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AI Implementation: Where Do We Go From Here?

The previous two articles published have attempted to raise awareness of what Artificial Intelligence (AI) is and isn’t as well as the associated risks and opportunities.    This third article describes how the implementation of these technologies can change the way we operate and how, from an operational perspective, using the tools and the benefits can expand business and reduce costs. 

Fluctuating and Growing

One fact that we have to acknowledge is that AI is in flux and will most likely continue that way for some time.  For example, a recent article I read claimed that RPA (robotic process automation) is dead. It doesn’t mean that this type of AI is no longer useful, but that when the individual types of AI such as expert systems, robotic automation for individual processes as well as the disintegration of siloed processes and programs is addressed, it will result in a comprehensive integrated program that utilizes the appropriate method for the task at hand.


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Another area that has come to the forefront of AI is what is known as IoT or the Internet of Things. In this instance, data on the internet can be incorporated into databases as needed, to complete tasks through the use of AI.  A step further in this approach is what is known as AIoT or artificial intelligence data of things.  In other words, if by using the internet to create data, that data then becomes available for others to use in their AI efforts we have effectively created an internet of AI “things”.   

Of course, this is not going to be the final product or program for the use of AI.  As we know from just listening to the news or reading articles on the technology, the future uses of AI are seen as unlimited. 

Roadblocks

Today the mortgage industry is trailing behind other industries in the use of AI in their workflow. While there are numerous reasons for this, one of the most basic is that organizations continue to run processes exactly as they were run 40 years ago.  There have been multitudes of new technology products that can be purchased and added to make the process cleaner with fewer errors, but the fundamental changes necessary to realize the intrinsic benefits of many have not been made.  


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Way back in 1979, loan applications were taken by Loan Officers and handed off to Processors, who put together the documentation that supported the application, including required regulatory disclosures, and handed it off to an Underwriter to evaluate for acceptability.  If approved the loan was handed off to a Closer, who made sure the title was acceptable and prepared the documents and funding.  Once the loan was closed, it was sent to Post-Closing, where it was reviewed, corrections made, documents sorted, MI filed, and the loan was then delivered to an investor. The technological support we have today was non-existent and management dealt with only people and processes. 

This linear process is still in place today although we now have multiple systems in these siloed functions to manage the data that was previously on paper. The process also has more documents and more people working on these tasks.  Despite these individual and in some cases co-joined systems, the process is still dependent on having people in place to connect the data and documentation.  Add to this the maintenance of system upkeep, the struggle to ensure the continuity of the data and process as well as the implementation of on-going updated systems and requirements and we get a view of the process  that can best be described as organized chaos and makes it near impossible to streamline the costs or effectively provide “quality” customer service.  

As a result of these Operational Risk issues (people, process & technology), the cost of producing a loan has now surged to around $8800, much of which involves manual reviews and rework.  Why?  A recent survey of the industry found that on average 62.5% or $5500 of the total $8800 is for personnel costs.  Another 11% or $1,000 covers general expenses and approximately the same amount is dedicated to secondary marketing expenses. Eight percent, or $704 is spent to support the technology used by the organization.  The remainder is spent on other various necessary expenditures.  In other words, we have not made any of the transformational transitions that have been promised by technology for years, just added more costs. This lack of vision and the resulting failure to redesign and manage effective change is definitely a roadblock to not only the use of AI, but the ability to run a profitable organization.


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Another roadblock we face is the inability to collect, comprehend and utilize data. Collectively data is the foundation of the products produced and the driver of the processes and people we employ.  The process begins with data collection and proceeds to add additional information throughout.  Yet the systems in use today do not allow us to utilize data from disparate programs to assist in the analytic process of acceptable loans and associated profitability. As I noted in an earlier article, in addition to each individual company’s data, the industry does not share any data.  This results in the inability to utilize comprehensive industry data to develop artificial intelligence.  While Fannie Mae frequently utilizes its data to develop “tools” for lenders, in reality it is only a very small piece of the total data set that could/should be used. Having a tool biased by the use of a segregated population of loans does not provide legitimate results to the total populations.  

Organizational culture can be a roadblock to success.  The culture of an organization actually holds back any company from improving their processes as management and staff adopt a “we have always done it this way” attitude. In fact, a survey of 590 G2000 leaders by HFS Research found that 51% of the highest performing enterprises see their cultures as holding them back in their technological transformation process.  From an Operational Risk perspective, the redesign of processes and corresponding people skills significantly lags technology implementation in organizations.  The only way to address this roadblock is by radically rethinking existing process which will ultimately drive the greatest benefits to the company.  The three pillars of operational performance (people, process, technology) no matter what form the tasks take are fundamental to the ability of the company to produce what has been promised. 

Mortgage Lending Redesign

A true transition of the lending process begins with understanding what AI technology can do. Once this awareness has taken place, a strong change management team needs to be identified and concepts, no matter how “off the wall” they seem, need to be identified.  This envisioning process must include not only how the process will be designed, but the necessary skill sets as well.  


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One of the most frequent questions asked when an AI discussion is held, is “Will I lose my job?” This fear of losing a job is one of the most pervasive concerns by the staff of all industries.  While skill sets will change, most individuals will continue to work in the same profession.  In other words, the tasks will change, but people will work. Recent studies have begun to reorganize common types of tasks performed and reorganized them into potential skill set requirements. 

