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Loan modifications – another use for credit attributes

A recent article in the Boston Globe talked about the lack of incentive for banks to perform wide-scale real estate loan modifications due to the lack of profitability for lenders in the current government-led program structure. The article cited a recent study by the Boston Federal Reserve that noted up to 45 percent of borrowers who receive loan modifications end up in arrears again afterwards. On the other hand, around 30 percent of borrowers cured without any external support from lenders – leading them to believe that the cost and effort required modifying delinquent loans is not a profitable or not required proposition. Adding to this, one of the study’s authors was quoted as saying “a lot of people you give assistance to would default either way or won’t default either way.” The problem that lenders face is that although they have the knowledge that certain borrowers are prone to re-default, or cure without much assistance – there has been little information available to distinguish these consumers from each other.  Segmenting these customers is the key to creating a profitable process for loan modifications, since identification of the consumer in advance will allow lenders to treat each borrower in the most efficient and profitable manner. In considering possible solutions, the opportunity exists to leverage the power of credit data, and credit attributes to create models that can profile the behaviors that lenders need to isolate. Although the rapid changes in the economy have left many lenders without a precedent behavior in which to model, the recent trend of consumers that re-default is beginning to provide lenders with correlated credit attributes to include in their models. Credit attributes were used in a recent study on strategic defaulters by the Experian-Oliver Wyman Market Intelligence Reports, and these attributes created defined segments that can assist lenders with implementing profitable loan modification policies and decisioning strategies.  

Published: Jan 06, 2010 by

Return on Investment definition

By definition, “Return on Investment” is simple: (The gain from an investment – The cost of the investment) _______________________________________________ The cost of the investment With such a simple definition, why do companies that develop fraud analytics and their customers have difficulty agreeing to move forward with new fraud models and tools?   I believe the answer lies in the definition of the factors that make up the ROI equation: “The gain from an investment”- When it comes to fraud, most vendors and customers want to focus on minimizing fraud losses.  But what happens when fraud losses are not large enough to drive change? To adopt new technology it’s necessary for the industry to expand its view of the “gain.”  One way to expand the “gain” is to identify other types of savings and opportunities that aren’t currently measured as fraud losses.  These include: Cost of other tools – Data returned by fraud tools can be used to resolve Red Flag compliance discrepancies and help fraud analysts manage high-risk accounts.  By making better use of this information, downstream costs can be avoided. Other types of “bad” organizations are beginning to look at the similarities among fraud and credit losses.  Rather than identifying a fraud trend and searching for a tool to address it, some industry leaders are taking a different approach — let the fraud tool identify the high-risk accounts, and then see what types of behavior exist in that population.  This approach helps organizations create the business case for constant improvement and also helps them validate the way in which they currently categorize losses. To increase cross sell opportunities – Focus on the “good” populations.  False positives aren’t just filtered out of the fraud review work flow, they are routed into other work flows where relationships can be expanded.    

Published: Jan 04, 2010 by

DDA and the risk of fraud in the retail bank, Part 2 – How is your fraud prevention affecting your customer experience?

By: Heather Grover In my previous entry, I covered how fraud prevention affected the operational side of new DDA account opening. To give a complete picture, we need to consider fraud best practices and their impact on the customer experience. As earlier mentioned, the branch continues to be a highly utilized channel and is the place for “customized service.” In addition, for retail banks that continue to be the consumer's first point of contact, fraud detection is paramount IF we should initiate a relationship with the consumer. Traditional thinking has been that DDA accounts are secured by deposits, so little risk management policy is applied. The reality is that the DDA account can be a fraud portal into the organization’s many products. Bank consolidations and lower application volumes are driving increased competition at the branch – increased demand exists to cross-sell consumers at the point of new account opening. As a result, banks are moving many fraud checks to the front end of the process: know your customer and Red Flag guideline checks are done sooner in the process in a consolidated and streamlined fashion. This is to minimize fraud losses and meet compliance in a single step, so that the process for new account holders are processed as quickly through the system as possible. Another recent trend is the streamlining of a two day batch fraud check process to provide account holders with an immediate and final decision. The casualty of a longer process could be a consumer who walks out of your branch with a checkbook in hand – only to be contacted the next day to tell that his/her account has been shut down. By addressing this process, not only will the customer experience be improved with  increased retention, but operational costs will also be reduced. Finally, relying on documentary evidence for ID verification can be viewed by some consumers as being onerous and lengthy. Use of knowledge based authentication can provide more robust authentication while giving assurance of the consumer’s identity. The key is to use a solution that can authenticate “thin file” consumers opening DDA accounts. This means your out of wallet questions need to rely on multiple data sources – not just credit. Interactive questions can give your account holders peace of mind that you are doing everything possible to protect their identity – which builds the customer relationship…and your brand.  

Published: Jan 04, 2010 by

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