Application risk management processes for deposits has remained relatively unchanged for decades. Typically, it involves credit bureau data and a secondary check of “debit bureau” data. A “debit bureau” typically gathers information regarding known fraud and compiles a fraud database of perpetrators. Every applicant who passes the credit risk strategies is checked against this database. The challenge is that this process can be very expensive.
Among a new class of fraud best practices is the idea of applying fraud models/fraud analytics as a filter upstream from the debit bureau’s fraud database. This practice enables deposit institutions to still identify known fraud and minimize fraud losses on those applicants that carry the highest risk. At the same time, costs are reduced by removing low risk accounts from the debit bureau check.
In addition to reducing costs, these revised acquisition strategies help reduce fraud referral rates while ensuring that application fraud does not increase.
As deposit institutions look for ways to significantly reduce costs without suffering additional application fraud, look for the continued emergence of fraud analytics among 2011’s fraud best practices.