Being touted as the answer to all the stresses and dissatisfaction currently found in the workplace, it is being developed as the advancement promised by technology prognosticators who have promised that work can be reimagined to be more flexible, faster and adaptable. A benefit, or so we are told, is the elimination of work that is standardized and repetitious.

So, is this good for mortgage lending?  Reading numerous articles in industry trade magazines and discussing these ideas with those who are currently mortgage lenders, it is apparent that we actually know very little about AI. Every article I have read claims to have found the answerandgives us a short, simplified explanation about what it actually does.  To add more confusion, artificial intelligence technology is sprouting a completely new set of acronyms which are anything but helpful unless you thoroughly understand it.  Another issue I found prevalent in the discussion is the fear about its usage and the elimination of jobs we now hold. But are there any risks in using AI that we don’t know about?  Do we understand how technology can make better decisions that humans?  What do we need to have to make sure the technology works accurately?  How can we implement it into our production and servicing processes?  So, before we rush head-long into the next great thing, we need to take the time to really understand what this technology is and does. 

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This series of articles provides some basic definitions utilized by artificial intelligence technology providers, identifies the various iterations now in use and describes what critical elements are necessary for it to do what it claims to do. In addition to the technology itself, the articles will focus on the risks and opportunities associated with AI as well as discussing the potential for process and job function disruptions the industry will face as it is implemented. 

Beginning with the basics

“Work”, the basis of economic betterment, is changing again.  Known as the “Human Economy”, work involves people performing activities that are considered economically productive.  From the earliest days of economic activity humans have experienced the need to improve productivity.  Work, as we think of it today began with the development of wheels, wagons and animals for transportation as well as the use of small hand-tools for creating individual products one at a time. Next came the industrialization of product production, and then the initialization of automation to conduct the actual work. Ultimately this has led to better products, more satisfied customers and greater profitability while providing a better income and more time for leisure and family time for employed individuals. We are now in the initial stages of the next advancement, the use of artificial intelligence, to once again change what work is and it isn’t. Despite numerous nay-sayers, this new technology is bound to have a significant and fundamental change to our business. We, as an industry, need to know more. 

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As defined by Paul Daugherty and James Wilson in their book, Human + Machine, Reimagining Work inthe Age of AI,artificial intelligence is a system that extends human capability by sensing, comprehending, acting and learning.  In other words, it is the simulation of intelligent behavior in computers or sometimes referred to as a machine with human cognition and the ability to carry out tasks as a human would. 

Within this field there are various levels of “intelligent” program types. They have names such as Expert Systems, Machine Learning, Deep Learning, Robotic Process Automation (RPA), and Neural Networks. Another common acronym describing how AI works is Natural Language Processing. All of these terms reflect a variation on the basic purpose of AI, that of having machines do the work of humans.  But how it is done and what functions it addresses are quite different.   

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Defining Types of Artificial Intelligence

Today, the term is discussed by many in our industry as a single effort of implementing AI, yet they confuse terms like Expert Systems, Machine Learning and Deep Learning, seeing it all as one new technology.  When you hear these buzzwords tossed around in conversations, it is obvious that many do not recognize these terms as very different types of AI.  In order to clear up the confusion a definition for each type must be recognized for what it does and an understanding of how it can be used, beginning with simplest to the most advanced.  

All artificial intelligence products are based on one core requirement; the ability of the system to collect and learn facts.  Just as individuals are not born with the knowledge they have today, a machine must learn what it needs to know.  First these machines must be taught the basic facts. Even once these basic facts are learned, they are not innately able to associate those facts with the problem presented to it. It must be taught how to associate these facts appropriately with the problem it is trying to solve. As the complexity of the problem increases, the need for these machines to conduct this association becomes more complex, thus the varying levels of AI. 

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Because AI is a relatively new technology, scientists do not necessarily agree on the terms and definitions utilized in describing the capabilities of current AI products.  The definitions and examples given below are most consistent with the terminology we use today.  In addition, there are terms that are frequently used within the scientific community for describing the abilities of specific AI functionality.  

Expert Systems– The most basic of AI products, this technology is a method of automated reasoning based on a very specific set of facts, rules and principles.  The automated underwriting systems we use today are an example. When we ask a question, such as does this loan meet our credit guidelines, the program takes the facts as provided in the application and from external data, compares them to the facts taught to it as an acceptable loan and filters this data to arrive at an answer. 

