Applying Machine Learning to Streamline Mortgage Decisions

Big data, machine learning, artificial intelligence.  You have heard the words, you know this must be valuable, but how do you translate these solutions into measurable business strategies?

Predicting the Future By Looking At The Past

This is one of the many questions we work through with our clients on a daily basis.  As a quick refresher, predictive analytics is a branch of advanced analytics that is used to make predictions about future events typically based on a statistically significant number of past examples.

Most of us experience the effect of predictive analytics when it comes to fraud alerts on our credit cards or “suggested” items when shopping online. At the heart of predictive analytics is the ability to leverage historical data to determine the propensity for a future event to occur.  Predictive models have the ability to use data and machine learning to identify things such as fraud, loan default, a new purchase or a new home loan.

There are Many Applications for This Technology in Mortgage

Mortgage lending and servicing are complex.  There are a multitude of data points, participants, and stakeholders in every transaction.  With so much data and so much complexity, the experts at PointPredictive know that machine learning is a perfect match to help improve the ways things have historically been done.

We can apply this technology to origination, servicing and capital markets in a multitude of ways.  From sub-second loan preapproval by originators, all the way to portfolio retention by servicers, predictive models are forever changing the way that our industry will make decisions on borrowers, products, sales, and marketing.

Applying Machine Learning to A Non-QM World

One of the many opportunities we see today for predictive models is origination risk.  As the non-QM and nonprime lending environments build, one very effective way that lenders will be able to scale is by leveraging machine learning solutions.

Machine learning solutions can score and provide a customized risk assessment on each application in less than a second.  Armed with that information, lenders can instantly apply treatments to both their high and low-risk applications within their existing origination workflow processes.    For high-risk scores, they can focus their limited resources to reduce risk.  For low-risk scores, they can streamline decisions to automatically approve those loans with less customer friction.

Moving from Man to Machine to Streamline Risk Decisions

Today’s manual loan file review process that uses “stare and compare”, and database validation tools, greatly increases origination costs (more people), misrepresentation risk (hidden) and time to close (manual process).    Large teams of underwriting and risk analysts view loans manually in the attempt to reduce risk.  They can be effective, but the process is slow and creates unnecessary friction for borrowers and customers.

In contrast, Machine Learning can replace or augment much of this process very easily.  Where a processor or underwriter would make one-to-one comparisons of data points, a machine learning model captures the relationship among hundreds or thousands of factors.

PointPredictive is Powering Smarter, Faster Decisions

It is an exciting “big data” time for our industry.  As we strive for a true digital mortgage – faster closings and lower cost to originate – predictive models will be an essential solution for market dominance.   PointPredictive is thrilled to be leading the charge towards the use of machine learning to enable smarter and faster decisions.