One example is found in the book by Paul Daugherty and H. James Wilson entitled Human + Machine, Reimagining Work in the Age of AI, where tasks are divided into three groups. Those that involve human-only activity include leadership, creative activities and evaluative or judging skills. Those skills seen as primarily using the AI technology involve transactions, iterations, predictions and adaptations. However, there are a series of hybrid tasks that involve both human and technology efforts.  The human efforts within this hybrid set include communicating with applicants and borrowers to explain and educate them, much as we do now.  There will also be the need for individuals who train the technology.  For example, newer flexible robotic systems that work along with humans need to be trained to handle different tasks, just as we do now when machine learning is required.  There will also be those who sustain the processes and programs by incorporating the company’s risk profile, such as setting limits or allowable override decisions on profitability or legal and ethical compliance.  They do this by ensuring the quality of the data, flag errors and poor machine results, design interfaces for the AI expanded workforce.  

Within each of these groupings are a variety of tasks and corresponding skill sets, but one important factor is that the individuals fulfilling these jobs must understand the when, where and how of every action included in the lending and servicing processes.  Currently, the knowledge and skills necessary to work within the industry are siloed similar to the processes.  For example, underwriters today have a far different knowledge base than those who began working in the industry prior to automated systems.  More experienced underwriters understand why something is required, what the impact is if not and whether or not there are potential ways to address any problems.  That knowledge is quickly being lost.  In addition, because we have bifurcated the origination and servicing processes, few individuals in the production area can explain what and how servicing does and would not be able to explain why a payment was not processed.  For future work within the industry, employees must understand it all. 

Another area that is critical to lenders is regulatory issues.  In November,2018, Lael Brainard of the Board of Governors of the Federal Reserve System presented remarks on What Are We Learning about Artificial Intelligence in Financial Services.  In these remarks, he stated that “AI… is not immune from fair lending and other consumer protection risks…”  and alerted lenders to the challenges in the areas of opacity as well as the ability to explain how the system complies with these requirements.  Since this area is so critical to lenders it is important that there are individuals with the skill sets to provide this information if requested.   

The development and maintenance of credit policy is therefore critical to lenders, and in conjunction with affordable housing initiatives, artificial intelligence, through its data sets and analytics can provide more rationalization for lending parameters.  One chief executive of a financial services company recently stated that what artificial intelligence and machine learning allows is the ability to get much broader perspectives on consumers thanks to additional data, shedding light on their creditworthiness.  

Other processes in the business appear to be ripe for implementation of AI.  Two prominent ones that come to mind are quality control and post-closing.  Using expert systems and RPA, the post-closing review processes can be developed to scrub data, identify missing documents or those that need correction and notify the individual responsible.  Of course, staff will be needed to address those loans that fall outside the parameters of the review program. 

Today quality control consists of reviewing loans to identify errors in all facets of the loan process.  Using artificial intelligence tools such as expert systems, machine learning and electronic verifications ordered and controlled by the technology, QC could be done automatically throughout the origination process n 100% of the loans. Using collected data from other reviews, a more comprehensive analysis of the workflow can be conducted, and opportunities and risks identified.  Rather than the paper intensive manual process that we employ today, executives can have results as frequently as desired while significantly reducing or eliminating many of the costs included in the $8800 that is problematic today.  

The Bottom LineWhether or not the industry surges ahead with the adaption of artificial intelligence, it is coming.  For those lenders who eagerly take on the job of redesigning their people and processes in conjunction with its implementation, the opportunity to increase business while cutting excessive costs are unlimited. 

About The Author

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. 

About The Author

Is QC Now Officially Dead?

In 1995, with the advent of automated underwriting systems, I co-authored an article entitled “Is Quality Control Dead?” that appeared in the Mortgage Banking magazine. At that time there was a strong belief that QC was only used to find underwriting errors and with automation taking over the underwriting process there was no need to review these loans. At the time, the agencies, Fannie Mae, Freddie Mac and HUD must have been part of that trend because they made no changes to the existing antiquated QC programs they required for seller servicers.

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Unfortunately, as we all have learned to our great regret Quality Control was needed more than ever. From the turn of the century through 2007 lenders rode roughhouse over the underwriting requirements and triggered the greatest financial crisis since the Great Depression. Even as the QC Committee of the MBA meet with agencies, Congress members and consumer groups asking that they support stronger QC requirements, less and less attention was paid to QC. Despite the white papers developed showing the extent of fraud and documenting proof of what the lack of support for QC was conjuring up in the “magic elixir” that were the subprime ingredients of the collapse, QC was so weak there was no hope that the industry would listen. None of these warnings were heeded. Since then of course, QC has been revived and strengthened and the economists say we are fully recovered.

Yet once again we are hearing and reading about the latest and greatest mortgage program; the digital mortgage. According to the developers and purveyors of these programs, we have once again eliminated the need for Quality Control. These programs and their supporters claim that by using these programs have the capability of electronically validating the information entered by the consumer, running the data through an AUS and providing an approval within minutes. It is only if the loan cannot be electronically approved does it go to a loan officer to amend and approve.

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However, just as in the initiation of the automated underwriting systems, there are opportunities for mistakes inadvertent or otherwise. One of the biggest concerns is the inability to validate the information. While it sounds good, how many consumers are willing to give bank account information on-line, or those who work for small companies that don’t report the information to these on-line employment services. Sure, we can get tax returns, but they are at least a year old and not helpful in giving current income information. How is that validated?

Furthermore, despite the restrictions placed on lenders regarding DTI limitations, product and document types, non-QM loans are thriving. Just today I saw an advertisement for a “new” mortgage type, “No Income, No Employment”. There has also been a myriad of statements from the current political administration that the controls put in place to prevent another crisis will be loosened and/or eliminated in the near future.

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The question then is, can this alternative QC process that leaves numerous loopholes for bad loans to slip through and provides little incentive to do things right, stop another housing crisis. More than likely the answer is no, and because of that, it is likely that Quality Control really is dead. May it rest in peace because the rest of the industry surely won’t.

About The Author

Artificial Intelligence: The Pros And Cons

Here’s hoping you got exactly what you wanted for Christmas! You did want an Artificial Intelligent loan application, didn’t you? After all, based on what we saw at the convention in Denver and all the publicity since then, this is what most mortgage bankers had at the top of their list to Santa.

