How Machine Learning is Transforming the Mortgage Lending Industry

Mortgages are important to both consumers and financial institutions — they’re often the largest loans that people take out in their lives and they account for the majority of outstanding consumer debt in the US. Unfortunately, the mortgage lending process is very complicated — it involves multiple parties and many separate documents, and it’s governed by a broad set of regulations. As a result, mortgage lending can be extremely tedious, time consuming and frustrating for both consumers and financial institutions. However, advanced technologies such as machine learning and predictive analytics are changing this by streamlining workflows, reducing required human intervention and automating critical decisions.

This post discusses a few critical ways machine learning is transforming mortgage lending to benefit both consumers and lenders. If you’re unfamiliar with machine learning, take a look at this post for an introduction.

1. Improved Application Verification

Machine learning can greatly help with verification to the benefit of both lenders and applicants. For example, lenders can use historical data to train predictive models that accurately estimate the applicant’s income, based on a wide variety of potential factors. This estimate can then be compared to the applicant’s stated income level to help confirm that it was reasonable and accurate. This type of automated assessment can accelerate income verification while also flagging high-risk cases for manual review, and machine learning can be used as either a decision-making tool or a decision-support tool (depending on the specific situation).

2. Application Queue Prioritization

The mortgage lending process involves back and forth between the lender and the borrower, and loan officers need to contact applicants frequently to communicate next steps and move the process forward. Historically, this has involved manual processing applications one-by-one in a simple queue, which can result in sub-optimal allocation of the loan officer’s time.

As lenders gather data on how consumers move through the process, respond to reach outs, and take actions, they can train machine learning models that optimize the loan officer’s workflow and recommend their “next best action”. This lets loan officers work through their queues dynamically (based on the opportunities that are most in need of attention), reduces the time loan officers spend deciding what to do next and creates more consistent processing across the organization.

3. Better Document Validation

Loan officers have historically had to do this work themselves, but now machine learning models can be trained to do it for them. Models can automatically identify text within documents and compare it to ensure that the forms have been filled out correctly, letting the loan officers focus on more important tasks. When issues are found, the exception is flagged for human review and a signal is sent to the loan officer to manually resolve the problem.

4. Underwriting & Loan Pricing

Machine learning is most effective when many data points inform a given decision, and mortgage lenders generally have a large amount of data on the applicant at the time of underwriting. This data typically comes from a credit report on the consumer from a major credit bureau, such as Transunion, Experian or Equifax, as well as property valuation information from a range of real estate data providers. Machine learning can effectively discover relationships within this information that can more accurately assess credit risk than traditional rules-based underwriting.

Conclusion

Originally published at digifi.io on September 21, 2018.

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