With lending as one of the key drivers of today's eeonomy, lenders are faced with considerable opportunities-and risks. The Internet has speeded up the process-and expectations-of loan originators. Adding competitive pressures to be both fast and accurate, retail online originations for the top 20 reporting lenders more than doubled in one year, growing 126 percent overall.1
Yet, on the risk side of the equation, organized fraud and electronic "scams" are on the rise. Identity theft has been the number one national crime for three years running and continues to be the fastest growing crime in the United States. The Tower Group conservatively estimates that U.S. lenders lose more than $1 billion annually due to fraud perpetrated in conjunction with a stolen identity.
To this equation of growing demand and increased risk we must add the factor of a highly competitive market. To remain competitive in this volatile lending environment, lenders must be able to not only offer attractive rates, but also make decisions quickly. And those decisions must not only be rapid, they must be accurate.However, extending credit to the wrong customer can be worse than missing the opportunity to land the right customer.
While data may exist on which to make these decisions, it is often "siloed" and not easily available to the underwriter when needed. Multiple databases may yield valuable information, but how can this information be put together quickly and completely to support a decision that frequently must be made in real time? And what about situations in which data is sparse-as is often the case with small businesses, which may turn into profitable clients for the long-term but which may initially carry the most risk?
The fact is that no human, however brilliant, can come close to providing the intelligence and speed required for making this volume of decisions. Banks and lending institutions of all sizes are finding their solutions with sophisticated decisioning technology that helps them make the right decision, right away, with the right customers.
Real-time credit evaluations and the risk of application fraud has been a challenge for lending institutions for years. Technology that can identify risky credit applications and instantly access multiple sources of data to make decisions is now available for the loan origination process. This technology includes:
* Real-time application fraud detection and identity verification using multiple data sources helps to reduce losses for all types of fraud, including identity theft and potential "money laundering" activity.
* The ability to evaluate applications from multiple entry points-branches, laptops, the Internet, remote locations-at the customers' convenience.
* Flexibility in configuring business rules to meet changes in lending policies due to changes in the economy or management policies. The business rules used in the decisioning process can be configured to meet the business needs of the institution by the user, eliminating the need for using expensive and scarce IT support to make these changes.
* Enterprise-wide consistency and efficiency. These "smart" systems can be part of an enterprise-wide platform that decisions different types of loans, thus applying speed and accuracy to all origination decisions.
* Application risk models for credit risk evaluation tailored by credit product portfolio. These risk models combine multiple data sources, including the proven FICO(R) score, for more powerful decisioning. Precise risk assessment for specific portfolios is developed solely for credit origination.
What's inside the "black box" of these systems, and how do they work? As data is accepted from the credit grantor's web site or front end system, the software pulls a credit bureau report on the credit grantor's behalf from a the major consumer and business bureaus. If little or no credit information is available, "debit" bureau information is also obtained. …