Ranking mortgage origination applications using customer, product, environment and workflow attributes

Abstract

In this paper, we analyze the performance of an end-to-end Mortgage Origination (MO) process. The process begins with the submission of a mortgage application by an applicant to a lender and ends with one of the following outcomes: closing, i.e., loan approved by the lender and accepted by the applicant or non-closing, i.e., loan either rejected by the lender, or approved by the lender and not accepted by the applicant. Ranking mortgage applications by their predicted likelihood of closing at various steps in the process is useful for process efficiency and identification of actionable insights to convert applications likely to non-close into those that are likely to close. To build models for ranking applications at any step of the MO process, we take into account customer and product specific attributes of the applications as well as environment attributes and the history of the applications or workflow. The large state-space of the workflow makes the ranking problem challenging. We propose two workflow attributes, each with a state-space of dimension one, based on the number of visits to any step and a particular step (re-work) respectively. We find that incorporating these workflow attributes into the density modeling technique that we develop results in improvement of 4.8 percent in Average Precision over models that only incorporate customer, product and environment attributes. The simple and scalable density modeling technique allows for easy identification of applications that are likely to non-close and consequent corrective action such as change in the attributes of the mortgage product being offered. Further, our results indicate that the model is comparable to Support Vector Machines and superior to Logistic Regression for ranking. © 2009 IEEE.