If any of the data is inconsistent with the “facts” taught to the machine, or there is no rule, it will be unable to decide.  These systems do not “learn”.  For example, if an AUS system has a rule that limits DTI ratios to 43%, it will not approve one at 44%, regardless of the fact that the human who then reviews the application does so 95% of the time.  The system has not “learned” that 44% is acceptable.  

Robotic Process Automation-RPA was designed and is utilized to automate those processes that are routine and labor intensive.  Today businesses find that work associated with repetitive tasks, such as inputting data, making calculations and answering standard customer questions is work that can be done by basic RPA technology.  Your home-based Alexis system is an RPA devise that answers questions and conducts some limited analyses based on the input.  For example, when you ask Alexis to play music, it will find a music program and begin to play it. The robot has learned what music is, the types of music and what performers are associated with each.  If you do not like the type of music selected you simply tell Alexis and the music is changed.  The same applies to individual songs.  Alexis then “learns” what type or piece of music you do not like and will not play it again.  The “bot” as it is now called, has learned something new and will apply it in the future.  

Today, companies with workflows that are consistently repetitive and simple are using this technology.  For example, a warehouse which contains thousands of products which need to be compiled into one order uses these bots to find and deliver the items to a central location.  These bots, which have the appearance of what we think of as robots, move up and down narrow rows of products and quickly and accurately collect the necessary items. This has resulted in the elimination of personnel who perform this function with much less accuracy and in a greater time period.  It even has the impact of allowing warehouses to be built higher, thereby eliminating the need to have long, low buildings with lower rows of goods so that humans can reach.  

One of the critical features for these bots is the ability for it to recognize what the user is saying in their own vernacular. Known as Natural Language Processing (NLP), its purpose is to allow bots to learn various languages, accents and idioms used by customers. The development of NPL requires that this occurs for industry specific terms as well.  Asking a question of a bot when the terms are used in different scenarios will most likely not get the answer you need.  Again, if the bot has been trained in the Northeast and is asked a question from someone in the South, without NLP adaptation to this accent, it will have problems answering the questions. 

Narrow A.I.- This term has come to be used by many of the individuals working in this field. Like its name suggests, it is focused on executing a single task.  Human interactions with a narrow A.I. are limited because Narrow A.I. can’t think for itself. This is why sometimes you’ll get a nonsensical answer back when attempting to use it because it lacks the ability to understand context.

General A.I. General A.I. or Strong A.I., as it is sometimes called, provides the ability to understand context and make judgments based on that context. Over time it learns from experience and is even able to make decisions, even in times of uncertainty.  Even with no prior available data it can use reason and be creative. Intellectually, these computers operate much like the human brain. This is where we are headed when we talk about its functioning in place of a human.  To understand the abilities of this technology and how it operates, the basic functioning of the human brain needs to be understood.  

Neural Networks. Each individual has within their brain a series of networks which transmit data. This can be a s simple as learning what tastes you like and what you don’t like.  Before you learn, by tasting, that you don’t like fried liver there is nothing in your thought processes that tell you it is not acceptable to you.  However, once you taste it and realize it tastes awful to you, you reject this food choice because your brain has learned from this observational data.  Our internal neural network processes the data we are observing and alerts us to the fact that fried liver tastes awful to us and alerts us to that fact before we make an unacceptable choice.  

Developing these neural networks in our brains takes time. My son recently bought a 2-month-old puppy.  Obviously the first thing he needed to do was house train him and so he has spent a great deal of time making sure Bogey understands what is expected and what he must do when he needs to go.  Developing this neural network in the dog’s mind is not an easy task.  Over time, the training took hold.  In other words, a neural pathway had been developed. 

Machine Learning. Machine Learning, “ML”, technology. This is the field of computer science with algorithms that learn from data that is “taught’ to the technology.  It also uses the data and incorporates algorithms that learn from and make predictionson data.  This technology can learn from humans or it can learn from other data. In other words, this technology is conducting some of the analytic thought used in bringing various components together and allows it, to some extent, to predict a result.  Another utilization of ML is known as “supervised learning”.  In this scenario data is broken into categories such as inputs and outputs and humans use it to develop an expected output from the technology. When the output is inconsistent with what is expected based on the algorithm, it sends out an alert to the user.

One of the most common uses of Machine Learning is found in fraud investigation. In these cases, the machine is feed information and searches for a pattern of data, such as spending habits or income, and utilizes its algorithmic tools to determine if the data is what would be expected.  If in using the previously received data, it determines that the new data does not fit the pattern the output reflects this inconsistency and potential fraud is identified. This is what happens before you get that phone call asking if you had made a recent trip to Saudi Arabia. 

