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Getting the Complete View

Falling house prices combined with the changing conditions of borrowers and the deterioration of balance sheets have brought to light opportunities in the way risk - servicing, marketing and profitability - is viewed and addressed within the industry. One needs to look no further than the loan origination process for an example.

The origination unit gathers data on borrower income and credit history to predict borrower behavioral risk, appraise the collateral property to determine loss severity risk, incorporate all the data to model potential fraud risk and to ultimately make an underwriting decision. However, even though a great deal of valuable information is gathered during the origination process, not all data elements are transferred to servicing after the closing. Analytics in servicing tend to focus on trending borrower payment behavior and monitoring changes in property values to determine propensity to prepay and default risk. While valuable information such as borrower income would be useful to the analysis, this data - gathered during the origination process - is not readily available after the closing.

Information that is locked in one business unit - but causes information gaps in another - as well as valuable customer behavioral information resides in multiple platforms across the enterprise. This information must be accessed to enable a more comprehensive approach to serve the best interests of the customer, financial institution and potential investors as well as reduce future underwriting risks. Access to this vital data can be accomplished in a number of ways, but it is the critical foundation to implementing both servicing and origination strategies that can dramatically improve results.

Unfortunately, origination data is seldom looked at again once the loan is in the servicing phase. However, if data sharing occurs throughout the life cycle of the loan, a better understanding of the product itself, as well as the customer base, emerges. While many lenders are now deploying analytics at a higher level than ever before, it is still rare for them to have a complete view of the borrower across product lines and throughout the loan life cycle. This view could be used to better manage the current portfolio and plan for the future.

Today, new business opportunities still exist. For example, even though property values have declined nationwide, there are still customers who have equity in their homes and may benefit from a home-equity product. With proper analytics, a lender that understands not only the market but also their customers' needs will be best poised to find those pockets of opportunity.

Many lenders and servicers have started to understand the incredible power of deploying enterprise-wide data and analytics tools within both the origination and servicing segments. Larger financial institutions are already working to access customer data wherever it resides within their organization so it can be analyzed at the product level and the aggregate level. This enables the lender and the servicing organization to establish their relationship-based customer strategies.

Financial institutions are attempting to leverage this data and implement modeling that has traditionally been used to market and originate loans. For example, before the current crisis in the mortgage industry, there was little need to deploy aggressive data and analytics modeling in the home-equity servicing line of business because the risk of loss was much lower. However, when default volumes began their rapid climb, servicers started looking for answers and found them by taking a more in depth look at borrower behavior by utilizing powerful analytic tools to help them determine their loss mitigation strategies.

Still, financial institutions must make even more progress by enabling data access and analytical modeling across all product lines. This will take data and analytics collaboration to an entirely new level to enable new business strategies and grow market share.

Just as the data and analytics capabilities that were common to the mortgage and home equity origination lines of business are now being leveraged by servicers to address delinquencies and losses, an enterprise-wide view of customer behavior should also be leveraged within home equity to recommend product and pricing decisions for new business and cross sell initiatives.

The results of comprehensive modeling of customer performance - both at the product level and at the aggregate level - can also be directly linked to the types of new products that are appropriate for each segment of the customer population. With data from across the enterprise and their analytics capabilities, financial institutions will have information about the way customers perform across their banking relationship - and how profitability is impacted based on various scenarios.

For example, an institution that understands and has analyzed the different origination vintages, regional market data and demographic "slices" of their home-equity portfolio may be in a position to offer a product that will render a profit despite the current economic situation. Looking at the characteristics of a specific group of loans at origination and layering their performance while in servicing may also lead to revised underwriting guidelines for existing products. In addition, this process will give financial institutions tremendous confidence in the development of new products, pricing and packaging. If lenders know the specific customer characteristics and product types that perform the best, they can design new products with the features and customized options that will appeal to their target market. They can also design the products and pricing that are most appropriate for other customer segments along the risk spectrum.

Lenders and servicers can then use these analytics to help build the confidence of investors who want to be assured of the value of portfolios before they buy them. Given the current status of today's economic climate, building confidence has been difficult to accomplish since many of the industry's previous practices and assumptions proved to be unsustainable. Granular, comprehensive modeling of current customer behavior across various product lines and throughout the loan life cycle will prove to be a much better indicator of future consumer behavior and portfolio value.

This new focus on customer behavior and the sharing of data will position the industry to connect the dots and complete the cycle from originations to servicing and back to marketing and product profitability. As lenders enable an aggregate view of their customers, deploy the robust data that is available, and utilize predictive, behavioral and market-modeling capabilities, new opportunities will become available. These opportunities will bolster the industry's confidence by demonstrating the proven value of its strategic customer relationships and ultimately stimulate new growth.

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