect these areas in your fraud database? First-party fraud, third-party fraud, bust out fraud, first payment default, never pay, or a combination of these? These questions are particularly important when you are validating a fraud model. For example, if you only have third-party fraud tagged in your test file, a bust out fraud model would not perform well. It would just be a waste of your time.

What data was used for model development?
Other important questions you may want to ask yourself include:  Was the score based on sub-prime credit card data, auto loan data, retail card data or another fraud database? It’s not a definite deal breaker if it was built with credit card data, but, if you have a retail card portfolio, it may still perform well for you. If the scores are too far off, though, you may not have good result. Moreover, you also want to understand the number of different portfolios used for model development. For example, if only one creditor’s data is used, then it may not have the general applicability to other portfolios.

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In today’s digital lending landscape, fraudsters are more sophisticated, coordinated, and relentless than ever. For companies like Terrace Finance — a specialty finance platform connecting over 5,000 merchants, consumers, and lenders — effectively staying ahead of these threats is a major competitive advantage. That is why Terrace Finance partnered with NeuroID, a part of Experian, to bring behavioral analytics into their fraud prevention strategy. It has given Terrace’s team a proactive, real-time defense that is transforming how they detect and respond to attacks — potentially stopping fraud before it ever reaches their lending partners. The challenge: Sophisticated fraud in a high-stakes ecosystem Terrace Finance operates in a complex environment, offering financing across a wide range of industries and credit profiles. With applications flowing in from countless channels, the risk of fraud is ever-present. A single fraudulent transaction can damage lender relationships or even cut off financing access for entire merchant groups. According to CEO Andy Hopkins, protecting its partners is a top priority for Terrace:“We know that each individual fraud attack can be very costly for merchants, and some merchants will get shut off from their lending partners because fraud was let through ... It is necessary in this business to keep fraud at a tolerable level, with the ultimate goal to eliminate it entirely.” Prior to NeuroID, Terrace was confident in its ability to validate submitted data. But with concerns about GenAI-powered fraud growing, including the threat of next-generation fraud bots, Terrace sought out a solution that could provide visibility into how data was being entered and detect risk before applications are submitted. The solution: Behavioral analytics from NeuroID via Experian After integrating NeuroID through Experian’s orchestration platform, Terrace gained access to real-time behavioral signals that detected fraud before data was even submitted. Just hours after Terrace turned NeuroID on, behavioral signals revealed a major attack in progress — NeuroID enabled Terrace to respond faster than ever and reduce risk immediately. “Going live was my most nerve-wracking day. We knew we would see data that we have never seen before and sure enough, we were right in the middle of an attack,” Hopkins said. “We thought the fraud was a little more generic and a little more spread out. What we found was much more coordinated activities, but this also meant we could bring more surgical solutions to the problem instead of broad strokes.” Terrace has seen significant results with NeuroID in place, including: Together, NeuroID and Experian enabled Terrace to build a layered, intelligent fraud defense that adapts in real time. A partnership built on innovation Terrace Finance’s success is a testament to what is  possible when forward-thinking companies partner with innovative technology providers. With Experian’s fraud analytics and NeuroID’s behavioral intelligence, they have built a fraud prevention strategy that is proactive, precise, and scalable. And they are not stopping there. Terrace is now working with Experian to explore additional tools and insights across the ecosystem, continuing to refine their fraud defenses and deliver the best possible experience for genuine users. “We use the analogy of a stream,” Hopkins explained. “Rocks block the flow, and as you remove them, it flows better. But that means smaller rocks are now exposed. We can repeat these improvements until the water flows smoothly.” Learn more about Terrace Finance and NeuroID Want more of the story? Read the full case study to explore how behavioral analytics provided immediate and long-term value to Terrace Finance’s innovative fraud prevention strategy. Read case study

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