Presentations | Fidelity National Information Services - formerly Applied Financial Technology

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PowerPoint Presentation
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FIS Applied Analytics Breakfast Seminar

Summary:
What is happening in the state-of-the-art prepayment modeling arena? Important trends in the home equity loans and HELOCs, reperforming, hybrids and subprime prepayment models will be discussed as well as an introduction to FIS Applied Analytics' Loan Level Prepayment Score.

The FIS Applied Analytics Prepayment Loan Score is an application that provides accurate insight on prepayment propensity to loans, pools, MBS and CMO deals based on loan level data (Loan Size, LTV, Geography, FICO Score, etc). This score translates into accurate relative values and greeks that create a significant arbitrage opportunity for the user. A Prepayment Score has a value between 30 and 80 basis points where a credit score is between 7 and 20 basis points. Michael Bykhovsky will illustrate how the Score from FIS Applied Analytics will considerably impact your risk-based pricing strategy.

PowerPoint Presentation
1.5mg - download

Prepayment Modeling

Summary:
In recent years, some new approaches have been developed in modeling the refinancing behavior of mortgage loans. What makes these new approaches work is the focus on modeling borrower behavior and the reasons for changes in that behavior, instead of focusing on the model's tenability within existing statistical machinery.

Statistical analysis generates both "signals" (the effect you're measuring) and "noise" (random events that have nothing to do with what you're measuring but may affect your measurement results). The idea behind statistical significance is that the information you want is obscured by other information you do not care about. By finding confidence levels, you are hoping to determine what effects from your data are significant and what are not. The problem with using statistical analysis on a large pool of loans is that every effect is a signal. There is no noise in your measurements. Any effect large enough to register is significant. When you analyze a large pool of loans and get noise or errors, it is reflective of mistakes in the statistical model.

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