Collections organizations are literally "swimming in a "sea of contact related data and information". As such, firms everywhere are looking to more effectively use their contact data, gain better control of it, and optimize overall contact performance. In this session, you will gain insight as to where your organization may currently stand on the "Contact Data Continuum", and more importantly, the steps you may need to take in order to better manage and leverage your debtor contact data for greater collections performance.

Simple “dialing for dollars” payment-based collections is a thing of the past. Customers expect treatment options beyond just the government loan modification programs and will wait until these are offered to make payment. Nonetheless, low pull through and high re-default rates are plaguing both the Treasury Department’s Home Affordable Modification Program (HAMP) as well as all types of lender’s treatment efforts. All too many executives, politicians and reporters are quick to point fingers at the culprit, but identifying a sole reason for the limited success so far is not an easy target. Bridgeforce along with lenders/issuers will discuss how they are addressing these pull through and high re-default challenges by closing the communications/workflow technology gaps in the treatment process. In addition, the panel will review how these advanced technology solutions can also support the increased management and oversight that will be required as these newly treated populations continue to grow at a rapid pace.

In an economy where whole municipalities and entire industries are experiencing financial crises, it’s plain to see that there’s more to determining the expected loss of a particular loan than simply evaluating the three C's. In this session, mortgage lenders and servicers will learn how data quality can impact the bottom line profit or loss on each loan, and what they can do to bring more reliable predictors into the evaluation process. Panelists will discuss the consideration of factors such as future economic growth and employment projections have on credit risk, and explain how to leverage broader-based economic data to make better credit decisions.

…