Without a doubt, the full force of this change is sweeping the industry. Whether it is called AI, Machine Learning, Digital Mortgages or has a unique name, lenders are looking for ways to add some means of streamlining the application process. However, not all of this is necessarily good news. In talking to numerous loan officers and technologists in the industry it appears we are headed for a consumer and secondary crisis of confusion. This potential confusion crisis has many different causes.

Industry members from all segments are asking if every automated option is really “artificial intelligence” and what it means to them. Some are concerned that the expense of developing such a system is far beyond their means. Overall however, they just are not sure what it is and what to do about it.

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The quick answer to that question is obviously dependent on what you want a machine to do. Today the term artificial intelligence is discussed by many in our industry as a single effort which will take in information and use it to make an underwriting decision. In other words, an “expert system”. What’s so hard about collecting data and sending it to an automated underwriting system? We already do that. But is that really AI?

In its most simplistic form “AI” is a machine with human cognition, but there is much more to it than that. Just as individuals are not born with the knowledge they have today, a machine must learn. This means learning facts and, while humans are capable of innately associating the facts with the problems, it must learn how to associate these facts appropriately with the problems it is trying to solve. How much we teach machines is dependent on what we want it to do as well as how much information we give it. Machines with the ability to think like humans not only have millions of facts but can use these facts in a “Neural Network”. In other words, they can reason based on what they know.

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Lesser types of AI include programs such as Expert Systems, Machine Learning or RPAs. While these programs are all based on the core expectation, their abilities are very different. For example, Expert Systems are a method of automated reasoning based on a very specific set of facts, rules and principles. Applying a user interface that asks a question, they will filter the data with the rules they have. If the rule is not there they cannot “reason” an answer.

Machine Learning development involves the construction of algorithms, which can learn and make predictions or decisions based on the data incorporated into the program. This type of AI is most often used in such areas as data mining since it uses statistical analysis to identify patterns. Using the data available it makes predictions about any new data it receives. Machine Learning algorithms include such statistical methods as regression analysis and decision trees. The education of this type of program is conducted by running large amounts of data through the model until it finds sufficient patterns to make an accurate decision.

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RPA or Robotic Process Automation mimics user activities and can process structured and semi-structured data in line with the rules embedded in the program. It is highly deterministic. In other words, based on the information received the output will be what the rules have defined. These programs are frequently being used expert systems and typically include a “human-assist” factor that deals with exceptions that do not meet the rules in place. However, one of the values of using RPA technology is that this program can “learn” or add rules based on the user actions. For example, if such a program is evaluating the completeness of a document, the rule may say that all fields must include data. In this instance, a document with an empty field would become an exception. If, however, when this occurs in a specific field it is labeled an exception and the human interface says it is “OK”, the machine will adapt the rules to allow a document with this empty field to be labeled acceptable.   This type of AI is used primarily with OCR technology and employed in operations, which currently employ a “stare and compare” process.

In developing any of these systems, it should be evident by now that the common and prevalent factor in any of these programs is the data. Types of data received by any organization include structured data, semi-structured data and unstructured data. A true AI program can utilize all three types of data whereas the lesser options are limited to structured data, and in some cases semi-structured data. Unstructured and semi-structured data is sometimes referred to as “dark data”.

Here is where the issues and risks begin. Everyone is familiar with the term GIGO or “garbage in; garbage out”. Without consistent and accurate data any “thinking” done by these programs is suspect. For years, the industry has struggled with developing a single source of data with a consistent definition and characters and still has failed to ensure that the data used is accurate. This however is not the only issue when it comes to data utilized in an AI effort. There are also a variety of other data issues. Among these are “noisy data”, “dirty data”, “sparse data” and “inadequate data”. These issues result in having conflicting or misleading data, missing or erroneous values, and incomplete data. While MISMO has done a tremendous job of structuring data definitions, programs being developed must have some type of pre-analytics that can be applied to raw data to ensure standardization, imputation and other basic techniques to ensure the quality of the data.

A second issue with data is its security and governance. Each entity must develop a method for ensuring that the data is managed properly. In other words, establish acceptable sources of data, determine how it will be analyzed for consistency and accuracy and identify who has control of the final data set so that it cannot be accessed by those without authorization. Data governance involves the management of this resource in a way that ensures its quality and use while maintaining the ability of the organization to capitalize on the opportunities presented.

In addition to data there are other critical issues that must be addressed by any company seeking to utilize AI technology. Chief among these are talent and culture. One of the primary issues faced is the talent pool necessary to sufficiently manage, develop and execute analytic projects involving machine learning. Data scientists, those individuals skilled in computer science, math and domain expertise are necessary to develop and maintain these AI programs. However, there is a shortage of data analytic talent in this industry. On the down side, many employees will quickly lose relevance in the workplace as much of the tasks they perform are taken over by machine learning technology.

Another potential employee change-over will be found in the level of consumer interaction. With the advancements of expert systems and virtual assistants, consumers can ask questions, receive answers and proceed with their applications without a human interface. While the feedback so far is mixed on who is most likely to use such an approach, Loan Depot has already engaged a company to develop an interactive consumer facing system to assist potential customers in this process. Another company, Neutrino Financial Services is developing a program that will allow consumers to obtain answers to questions and start the application process. When ready they can then select a company from a broad listing of lenders, for whom they want to apply. All this without the use of a loan officer. The applications taken in this program will then be transferred to the selected lender, thereby giving the consumers more choice and the lender more access to a variety of applicants.

