The overarching ‘business driver’ in adopting a risk-based authentication strategy, particularly one that is founded in analytics and proven scores, is the predictive ‘lift’ associated with using scoring in place of a more binary rule set. While basic identity element verification checks, such as name, address, Social Security number, date-of-birth, and phone number are important identity proofing treatments, when viewed in isolation, they are not nearly as effective in predicting actual fraud risk. In other words, the presence of positive verification across multiple identity elements does not, alone, provide sufficient predictive value in determining fraud risk. Positive verification of identity elements may be achieved in customer access requests that are, in fact, fraudulent. Conversely, negative identity element verification results may be associated with both ‘true’ or ‘good’ customers as well as fraudulent ones. In other words, these false positive and false negative conditions lead to a lack of predictive value and confidence as well as inefficient and unnecessary referral and out-sort volumes. The most predictive authentication and fraud models are those that incorporate multiple data assets spanning traditionally used customer information categories such as public records and demographic data, but also utilize, when possible, credit history attributes, and historic application and inquiry records. A risk-based fraud detection system allows institutions to make customer relationship and transactional decisions based not on a handful of rules or conditions in isolation, but on a holistic view of a customer’s identity and predicted likelihood of associated identity theft, application fraud, or other fraud risk. To implement efficient and appropriate risk-based authentication procedures, the incorporation of comprehensive and broadly categorized data assets must be combined with targeted analytics and consistent decisioning policies to achieve a measurably effective balance between fraud detection and positive identity proofing results. The inherent value of a risk-based approach to authentication lies in the ability to strike such a balance not only in a current environment, but as that environment shifts as do its underlying forces.

By: Kristan Frend As if business owners need one more thing to worry about — according to the Javelin Strategy & Research’s 2010 Identity Fraud Survey Report, respondents who defined themselves as “self-employed” or “small business owners” were one-and-a-half times more likely to be victims of identity theft. Intuitively this makes sense- business owners exposure would be higher than the average consumer as their information is viewed more often due to the broad array of business service needs. Also consider the fact that until recently, multiple states had public records containing proprietors social security numbers as tax identification numbers readily accessible on-line. What a perfect storm this has all created! Javelin’s report also explained that while the average fraud incidence for business owners was lower than the average consumers, small business owner’s consumer costs were higher.  In other words the small business owner suffered more out of pocket costs for identity theft losses than the average consumer. Experts believe this is due to the fact that commercial accounts often do not receive the same fraud guarantee protections that consumer accounts are afforded. While compliance regulations such as Red Flags Rules will enhance consumer safety, institutions must further develop their prevention and protection methods beyond what is legally required to sufficiently protect their small business customers from future fraud attacks. Small business owner fraud and the challenges organizations face in identifying and mitigating these losses are frequently overlooked and overshadowed by consumer fraud. Simply put, fraud is prevented because fraud is detected- verifying that the business owners is who they say they are using multiple data sources is critical to identifying applicant irregularities and protecting small business owners. A well-executed fraud strategy is more than just good business – it helps reduce small business customer acquisition costs and ultimately allows you to make better business decisions, creating a mutually beneficial relationship between your organization and the small business owner.  

With the news from the Federal Reserve that joblessness is not declining, and in fact is growing, a number of consumers are going to face newly difficult times and be further challenged to meet their credit obligations. Thinking about how this might impact the already struggling mortgage market, I’ve been considering what the impact of joblessness is on the incidence of strategic default and the resulting risk management issues for lenders. Using the definitions from our previous studies on strategic default, I think it’s quite clear that increased joblessness will definitely increase the number of ‘cash-flow managers’ and ‘distressed borrowers’, as newly jobless consumers face reduced income and struggle to pay their bills. But, will a loss of income also mean that people become more likely to strategically default? By definition, the answer is no – a strategic defaulter has the capacity to pay, but chooses not to, mostly due to their equity position in the home. But, I can’t help but consider a consumer who is 20% underwater, but making payments when employed, deciding that the same 20% that used to be acceptable to bear, is now illogical and will simply choose to stop payment? Although only a short-term fix, since they can use far less of their savings by simply ceasing to pay their mortgage, this would free up significant cash (or savings) for paying car loans, credit cards, college loans, etc; and yet, this practice would maintain the profile of a strategic defaulter. While it’s impossible to predict the true impact of joblessness, I would submit that beyond assessing credit risk, lenders need to consider that the definition of strategic default may contain a number of unique, and certainly evolving consumer risk segments. __________________________ http://money.cnn.com/2010/08/19/news/economy/initial_claims/index.htm