AI Has Come To Mortgage Lending

Artificial Intelligence is coming!  The use of artificial intelligence is beginning to make its mark across most, if not all, industries.  A current TV ad shows a worker in the Carlsbad Beer company explaining how they develop new products using artificial intelligence.  More and more we are seeing and hearing about how it is impacting our lives.  It is foolish to deny that it will have a huge impact on the mortgage industry or that this technology will have significant impact on the overall operations of an organization, whether it is origination, servicing or secondary.  


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The advancement of artificial intelligence technology opens new opportunities for lenders.  Most common discussions include the reduction in costs, coming primarily from the elimination of staff positions that will no longer be needed to complete repetitive tasks. Among these positions loan officers, processors and closers are most frequently mentioned.  Yet backroom operations seem to be a perfect fit for this type of technology.  For example, Narrow AI is focused on a single task reflecting work that is a comparison of documents delivered after the disbursement of funds.  Setting up loans for delivery to a servicing system and filing and payment of insurance are also single focused tasks that an AI program can easily accomplish once trained.  


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Today’s focus on providing exemplary customer service can be aided by the use of properly trained bots. These programs will be able to handle more questions and explain more of the product features than ever before. This will give loan officers, processors and closers the opportunity to spend less time on answering these standard questions and will allow these individuals to focus on the more complex issues facing their applicants.  


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One of the best suited for the type of activity that is best served by AI is the post close review processes and back-office reviews such as the quality control function.  Currently agency requirements include a pre-funding review and a post-closing review.   Most lenders have implemented two separate functions to meet these obligations.  In addition, because of the current emphasis on regulatory requirements, lenders have also implemented a third group of individuals who review only the documents associated with these requirements. AI technology can be trained through machine learning to conduct these reviews.  Not only does this reduce the headcount and general overhead costs, the reviews could be conducted on 100% of the loans.  While there will be variances that require human review at first, as the technology becomes adept at recognizing these variances and how they are resolved, the issues will also be addressed by the technology.  More importantly, as the volume of loans processed by this technology grows and the actions taken to resolve the issues become repetitive, AI can learn how to handle more and more of these processes. 


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Servicing also has significant opportunities with the introduction of AI technology.  Most of the positions involved in the administration of payment processing, escrows and related activities are typically uniform and repetitive.  These are prime opportunities for this technology to be embedded.  The technology can be taught to handle these functions and when unrecognized variations occur, the issue and the related loan function can be given to a human to resolve.  

Default management will undoubtedly be one of the biggest beneficiaries of AI. This is not only due to handling of the functions associated with these activities, but with the opportunities of identifying potential defaults long before an actual default occurs. Using “deep leaning” AI, the neural networks can incorporate more and more external databases into its analysis which will increase the probability that warning signs of default will become familiar and reliable.  Imagine the system using not only information on the borrower’s credit and property value changes, but economic data, news related to job growth and company movements and historic performance of similar loans, to analyze if a borrower will continue to pay.  

Another area that holds great promise for servicing is mortgage insurance claims.  Anyone who has worked in servicing is aware of the difficulty of getting claims paid, especially FHA loans.  This technology can be taught the allowable amounts reimbursable and prepare claim forms.  Based on any rejections received, the technology will adjust going forward until very few, if any, are rejected.  Furthermore, it can adapt the payment rates for the property preservation and marketing specialists involved in the process.  

The secondary market will also be the recipient of the benefits of AI.  With the depth of information used in RMBS pools, AI will be able to provide analyses that reflect the true risk of performance rather than the quasi-accurate data used today.  It is conceivable that as AI matures, lenders will be able to “score” each individual loan and thus have the ability to provide individualized pricing. 

Despite the benefits envisioned by this technology, there are risks.  Some of these are specific to the mortgage industry while others are expected to have a profound effect on the economy as a whole.  According to many futurists and technology developers, 40 % of all companies that exist today will not survive the adaptation of artificial intelligence.  There is nothing at this point that tells us whether or not they will be large or small, local or world-wide.  Much depends on how rapidly the technology advances and which of these companies have a management team that leads the changes or instead resists this technology to the detriment of their organizations.   Ultimately as the technology matures there is the potential for massive economic and job market disruptions.  