Deep Learning. Deep Learning specifically refers to the ability of the machine to learn from other data. Deep Learning consists of a multi-layered network of algorithms in which data is processed through multiple layers with each layer as individual inputs.  It also allows the data to flow back and forth between these layers thereby expanding the systems knowledge base.    Deep Learning “DL” machines can analyze patterns and people to identify potential problems or opportunities far enough in advance to allow for early intervention or resolution.  Imagine the ability to know the probability that a borrower will default even before the loan has closed and been set up in servicing.  

These types of artificial systems, machine and deep learning, are both predictive systems. These systems find relationships between variables in the historical data, identify a pattern and then develop a model to predict future outcomes.  Developing these patterns requires the use all available data, including borrower information from sources other than the application, property information, both local, state and national, economic conditions, etc., that are now available provide the opportunities to find correlations never before considered.  By understanding these different approaches to artificial intelligence, it is much easier for management to evaluate the type needed to successfully implement such a program.  There are however other issues to be understood and addressed before taking the leap to artificial intelligence.  The next article will explore the risks and opportunities of AI along with new developments addressing these issues.  In addition, it will discuss what it means to “reimagine” how work will be done. Finally, we will discuss what a reimagined mortgage operation looks like and the impact its inclusion will have on the workflow and job functions.  After all, don’t you want to know if a robot will be taking your job?

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It’s been said that artificial intelligence is the future, but I’d argue that AI is very much a thing of the here and now. It’s playing an increasingly significant role in marketing efforts, and is taking marketing automation to the next level. And during an era when customers are demanding fast and hyper-personalized service, AI-based technologies couldn’t be more critical. AI-based technologies bolster marketing automation efforts through personalized interactions. Your business can benefit on a multitude of fronts by embracing these game-changing advancements.

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Email marketing

It’s easy for emails to get lost in the shuffle of a busy day. After all, inboxes are overflowing with meeting reminders, work correspondence and plenty of promotions. Enticing a customer to simply open a marketing-related email can be challenging. But with the right tools, you can consider that challenge conquered. 

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AI-based technologies are helping marketers win the attention of potential customers in numerous ways. For instance, they’re allowing small- and medium sized businesses to improve their email marketing automation strategies by knowing who to target, when and how frequently.

Know your target:AI helps businesses understand – and foresee – customer behavior patterns, and detect which strategies email subscribers best respond to. It can also help businesses more effectively automate emails to ensure customers are receiving personalized correspondence and promotions relevant to their needs.

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Adding a personalized touch to emails pays off. A Statisa study shows that personalized emails had an open rate of 18.1 percent, compared to a 13.1 open rate for non-personalized emails. 

Engage thy customer:Of course, AI-generated emails should do more than attract customers – they should also engage them. Email subject lines can be tailored for each customer, for example, while the emails themselves might contain personalized offers. 

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Lead nurturing 

When it comes to gathering and analyzing customer information, we mortals are no match for the pace and efficiency of computers. AI-based programs are capable of processing user data at lightning speed, and can be hyper-responsive to customer needs.

Automating the data collection process can help simplify the lead-nurturing process for businesses, while AI can set the course for future marketing strategies.

Capturing information:AI provides marketers with the valuable information they need to close a sale. It tells us when someone has visited their site, which pages they visited and how much time they spent perusing products. 

Marketing teams are using this data to drive sales, and if statistics are any indication, it’s working. AI could lead to an economic boost of $14 trillion in additional gross value added (GVA) by 2035, according to Accenture research.

Automated actions:As I mentioned earlier, AI-based software doesn’t just benefit businesses in the long term — it can also be of tremendous value in the present. Through AI, valuable customer information is being captured in real time, and can be instantly used to tempt shoppers with personalized offers and targeted advertisements. Forrester has predicted that 20 percent of enterprises will begin using AI to make decisions and provide real-time marketing instructions.


Successful businesses strive to equip customers with the tools and services they need. The trouble is, it can be a bit tricky to determine what those needs are.

AI aid:While automation can help track previous purchases and user experiences, AI can take that information and make predictions on the potential for additional sales. It can determine whether a customer is a good candidate for additional products or services and establish which products a customer might be in the market for next. 

Once your sales and marketing teams are made aware of your business’s cross-selling potential, they can make customers aware of those additional offerings.


A happy customer is a loyal customer. AI can help you retain your existing customer base by providing you with a better understanding of client needs. You might think you already have a handle on that front with your CRM platform, but AI can provide a more complete, more actionable picture of your current customers.

Meantime, customers will appreciate the personalized service AI-based software offers, and are more likely to become repeat customers.

