There are many mortgage decisions that can use business rules or business rules and analytics in combination. Here are some examples.
Business rules have been used to implement underwriting for sub-prime products - often in conjunction with analytics. For instance, one such decision engine qualifies a loan for multiple products based on a seller’s guide or underwriting guideline consisting of about 5000 rules and a simple Fraud scorecard. It can accept a 1003 and credit information and can provide pre-approval or approval or referral decisions. These implementations also involve ability to associate stipulations/conditions in both numeric code and text. In addition, for each loan processed, it has the ability to report all associated rule failures and should supply the area that caused the failure and the data that would be required for a pass.
Business rules can be used to implement Pre-qualification decisions based on a smaller set of application data. These implementation’s enable Pre-Qualifications on a flow or a bulk basis. Only high-level underwriting guidelines are applied in general.
Business rules can be used to implement compliance rules of all kinds.
Business rules can be used to implement Pricing. Pricing is determined by a set of decisioning variables such as credit grade, LTV, doc-type, etc., Pricing decision also involved rate adjustments based on certain application data such as LTV, doc-type, etc.
Business rules, in the form of Blaze Advisor, have recently been selected by a large service provider with a large servicing market share to re-architect their servicing platform. First implementations are targeted for a 1Q2005 completion.
- Best execution decisions combining multiple individual underwriting decisions
An implementation of Blaze Advisor business rules has recently been started to automate secondary marketing functions including investor underwriting, pricing and bid pool creation based on best-execution.
- Credit analysis
As part of the underwriting implementation, credit information including attributes can be passed to a business rules decision engine to establish credit grade. Credit grade decision is based on credit analysis of credit attributes such as Minimum FICO, mortgage lates, foreclosure, bankruptcy, credit at application, consumer lates, debt/income, and streamlined rates. Again this combines business rules and analytics.