June 5, 2012 - How important is it to measure a borrower's ability, or even willingness, to pay?

Considering that a search of "walk away from your mortgage" turns up tips for consumers on doing just that, making automated credit risk work better and faster is an evergreen must-have. The good news is there is a vast new world of social media and other unstructured data to help, but only if lenders can quickly adjust their credit risk systems to handle the new data and models.

That's where the hypercompetitive market of scoring providers comes in, each claiming faster development cycles and easier usage than the other. "My example is if Facebook had been around for 20 years, would we have been able to identify people who would have walked away from their mortgage?" says Rod Nelsestuen, a senior research director at TowerGroup. "As we account for the behavioral factors, banks have to figure out how to deliver better decision-making."

Credit scoring and business intelligence providers such as Experian, Equifax and SAS have all developed new systems that work across departments, are designed to deploy quickly, and include interfaces that are simple for end users on the sales and marketing side of the business to access and use. "It's not about the models or a raw set of information ... the institutions want to have users that don't have to understand [the complex scoring metrics]," Nelsestuen says.

Experian in early May showcased its new PowerCurve decision management suite at an internal conference in Scottsdale, Ariz. PowerCurve includes two software products - PowerCurve Strategy Management and PowerCurve Customer Management. Strategy Management is a credit risk product that lets staff design, test, execute and continuously update decision strategies by adding new data. Customer Management uses analysis for marketing purposes. It uses the same unstructured data that's used as part of credit scoring, such as social media analysis, along with internal transaction histories and purchase tendencies, such as the use of web and mobile payments, to drive marketing and sales. The product melds analysis of broader data sets with traditional CRM information such as life stage, demographics, and the breadth of financial relationships, to match the consumer's profile to cross-sell and up-sell marketing opportunities. These opportunities, such as special offers or pitches to additional financial products, can also be priced in line with the consumer's credit profile by using the broader data sets.

"What's changing is there is more data available, and our clients have to make decisions about how to put that data to work," says David Proctor, senior director of product management at Experian Decision Analytics. Proctor says usability and ease of deployment play a major role in making expanded credit risk techniques work.

In the traditional credit scoring expansion process, Proctor says that as new data gets put into models, lenders have to test how the new data impacts functions in different departments. He also says underwriting and other staff have to learn how to use the new information to make decisions on creditworthiness. Proctor says these cycles are usually 12 to 18 months, though Experian is hoping to compress that time with the new products. "It takes time to understand the new data and what it means, whether it's loan data, demographic information, or portfolio data. So with all of this new data being used as part of scoring, how do we make that [12 to 18 month] time frame smaller?"

Experian believes the path to a shorter time frame will come from deploying new scoring and modeling changes directly to the risk department. The risk departments will use Experian's software to update and change the underlying systems that run the desktop workstations at different departments in the financial institution, so an expanded customer profile that includes deeper credit scoring can be accessed quickly through an interface that staff are accustomed to using-rather than having to install a new scoring system for each department. "PowerCurve isn't designed to provide a tool for each department, but to make the IT job easier by helping them make changes by using software," Proctor says.

Experian's competitors include other scoring firms like Equifax and FICO, and business intelligence firms such as SAS. Daryl Toor, an Equifax spokesperson, says the firm has updated its systems to include a debt monitoring solution that notifies lenders of new loan applicant activity during the pre-funding periods. To speed information delivery, it's also established a partnership with Interthinx that enables lenders to access The Work Number's automated solution for income and employment verification, which provides digital access to capacity to pay. A FICO spokesperson said the firm was working on new credit scoring systems, but would not comment further. On the marketing side, FICO just acquired Entiera, which provides multi-channel campaign management delivered as software as a service. Entiera Insight combines structured and unstructured customer data, and enables marketers to design and execute large-scale marketing campaigns across multiple channels. It will be combined with products that use FICO's predictive analytics marketing tools.

There are also regulatory pressures that encourage faster updating of risk scoring models. David Wallace, global financial services marketing manager for SAS, says the increased level of regulatory mandates has driven banks' operational costs up significantly while at the same time revenue opportunities are challenged by the slow economic recovery.

SAS offers a series of products aimed at credit risk managers, such as SAS Credit Scoring for Banking and SAS Real Time Decision Manager, that ply emerging data analysis as part of credit decisioning. Scoring for Banking mines and organizes data, then uses that information to develop consumer scorecards and perform compliance with Basel II requirements for management of credit risks for pools of loans. Decision Manager uses analysis from the scoring product to aid in loan decisions, cross-selling, collections and decisions on the size of credit limits.

To further speed updates and expansion of data used in scoring, SAS's High Performance Analytics searches data sets that include billions of information items to locate correlations between different data sources that can be combined to accelerate the development of new predictive models.

Wallace says these models are tested on full data sets instead of samples, which enables broader test results when can also aid in speed to market. "Therefore, [these new] production models deliver increased lift which drives faster acquisition, lower attrition, and improved credit risk decisioning," he says.


More credit data is available, but the challenge is to update systems quickly to take advantage.

This story originally appeared in the June 2012 issue of Bank Technology News.

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