Predictive Modeling of Mortgage Refinancing

ACCELERATING AUTOMATION & AI

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© 2018 Accelirate, Inc., All Rights Reserved.CASE STUDY

Predictive Modeling of Mortgage Refinancing

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Summary

The client is a mortgage investment firm that currently has over $25 billion in assets under management, along with loan servicing and origination affiliates. After starting to benefit from the RPA solutions that Accelirate developed for them, the client began to ask what Accelirate could do to help improve processes that fell just beyond the scope of simple automation. Among many options was the task of improving the efficiency of their marketing campaigns, specifically their mortgage refinance campaigns. It was quickly determined that this was an area that our team of world class Machine Learning Engineers could add value.

ScenarioEvery month this lender considers a large group of people with mortgages to potentially be the recipients of refinance offers. Previously, hand-written rules were manually created by subject matter experts to reduce this large pool of potential recipients to a smaller number of borrowers that would then be sent offers. Of the thousands of offers they send monthly, typically, less than 10% of the recipients subsequently initiate a loan application to refinance with the client.

Project Objective The task was to create an application that automatically and accurately identifies a small subset of individuals that are more likely to refinance within the overall population of potential recipients; reducing the need for manual effort on the part of the client.

Business GoalWe wanted to help the client spend their marketing budget more effectively by focusing their marketing efforts on a smaller number of mortgage borrowers, which are the most likely to refinance their mortgages. This leads to both more loans for the client and reduces marketing wasted on borrowers that are the least likely to be interested in refinancing.

Solutions ApproachThe approach to developing a solution for this problem follows the general development cycle of a predictive analytics machine learning application. Those steps can be summarized as:

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Accelirate Machine Learning Methodology

Problem Identification

Business Understanding

Data Understanding

Data Preparation Model Training

Model Optimization

Model Deployment and Maintenance

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Problem Identification

One major reason some companies are so reluctant to explore machine learning (ML) opportunities within their organization is the inherent difficulty in understanding and identifying what problems are suitable for ML algorithms to solve. Our data scientists work with our clients to help explain what machine learning is as well as what it can and cannot do, via walking through examples of some common use cases. We then discuss some of the biggest challenges the client is facing and together select a handful of processes to further evaluate as candidates for machine learning projects. Each process gets assessed according to its applicability to ML as well its technical feasibility. The best then gets explored further in terms of the underlying business of

the process.

Business Understanding and Problem Definition

To build a Machine Learning application in any domain, it is critical to understand the underlying business model which the application will improve. In this case, our data scientists had discussions with the client’s subject matter experts to understand the nature of refinance marketing. This problem was then defined as a classification problem where borrowers who respond to an offer to refinance belong to one class and those who do not belong to the other class. We then identified key metrics such as precision and recall that would be important in evaluating the overall performance of the model. Then an anticipated ROI is calculated and an expected timeline with deliverables is compiled into a project proposal for the client.

© 2018 Accelirate, Inc., All Rights Reserved.

Predictive Modeling of Mortgage Refinancing

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Data Understanding

Once our engineers have access to the client’s data, that is when the real “magic” begins. In this step, our data scientists aggregated all the data the client had on the mortgage borrowers and supplemented this with additional external data such as monthly federal interest rates. We then conducted an exploratory data analysis (EDA) to understand what macro level patterns and trends existed among the variables being considered. This enabled us to identify and create additional features to use during the modeling phase of the project. The results of this analysis were then formally written up into a report for the client, so in addition to receiving a solution to the problem at hand, the client learned more about their data in the process.

Data Preparation

Perhaps the most important part of the development for a machine learning solution, and the step that takes a substantial amount of time, is wrangling the data into a format that the machine learning algorithms can use to build models. This step establishes the quality of the underlying data on which the models are built, that ultimately determines the overall performance of the final model. The results of this step are the training, validating, and testing sets used to build the models.

Model Training

In this case, close to a dozen different classification algorithms were tested to on their ability to classify a person as likely or unlikely to respond to a refinance offer. These models used a pool of both numerical and categorical variables such as “outstanding loan balance” and “borrower state” to create their predictions. Feature selection also occurred during this step, meaning the best subset of variables were chosen based on how important they are in predicting whether someone is likely to refinance. A small number of the best performing models in this step were then further explored in the subsequent step.

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Model Optimization

Each machine learning algorithm itself has parameters that govern the way it trains the model, these are called hyperparameters. The best models found up to this point were both individually and collectively optimized via the consideration of a range of hyperparameters that correspond to each model. Different combinations (Ensembles) of different classifiers were tested to see if the overall power and stability of the predictions could be improved. Finally, the best model was chosen and the results of its predictions on the most recent client data was produced and explained in a final report.

Model Deployment and Maintenance

Once the best model is selected, it is then delivered and deployed for the client. This can happen in different ways depending on the client’s individual preferences. Usually, it is incorporated as a step during the process which is being automated with RPA software. If it is a standalone project, an application can be created, either desktop or web-based, which performs the given tasks, and if desired, can be automated. Wouldn’t it be great to just receive an email with the output of the model every month without any human effort?

© 2018 Accelirate, Inc., All Rights Reserved.

Predictive Modeling of Mortgage Refinancing

Solution Results

The solution to the low response rate for the refinance offers was a machine learning application which the client can use monthly to identify borrowers that are most likely to refinance. This allows them to perform targeted marketing, rather than just blindly mass marketing to all mortgage loan borrowers. The model assigns a rank to every borrower according to the individual borrower’s probability to respond to an offer in the mail and allows the client to prioritize their marketing based on this rank. Based on this model, over the past six months, the client would have been able to send over 300,000 less offers while increasing the number of respondents by 10% each month.

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