If the use of AI, in all its various forms, is to be successfully implemented in this industry, a cultural change also must occur. The long-lasting impact of any AI implementation can only be made when a fundamental shift in an organization’s culture takes place. Although there has been much discussion about data driven organization, little of the effort to make this happen has occurred. Job types and job descriptions must change along with business processes and technological solutions are necessary. In addition to investing in the technology, businesses must invest in the appropriate training of staff and process redesigns if AI is to be successful.

However, the application process is certainly not the only area where AI will have an impact. We are already dealing with AUS systems and the accompanying deletion of underwriting staff. This however is just the beginning as much of the clerical functions can use Machine Learning AI with a small contingent of personnel available to deal with the exceptions. Then of course there is the closing process. Today we have acceptance of digital signatures and electronic delivery of the closing documents. While there are some states that will not yet accept digital signatures, this too is rapidly changing. Of course, the biggest issue at closing is yet to be tackled; that of explaining what all these documents mean and what they require of the consumer. However, with a program such as the one being developed by Neutrino, consumers will have access to the resources necessary to address these questions by phone, tablet or laptop.

One of the most obvious uses of AI is in the servicing environment where most of the administrative work is primarily clerical. The potential for the use of AI, including expert systems and machine learning is tremendous. The processes that take place here are prime targets for this type of change and would reduce the overall costs of servicing significantly. Default management could also benefit from these programs as “Bots” are developed to predict potential delinquents much sooner through statistical analysis of payment history and changes in credit status. Assistance with notifications to borrowers would also establish a more beneficial arrangement of calls and relieve some of the staff when applicable.   Another usage would be in the identification of property value changes.

Utilizing the data collected and standardized will assist in congregating loans to form MBS pools. Because of the data quality available from a data-driven organization’s focus, the secondary market will have confidence in the information provided. In addition, utilizing the existing risk model to establish operational variance risk, these institutions will be able to effectively price loans. By having loans accurately price for the performance risk will make them a more valuable investment tool and enhance the overall buying and selling or these assets.

There is no doubt that AI is coming to the mortgage industry and there is also no doubt that in order to reap its benefits, the industry must change. Only when we recognize that artificial intelligence brings with it, costs and risks that will redesign the familiar, and accept what is to come, will this intellectual achievement be achievable.

About The Author

QC: Now Officially Dead?

In 1995, with the advent of automated underwriting systems, I co-authored an article entitled “Is Quality Control Dead?” that appeared in the Mortgage Banking magazine. At that time there was a strong belief that QC was only used to find underwriting errors and with automation taking over the underwriting process there was no need to review these loans. At the time, the agencies, Fannie Mae, Freddie Mac and HUD must have been part of that trend because they made no changes to the existing antiquated QC programs they required for seller servicers.

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Unfortunately, as we all have learned to our great regret Quality Control was needed more than ever. From the turn of the century through 2007 lenders rode roughhouse over the underwriting requirements and triggered the greatest financial crisis since the Great Depression. Even as the QC Committee of the MBA meet with agencies, Congress members and consumer groups asking that they support stronger QC requirements, less and less attention was paid to QC. Despite the white papers developed showing the extent of fraud and documenting proof of what the lack of support for QC was conjuring up in the “magic elixir” that were the subprime ingredients of the collapse, QC was so weak there was no hope that the industry would listen. None of these warnings were heeded. Since then of course, QC has been revived and strengthened and the economists say we are fully recovered.

Yet once again we are hearing and reading about the latest and greatest mortgage program; the digital mortgage. According to the developers and purveyors of these programs, we have once again eliminated the need for Quality Control. These programs and their supporters claim that by using these programs have the capability of electronically validating the information entered by the consumer, running the data through an AUS and providing an approval within minutes. It is only if the loan cannot be electronically approved does it go to a loan officer to amend and approve.

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However, just as in the initiation of the automated underwriting systems, there are opportunities for mistakes inadvertent or otherwise. One of the biggest concerns is the inability to validate the information. While it sounds good, how many consumers are willing to give bank account information on-line, or those who work for small companies that don’t report the information to these on-line employment services. Sure, we can get tax returns, but they are at least a year old and not helpful in giving current income information. How is that validated?

Furthermore, despite the restrictions placed on lenders regarding DTI limitations, product and document types, non-QM loans are thriving. Just today I saw an advertisement for a “new” mortgage type, “No Income, No Employment”. There has also been a myriad of statements from the current political administration that the controls put in place to prevent another crisis will be loosened and/or eliminated in the near future.

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The question then is, can this alternative QC process that leaves numerous loopholes for bad loans to slip through and provides little incentive to do things right, stop another housing crisis. More than likely the answer is no, and because of that, it is likely that Quality Control really is dead. May it rest in peace because the rest of the industry surely won’t.

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Staying The Course

In talking to individuals who attended the recent technology conference I was somewhat surprised that many brought up the fact that the industry was not only ready, but looking for the opportunities to run their companies using robot or “BOT” technology.

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While the idea that companies would be run completely by some type of “bot”, whether intellectually, or physically is still far from reality, this conference seemed to be giving signals that we are headed that way. In reviewing various summaries of the conference, it was readily apparent that those involved in the technological side of the mortgage industry are looking toward the implementation of joining current technologies to make this happen. In other words, having progressed through data consistency, compliance issues, rule-based artificial intelligence and OCR opportunities, mortgage technologists are looking forward to combining them into the ability for the technologies alone to conduct functions that are currently being completed through an interface with company personnel. While this “BOT” approach, is already in place in parts of the industry, the mortgage application process comes to mind, it has not yet reached the potential envisioned in the early 1990’s when the first automated underwriting tools were developed.

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While no one, even those who believe strongly in the potential of AI, think the industry is on the brink of replacing humans with a machine, there are many areas where this approach can provide significant benefits. Many of these areas have been within the scope of industry visionaries for years. One that I am most familiar with is the one I generated for automated quality control.

