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By: Tracy Bremmer Score migration has always been a topic of interest among financial institutions. I can remember doing score migration analyses as a consultant at Experian for some of the top financial institutions as far back as 2004, prior to the economic meltdown. Lenders were interested in knowing if I could approve a certain number of people above a particular cut-off, and how many of them will be below that cutoff within five or more years. Or conversely, of all the people I’ve rejected because they were below my cut-off, how many of them would have qualified a year later or maybe even qualified the following month. We’ve done some research recently to gain a better understanding of the impact of score migration, given the economic downturn. What we found was that in aggregate, there is not a ton of change going on. Because as consumers move up or down in their score, the overall average shift tends to be minimal. However, when we’ve tracked this on a quarterly basis into score bands or even at a consumer level, the shift is more meaningful. The general trend is that the VantageScore® credit score “A” band, or best scorers, has been shrinking over time, while the VantageScore® credit score “D” & “F” bands, lower scorers, has grown over time. For instance, in 2010 Q4, the amount of consumers in VantageScore® credit score A was the lowest it has been in the past three years. Conversely, the number of consumers falling into the VantageScore® credit score “D” & “F” bands are the highest they have been during that same time period. This constant shift in credit scores, driven by changes in a consumer’s credit file, can impact risk levels beyond the initial point of applicant approval. For this reason, we recommend updating and refreshing scores on a very regular basis, along with regular scorecard monitoring, to ensure that risk propensity and the offering continue to be appropriately aligned with one another.

About a month ago, Senior Decisioning Consultant Krista Santucci and I gave a presentation at Experian’s 2011 Vision Conference on Decisioning as a Service. Due to the positive feedback we received, I thought it might be of interest to members of the communications industry who might not have had the opportunity to attend. A common malady The presentation revolved around a case study of an Experian client. Like many communications industry companies, this client had multiple acquisition systems in place to process consumer and commercial applications. In addition, many of the processes to mitigate fraud and support Red Flag compliance were handled manually. These issues increased both complexity and cost, and limited the client’s ability to holistically manage its customer base. The road to recovery At the beginning of the presentation, we provided a handout that listed the top ten critical functionalities for decisioning platforms. After a thorough review of the client’s system, it was clear that they had none of the ten functionalities. Three main requirements for the new decisioning platform were identified: A single system to support their application processes (integration) A minimum of 90% automatic decisions for all applications (waterfall) The ability to integrate into various data sources and not be resource intensive on their IT department (data access) Decisioning as a ServiceSM is a custom integrated solution that is easily applied to any type of business and can be implemented to either augment or completely overhaul an organization’s current decisioning platforms. We designed this client’s solution with a single interface that manages both consumer and commercial transactions, and supports a variety of access channels and treatment strategies. Following implementation, the client immediately benefited from: Streamlined account opening processes A reduction in manual processes Decreased demand on IT resources The ability to make better, more consistent decisions at a lower cost The agility to quickly respond to changing market needs and regulatory challenges Evaluate your own business Do you recognize some of your own challenges in this post? Download our checklist of the top ten critical functionalities for decisioning platforms and evaluate your own system. As you go through the list, think about what benefits you would derive by having access to each of the capabilities. And if you’d like to learn more about Decisioning as a Service, please complete our form.

By: Kennis Wong Data is the very core of fraud detection. We are constantly seeking new and mining existing data sources that give us more insights into consumers’ fraud and identity theft risk. Here is a way to categorize the various data sources. Account level – When organizations detect fraud, naturally they leverage the data in-house. This type of data is usually from the individual account activities such as transactions, payments, locations or types of purchases, etc. For example, if there’s a purchase $5000 at a dry cleaner, the transaction itself is suspicious enough to raise a red flag. Customer level – Most of the times we want to see a bigger picture than only at the account level. If the customer also has other accounts with the organization, we want to see the status of those accounts as well. It’s not only important from a fraud detection perspective, but it’s also important from a customer relationship management perspective. Consumer level – As Experian Decision Analytics’ clients can attest, sometimes it’s not sufficient to look only at the data within an organization but also to look at all the financial relationships of the consumer. For example, in the situation of bust out fraud or first-party fraud, if you only look at the individual account, it wouldn’t be clear whether a consumer has truly committed the fraud. But when you look at the behavior of all the financial relationships, then the picture becomes clear. Identity level – Fraud detection can go into the identity level. What I mean is that we can tie a consumer’s individual identity elements with those of other consumers to discover hidden inconsistencies and relationships. For example, we can observe the use of the same SSN across different applications and see if the phones or addresses are the same. In the account management environment, when detecting existing account fraud or account takeover, this level of linkage is very useful as more data becomes available after the account is open. Loading…