What then are the most pervasive risks that the industry faces?  The most critical is Data.  Data is the primary fuel that powers this technology and is the most critical factor in the success or failure of AI.  The more data available to “teach” the programs, the stronger and more meaningful the results.  For example, in developing “Watson”, the trainers feed the program over one million books which contained all types of information.  With the massive number of neural networks created by this data it provided the information that allowed Watson to beat the two Jeopardy champions.  

While there are millions of mortgages with corresponding data in existence today, lenders have been notoriously hesitant to share any of the data associated with these loans, especially the servicing data.  In addition, the data found in origination systems is also critical along with the quality control data which identifies the variances found in loans and can relate these findings to loan performance.  Unfortunately, QC data has had the least structure associated with it and as a result, there are have different definitions and input across the industry.  

Without the ability to combine these massive amounts of data while ensuring its accuracy, AI results are less than reliable.  While MISMO has done a stellar job of defining data fields, they cannot control what is input into those fields in every system.  In addition, some of the most critical data is found not in the data fields, but in free form notes and comments.  While AI has the ability to review and classify unstructured data, the ability of the industry to collect and validate these extremely large amounts of data is a huge risk and will require a massive industry wide effort to have what is necessary to support AI programs.  

Another risk facing lenders is the inherent bias unintentionally built into the programs by those who are teaching it to learn.  For example, if the program trainer taught the pronoun “her” when referencing a nurse, an application for a male nurse would most likely be rejected by the program. This type of unintentional bias can exist in numerous programs and would not necessarily be discovered until it was identified by those individuals impacted by it.  

The use of AI programs would ultimately change the operations processes and the jobs associated with each piece of the operation.  While on the surface, this may appear to be a non-issue.  Human based jobs associated with the development and use of these programs focus mostly on functions that are a hybrid of technology and human knowledge.  The need for experts in all areas of the organization will be necessary to complete the programming of these programs is critical.  Yet, since the introduction of automated underwriting systems, we have seen a drastic decline in the number of specialists in each area decline. Underwriters who have spent years learning what an underwriter needs to know are rare these days.  It is more likely to have “underwriters” who are simply inputting data and taking the output from an AUS system in making a final decision, without understanding what is needed or corrected. 

Government regulations, that have put a virtual stranglehold on the industry and its operations will most likely be adapted to this new work environment.  While machines can be taught to produce disclosures and documentation, their necessity will need to be examined more thoroughly.  The risk of course is that any old regulations will be replaced with new ones, regardless of the industry.  For example, property values that are developed through on-site inspections and evaluations may be entirely replaced by deep learning programs and huge databases which are currently being developed by companies such as Zillow and Google.  

Artificial intelligence will not impact our industry, or any other industry for that matter, in isolation. The changes brought about by the advancement of this technology will impact everyone.  We have already begun to see the shift of employees away from working in offices every day, to one where many people now work from home, wherever home may be.  We see more interactive work experiences achieved through technology rather having to actually go to another office in another state or even country.  

We are also more likely to see new types of organizations or combinations of work types that have never before been considered.  Capital One has introduced the idea of “Capital One Cafes” where banking and coffee needs are serviced in one spot and I’m sure it is not the last, or only one that will occur.  AI analytics can identify where “life events” intersect and provide opportunities that will change what we need, what we expect and the timing of its receipt.  

Based on “deep learning” patterns and relationships found in lending and servicing data, it is most likely that the credit culture prevalent in the industry today will shift from a front-end focus to becoming servicing results driven.  Because of the ability to identify patterns of loan attributes, economic data, demographics and even global issues, the actual performance of loans can be translated into the ability of companies to identify the most advantageous risk profiles.  These profiles can then be incorporated into the decision tools used for evaluating applications.  

With this type of technology and the ability to predict performance, will the delivery of loans to the secondary market change?  There is a distinct possibility that lenders of all size and scope, will be able to negotiate individualized product sales, thereby eliminating the need for secondary market intermediaries.    

A study by the board of Governors of the Federal Reserve found the pace and ubiquity of AI innovation to be much greater than expected for the financial services industries. They also found many positives in the use of AI as well as the risks.  Whether you have begun to work with artificial intelligence tools or not, they will have an immense impact on mortgage lenders.  

The issues discussed here are mainly focused on the broader problems and opportunities facing us in the near future.  What we cannot ignore however, is the issues surrounding how our work is to be done. In the final article of the series, I will take a look at the potential processes and job functions that are the changes job functions will incur and the skills and knowledge necessary for management and employees. 

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