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In 1995 I co-authored an article in Mortgage Banking magazine about the changing role of QC in the new area of automated underwriting systems. Prominently featured in that article was the idea of pre-funding QC which could be built off the systems created for underwriting automation. Even though it took a catastrophic collapse of the mortgage industry for anyone to recognize the value, lenders are now required to conduct such a review. However, it has not played out the way I envisioned it in that article where automation would provide the function.

Despite the rejection of the concept I plowed ahead with visions of automated quality control and the potential value it had to the industry. In my mind the existing quality control function would be replaced by a rule-based engine that would test each step of the process at it occurred. This program would then culminate in a score that would identify performance risk due to the lender’s mistakes or variances from guidelines. This score, and the underlying data, could be used to identify process weaknesses as well as give investors an accurate risk of the loans they were buying.

Eventually I did create this program based on a risk model that I developed and which has a patent pending. Unfortunately, I tried to commercialize it in the early 2000’s when the only concern was the avarice opportunities in the industry. The program has been sitting waiting for the right time and place. By staying the course I may finally see my vision a reality.

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The Good, The Bad And The Reality Of AI

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When I was getting my Masters in Business Administration, one of my professors lectured about the future of business. One example he used was from a futurist’s ideas on the factory of the future. According to this individual the factory of the future would have two employees: a man and a dog. The man would be there to feed the dog and the dog would be there to make sure the man did not touch anything.

While this tale may seem laughingly far-fetched, conversations held at the recent technology conference seemed to indicate that this scenario is within the realm of possibility. Numerous discussions were held about the use of artificial intelligence (“AI”) within the mortgage banking industry and ranged from rules-based programs to utilizing programs such as optical character recognition (“OCR”) and validating document collection to produce the first “bots” within the industry. But is this really AI or is real artificial intelligence the actual use of computers that can emulate human thinking processes and contain human drives such as hunger, power and self-preservation?

Controversy abounds on the subject and not just with potential users but among the most advanced thinkers in this area. Their thoughts and beliefs are wide-ranging from the concept that AI will likely play a part in human extinction to those that believe it will improve people’s lives and give them more family and leisure time. An article in April’s Vanity Fair magazine quotes Elon Musk, the developer of Tesla cars and cost-efficient rockets allowing for the settlement of Mars, as believing AI is humanity’s “biggest existential threat.” On the opposite end of the scale, Ray Kurzweil, a futurist, has predicted that we are only 28 years away from the point where AI will far exceed human intelligence and humans will merge with this super intelligent program to create the hybrid beings of the future.

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While arguments continue at this theoretical level, most of these individuals agree that one of the greatest drawbacks to the full use of artificial intelligence is the interaction currently necessary between humans and these tools. In other words, for these programs to work, a human must verbally ask a question, make a statement or key in information. This problem goes away however with the merger of biological intelligence (human thought) and machine intelligence. To accomplish this, companies are currently working on an injectable mesh, called a neural lace, into the brain that can flash data from your brain wordlessly to your digital device or to the cloud, thereby creating unlimited computing power.

With the on-going merger of AI into businesses there is also concern about how it will be managed and controlled. Current public policy on AI is largely undetermined and the software is largely unregulated. Some of the biggest technology companies have taken it upon themselves to develop a partnership on the subject in order to explore the full range of issues, including ethical concerns. The European Union is also deeply concerned and is considering such legal issues as whether robots have personhood or should be considered more like slaves as found in Roman law.

But the question overriding all of these issues appears to be what exactly is artificial intelligence? Is it simply a bot-like program that runs rules that do simple labor intensive work or is it actually the ability of machines to think as humans and take over the entire workload of any business. And if so, what does this actually mean to employees in those businesses? Will their jobs be replaced with robots that not only collect information and compile data for a rules-based engine but actually make decisions based on the data fed into the machine’s intelligence.

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Most importantly to mortgage lenders, what does all this mean to our industry? While the fun and intellectual stimulation that comes from brain-storming these ideas generates lots of enthusiasm, if these concepts become reality, we need to be prepared to utilize them to our advantage and not be thwarted by extensive costs and back room operations.

One way to envision how these AI programs will impact the industry is to look at what has been happening in similar operations. An article by Penny Crossman in the March 16, 2017 American Banker entitled “All the Ways AI will slash Wall Street jobs” gives some insights into what we might expect. According to the author, Opimas, a capital markets consulting firm, projects that by the year 2025 artificial intelligent technologies will reduce employees in the capital markets profession by 230,000 people. Furthermore, spending for AI-related technologies is expected to be more $1.5 billion and will reach $2.8 billion annually by 2021, just four years from now. This number does not even include the start-ups that capital market firms will invest in during this period. All of this expense is expected to be offset by a 28% improvement in their cost-to-income ratios.

So where are these programs being placed? The first functions being replaced by AI technology are process-oriented jobs. These jobs are actually being replaced by lower level AI functions that are programmed to do such things as look up documents, find data and compare multiple data sets.   In addition to these process oriented jobs, those whose function is to conduct analytics on the data are also being replaced with such technology as machine learning functions. In this “deep learning” technology, AI programs digest large volumes of real-time data within a very short period of time and then “learn” to find patterns that provide insight and direction at a speed humans can’t begin to match.

Another area of capital markets feeling the impact of AI is front office sales personnel. Since initiating AI technology in this area there has been a 20% to 30% drop in headcount. In addition, many jobs in the middle and back offices are also feeling the impact. Since the majority of these jobs are processes that are connected by human manual intervention AI that brings with it image recognition can replace this human activity.

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Compliance concerns that resulted in significant headcount increases are now being taken over by AI programs that validate specific documentation and provide a more holistic view of the regulatory risk and organization compliance trends. This is one area where IBM’s Watson is proving extremely valuable.

The implementation of AI in capital markets gives an excellent overview of how this technology can be implemented in the mortgage industry. Currently, we use some lower level rules-based programs to conduct underwriting as well as OCR usage in some back-office functions. Applying the applications discussed previously, many, if not most of the job functions being conducted today by humans could in fact be replaced with future AI programs.

One good example is the use of AI to replace loan officers for taking applications and collecting data. The Rocket Mortgage program in use today by Quicken Loans is just one example. Furthermore, most of the data collection and organization process that is labeled “processing” is also easily replaced with existing sites that offer independent validation of the information utilized in making decisions.

An area that is also ripe for AI application is the title and closing function. Using OCR, data comparison and document production, can easily be completed while the risk of mistakes or problems at the closing table could be handled in real time.

Back room operations can also be easily incorporated into AI functionality since it is very much a data recognition, document collection and validation effort. Post-closing functions which now take time and massive amounts of human labor can not only be streamlined, but the data collected can be used to revise and improve the processes themselves.

While what may appear to be already included as an AI function, underwriting is actually where some of the best deep learning artificial intelligence is applicable. Since 1995 we have been using rule-based technology to conduct what we considered AI, but instead are simply automated underwriting programs. Deep learning AI offers the industry the solution that has plagued it since its inception, that of identifying the true performance risk of loans.

Today’s credit risk function continues to use static attributes to develop, expand and or shrink credit policy without knowing the potential impact on any individual applicant. This credit risk stalemate has resulted in lending programs that reject applicants that may in fact prove to be credit-worthy borrowers. This can easily be seen in such programs as affordable housing and minority lending. Using deep learning, rather than simply applying standard credit policy to an application, AI can conduct an analysis of the applicant in comparison to all probabilities of performance and decide to approve or reject. In other words, credit evaluations would be individualized for every applicant. In addition, performance probability would be the yardstick by which pricing is tabulated.

Servicing is of course, primarily manual back-office functions that AI can address. Once again a deep learning application can contain any information on taxes, insurances and related issues, transfer funds if and when needed as well as provide an escrow analysis, tax statements and individual billing statements.

Just this brief recap easily demonstrates the value of bringing AI into the mortgage lending environment. The question is “at what cost?”   There is no doubt that the advancements in AI would have the same negative impact on mortgage lending employees. In fact, the annual convention might just turn out to be a dog and a man. However, as shown above, mortgage lending is not the only industry that will feel the same impact.

There are of course risks. One significant enough to delay implementation of AI by some firms is the risk that the technological intelligence could misinterpret input information and make decisions based on that information that would be disastrous to the company. One such example has already been experienced by Wall Street to a small degree when a mention of Anne Hathaway in the news resulted in a bump in Berkshire-Hathaway’s stock. Now known as the “Hathaway Effect”, companies are implementing practices such as running validation scenarios and are placing restrictions and stops on critical process points. This of course requires human oversight and runs headlong into the issue of AI and the reduction of human jobs in the industry.

The discussion over job elimination and creation however needs to be a much broader discussion around the impact of AI in the economy overall as well as in our industry. We have to think about what the massive reductions in employment opportunities means and what type of jobs will be created as current competencies are becoming less relevant and those trained in AI technology become harder to find. This change from the use of human intelligence to artificial intelligence is similar to the change undergone by those individuals who today are labeled “white working class” as large scale manufacturing replaced humans with more advanced technology and those individuals who were educated to do manual and factory related jobs became unemployable. Those that were smart enough to take advanced training and education in the field have found new jobs, but those that haven’t are left feeling angry and disenfranchised.

New skill sets that will be in demand for AI revolve around software engineering and data science. This is of course a given. But there will also be the need for a hybrid of business and digital skills which involves individuals who are knowledgeable about the business, who understand the digital environment and know how to benefit the business by continually improving its digital footprint.

While AI continues to advance, and becomes more accepted in the industry, it would be wise of us to think of when, where and how it becomes the most advantageous to the human side of the business equation. Using AI also means finding what our function as humans involves. Are we the masters of the technology or will we become evolve to a position seen by Steve Woznick when he said, “I now feed my dog steak because I see humans being the pets for robots and I want my robots to believe pets eat steak.”

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What Should Trump Do About The GSEs?

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Great news! It was announced that the housing market has fully recovered from the debacle of the Great Recession. While good for the housing industry as a whole, the better news for mortgage lenders is the elimination of any new regulations.  Having dealt with two of the most difficult fallout of the crisis can we now wipe our hands of the problem and get back to running business the way we did prior to 2004? Well, not quite. There is still the issue of Fannie Mae and Freddie Mac to address.

Everyone, including consumers, is aware that these entities were up to their eyeballs in the lending programs between 2004 and 2008. Despite the fact that they each provided an AUS for use by lenders, the rules were written so that it would basically approve every loan submitted. Staying competitive seemed to be the only criteria. Their punishment however was slightly different. They were taken into government conservatorship after being bailed out by the federal government. And here they have remained. But now it seems that the time has come, or the industry has determined that it is time to finally resolve this problem. So what’s to be done and when.

The interest in answering this question seems to have generated a number of ideas about what to do and when to do it. For example, on a recent conference call one speaker, when asked about the GSE’s situation stated that he did not see any political catalyst to do anything at the present time. One of the primary reasons is the belief that the FHFA and its director, have a reasonably well run organization which allows for a delay in any action. He went on to say that while some changes in the charter and mandate are likely to occur, the issue of repaying taxpayers the $187B owed by them has to be addressed.

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Prior to these statements, articles in industry magazines were suggesting that there seems to be some disagreement on exactly what to do with the GSEs. While most agree that there is a continuing need for the government to be involved in the secondary market, whether this is the current GSEs or some other type of entity seems to be at the heart of the debate. One popular idea is to create a public utility with government control of all fees and charges through regulation. Paramount in this approach is the expectation that this entity will continue to offer access to all lenders and provide them with equal pricing.

With one of the GSE mandates being to provide affordable housing options to working class families, those involved in organizations that monitor this also want to ensure that this focus continues. However, based on the latest HMDA data, which shows that the greatest correlation to denials for non-white, Hispanic males or females was whether they were to be sold to one of the GSEs. This issue will continue to be burdensome to the agencies and despite the fact automated systems will continue to evolve, the on-going use of rules-based algorithmic models will do nothing to ensure the viability of any lending program, especially for these affordability issues. One thing seems consistent through all of these discussions. In whatever structure this government involvement takes place, it cannot be allowed to pose such an enormous risk to taxpayers again, nor can it ever again place the broader financial system at the level of risk it did during the Great Recession.

Another voice in the on-going discussion of what to do with Fannie Mae and Freddie Mac is the Mortgage Bankers Association (MBA) who recently published an introduction of its forthcoming proposal for addressing this issue. Based on the document it is clear they support a new secondary market approach. This initial document places an emphasis on the role of the federal government and the necessity of preventing this new “market” from fluctuations due to political turmoil, favoritism and/or changing administrations.

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The GSEs cannot be allowed to pose such an enormous risk to taxpayers again, nor can they ever again place the broader financial system at the level of risk it did during the Great Recession.

While most agree that there is a continuing need for the government to be involved in the secondary market, whether this is the current GSEs or some other type of entity seems to be at the heart of the debate.

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The focus of any congressional actions, according to the MBA position, should be to promote liquidity that stimulates investor purchases of mortgage-backed securities and prevent the taxpayers from taking on the risk of these securities. There are four critical elements they have identified that they believe must be part of any long-term solution. These include establishing the value of combining competition and regulations; providing equal access for all lenders regardless of size or structure; enhancing their current public mission for promoting affordable housing and finally, to maintain the level of liquidity for both single and multi-family housing. Furthermore, the MBA has included in this statement support for allowing the creation of additional privately owned entrants to compete with the reformed Fannie Mae and Freddie Mac.

These entities, including the new Fannie Mae and Freddie Mac would be organized as private utilities with a regulated rate of return and a public purpose of providing credit to the conventional mortgage market. In addition to this “end state,” MBA has identified a series of “Guardrails” that must be implemented to reinforce this new mandate. Among these are such standards as the maintenance of a “bright line” between the primary and secondary markets; these utility companies must be standalone to prevent any undue influence (such as those from big banks) and the resulting utilities should be regulated as a Systemically Important Financial Institution (SIFI).

So, What’s Missing?

Although these ideas and discussions are preliminary presentations of the more in-depth concepts discussions and legislative actions to come, the common thread in all the current offerings is the focus on Fannie Mae and Freddie Mac’s role in a new secondary market functionality. Emphasis has been placed on the idea that these new utilities will be aggregators of conventional single family and multi-family loans. So, where does that leave the other activities of that these agencies now control?

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One of the most obvious is that of creating and administering credit policy. While one of the “guardrails” listed in the MBA’s GSE Reform Principles and Guardrails, released on January 30, 2017, is the operation and management of “…the government’s QM-like single family eligibility parameters…”. What is not clear is whether the credit policies to be put in place will be unique to each utility or whether there will be one set of guidelines for everyone. One question left unanswered is whether the QM exception in place today will remain past the current stated end date.

As everyone is aware today, Fannie Mae and Freddie Mac compete through the variations in these guidelines, even to the point of differing calculations for determining income for rental properties. If this level of diversity in guidelines was to exist in several different utilities, it seems likely that it would cause confusion as well as set up any number of these entities to take risks that are would not be acceptable. Furthermore, the ATR/QM standards are the result of the CFPB regulations that the current administration as well as congressional opponents have vowed to eliminate.

In addition to these problems, while Fannie Mae and Freddie Mac have as a foundation of their charters the requirement that they expand homeownership opportunities for potential borrowers requiring more affordable housing, the reality is that this has not occurred. All one must do is review the past year’s HMDA data, including 2015, to see that the denials for non-white, Hispanic, men and women are most highly correlated to the intent to sell the loan to Fannie Mae or Freddie Mac. So, the question remains, if the current guidelines are failing to produce the results required of these agencies, how does adding more of the same expand that opportunity? On the other hand, will the emergence of very divergent guidelines individualized from each utility cause too much confusion and misdirection for the industry to handle?

Another expensive and antiquated program that are part of the agency requirements is Quality Control. Following the mortgage meltdown and the abundant evidence that quality failures were a direct cause of the mortgage failures, both agencies introduced loan quality initiatives. Unfortunately, these programs did not address the primary issues of ensuring the processes that produced these loans were under control, but continued to rely on controlling each loan through an inspection process both before the loan went to funding as well as afterward.

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Despite the fact automated systems will continue to evolve, the on-going use of rules-based algorithmic models will do nothing to ensure the viability of any lending program.

The issue of what to do with Fannie Mae and Freddie Mac will continue to play out for some time to come.

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While the intent to “discover” problems prior to funding was a noble effort, the result has been companies implementing 100% reviews on approved loans without a clear understanding of what to do with the results, how to cure many of the problems or obtaining updated information. Furthermore, the post-closing QC still occurs 90 days after the loan is closed and the sampling programs acceptable remain biased and the results incomprehensive.   To add insult to injury, these additional reviews double the cost while adding little if any, value.

With the adaptation of multiple utilities will the existing QC requirements remain, will each aggregator have the option to determine how they expect this analysis process to be completed or will every lender can design their own? Regardless of how it plays out, the value of quality control can be added in by ensuring that pricing reflects the product quality sold.

Last, but not least is the issue of compliance with regulatory requirements. Up to this point the GSEs have deliberately avoided evaluating individual compliance to regulations. While there are some valid reasons for this approach, with the new utilities, will this continue or will utilities decide to become involved with this process.

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The issue of what to do with Fannie Mae and Freddie Mac will continue to play out for some time to come. However, forward thinking originators and servicers will also be scoping out how any of these options can impact their business and be prepared when it does happen.

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Fair, Unfair And Deceptive

With the announcement, earlier this year about the latest data additions to be included in all future HMDA reporting, the industry has been heavily focused on making sure that the necessary data is available within loan origination systems. Furthermore, the loan application form, commonly referred to as the 1003, has been updated to ensure that all this data can be collected from the borrower(s). This additional information, in conjunction with what is already collected, form the basis of regulators Fair Lending reviews.

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Fair Lending, is the federal regulation that requires all lenders to treat every applicant equally. For depository institutions, their lending patters must demonstrate that they offer mortgage opportunities in the communities in which they accept deposits. Additional analysis is also conducted on the areas in which a lender typically lends. This has traditionally been known as the lender’s footprint and is measured by racial population distributions within specific metropolitan statistical areas or MSAs. In other words, if an MSA is 50% Hispanic, regulators would expect to see that 50% of your applicants are Hispanic. This they believe demonstrates the “fairness” of your lending practices. There are however some very “unfair” issues associated with this analysis, many of which will more than likely be exacerbated by the collection of additional data and the scrutiny of the CFPB.

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The most obvious of these is the poor quality of the data. Although the submission process includes quality and validity checks, inaccurate and/or inconsistent data is rampant. While most lenders work diligently to ensure good data, there have been instances where manufactured and calculated data have been used. Furthermore, until this past week’s announcement, there has never been a way to identify if all required lenders have even submitted their data. If data is submitted late or corrected and resubmitted, the changes never make it into the overall HMDA database for the year. Imagine one lender’s surprise upon finding out that the entire LAR they submitted one year was not included at all.

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Unfortunately, even the bad and or missing data included in the HMDA database is used to analyze lenders. For example, not all applications have the monitoring data completed and since it is the borrowers’ prerogative to complete, few, if any lenders have all the race gender and ethnicity data for every application. This can lead to some very unfair conclusions. Recent comparisons of the number of loan applications compared to these completion of monitoring data found that these numbers just don’t add up. For example, if a lender has 10,000 applications but the breakdown by race shows that only 37% were minority, does that mean that 48% are white? If so, and the population is the MSA is 52% minority does this mean the lender is failing to meet regulatory standards? Without knowing the race of the remaining 15% of the applicants, it is impossible to tell. Yet this is a major part of the regulatory review. Isn’t this a bit deceptive on the part of the regulator?

Finally, regulators and lenders alike must reconsider the use of comparative footprints in conducting this analysis. When lenders and banks were primarily regionalized this may have made sense but with the expansion to nationwide lending and the use of electronic applications, this model is unreliable and in fact deceptive when reaching any conclusion. This must be changed if we are truly to identify any discrimination practices.

The issues identified here are clear indicators that the regulators are not accurately measuring a lender’s Fair Lending, but instead are conducting unfair and deceptive analytics themselves. To protect themselves, it in in every lender’s best interest to know more about their HMDA data then any regulator does.

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Consumer Evaluations Could Help Servicers

Recently the CFPB issued a proposed directive to servicers requiring the development of a rating system that would indicate to consumers how efficiently and effectively the servicer addresses complaints. They had suggested a five-star rating system which could be published and available to anyone interested. The response from servicers was quick and extremely negative. This idea to them was an anathema. However, before the issue came to a head, the current administration declared that any new regulations were to be withdrawn.

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However, one must wonder why mortgage servicers were so vehemently opposed to this idea. Was it because they have failed to address complaints appropriately in the past? Did they believe that this requirement would force them to expose negligent or unsatisfactory actions? Were they concerned that by allowing this information to be given out they would somehow diminish the value of their organization? Or is it because they still don’t recognize borrowers as their customer, believing instead that their sole purpose is to serve investors rather than consumers?

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The idea of a consumer rating system is not new. J.D. Powers is well known for its rating programs and awards given to companies who score well on these programs. The fact that a company has received such as award is frequently a central part of their marketing campaigns.   Quicken Loans continuously brags about the number and frequency of their J.D. Powers awards as does Delta Airlines and winners from other industries. What is so abhorrent about such a system for servicers?

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One reason has been the lack of a standardized approach to evaluating responses to consumer complaints and/or inquiries. Unfortunately, developing a taxonomy that would grade responses cannot be developed until there is some standard acceptance of what should happen and in what time frame. After all, the Chinese say “Any road will take you there if you don’t know where you are going.” Right now, there appears to a consistent lack of understanding and/or agreement of what constitutes ‘doing it right”.   Once that is determined, levels of performance can be developed. For example, if it is agreed that satisfactory performance is responding to the consumer within the required timeframe with an answer to a question posed or information provided, then actions that are better and worse can be described and a positive or negative assigned.

Another statement that keeps popping up is the fact that consumers are not going to like what the servicer did or the answer to their question. Since this is bound to happen, the servicer will appear to provide unsatisfactory service when in fact they were just complying to the required servicing standards. Of course, every company expects this to happen. Delta hasn’t flown every airplane on time and without incident and there are ways to deal with one off issues when analyzing the results.

Recognizing that the benefits far outweigh the negatives must happen before any of this can get started. Having data on which consumer activities are beneficial and positive would allow servicers to determine how to duplicate this same approach on those that appear to be a problem. Living in a world where management is blind to the positives and negatives of their organization is ridiculous when an industry devised and properly managed consumer evaluation program can win loyal customers and maybe even impress investors as well as regulators.

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