Reengaging past customers

Customers come and go – and they’ll come again with the right approach. Whatever the reason for losing a customer, it’s never too late to win them back – especially with some artificial assistance.

Helpful insights:AI can provide insight into how and why a customer left, and help determine the best way to reconnect. For example, it can be used to send an email with product recommendations based on a customer’s purchase history, or inform them of any relevant price drops.

If you’ve spent any amount of time looking into customer engagement and sales/marketing success strategy, you’re undoubtedly already familiar with the ways in which automation can help your business. Now, it’s time to build on those benefits by incorporating AI-based technologies into your marketing strategy. 

Businesses are becoming increasingly competitive and to remain relevant, AI-based technologies must be embraced.

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The Evolving Roles Of Mortgage Originators And Loan Officers

For years, many in the mortgage industry have used the terms “mortgage originator” and “loan officer” interchangeably. Today however, a new trend is emerging and the two monikers are developing into their own unique, individual roles. To understand this changing dynamic, and what their jobs will look like in the future, we’re going to dissect the two and see where the differences lie.

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While technology has helped simplify management of the more tedious processes of mortgage origination, sales and borrower engagement, there remains a great deal of information for lenders to keep up with. From creative marketing strategies to new regulations and products, it is here where loan originators tend to shine. Rather than engaging with borrowers on the minute details of their loans, loan originators tend to focus instead on their overall book of business and the “big picture” strategies for clients. I occasionally also refer to them as loan planners or mortgage consultants. 

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On the flip side, loan officerstend to be more adept at driving business through their branch or office. For these professionals, stress tends to build as they gather together a large book of business, but then have to back-up their sales with high-level research and study. Their preference is often to work directly with their borrowers on an individualized basis and help them identify the best loan for their own unique financial situations. These professionals are also tend to have a background in processing or underwriting, which typically means they are highly attentive to small details and nuances of specific loan structures.

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That’s not to say one is better than the other, or more necessary to a productive mortgage business. In fact, the most successful lenders have the two tracks running parallel with each other and functioning akin to a pilot and co-pilot, or even a counsel and co-counsel in legal fields.  

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This changing dynamic is important as it puts the borrower first by creating a synergy between the loan originator and loan officer, with both playing to each other’s strengths. Frankly, I see the loan originator as representing the first steps of a borrower’s journey, helping him or her identify possible strategies for their mortgage, building trust and offering general guidance on issues like credit scores, for example. Once this framework strategy is in place, borrowers can seamlessly shift to working with loan officers who can help them with the minute details and identify their personal pathway to debt-free homeownership. 

Taking a more in-depth look, one can see that it’s the originator who lays out the ‘big picture’ or 30,000 foot view for the borrower, and helps them narrow their focus to one or a few select options. Then, the loan officer helps them narrow that view even further to create a smarter mortgage plan that allows them to ultimately save money and build personal wealth. This works to the advantage of borrowers as they have access to not one, but two experts in the mortgage industry, each with their own specialization to help them identify the best possible loan. 

This trend will continue to develop as technology impacts how lenders do business with borrowers, particularly as data, machine learning and AI come to play as an integral part in the lending process. Using data, lenders are able to identify and interact intelligently with borrowers at the start of their decision making process, establishing trust prior to them even starting the mortgage process. When the borrower does decide to move forward with originating a loan, lenders will be better prepared to provide them the best quality advice regarding the loan options available. While the role of originator and officer may be diverging in some respects, when a borrower is deciding where to take their business, they do not look for individual titles or positions. Rather, they look for businesses that respect their financial position, care about their future and work with honesty and character. That’s why this changing dynamic is important, not because of job titles, but because it will offer lenders more opportunities to demonstrate their expertise, educate borrowers and guide them down a homeownership path that supports their long-term financial wellness.  

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AI-Based Textual Analysis

In today’s mortgage market you can’t pick up an industry publication or attend a trade show and not hear someone talk about Artificial Intelligence (AI).  Many of the claims talk about how AI will disrupt the mortgage industry or radically change how mortgages are originated or processed.

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Some of it is pure hype and some of it includes technology that can clearly streamline processes and reduce cost.  So how do you filter fact from fiction?  Let’s take a look at how AI-based textual analysis is actually at work in the mortgage industry and the different approaches some vendors are using.

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High-Level Approach

The most fundamental difference in approach between an AI textual analysis and the majority of other “advanced” document recognition technologies is that AI textual analysis treats variable layout documents as unstructured documents whereas most other prominent solutions treat them more as semi-structured documents.

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To illustrate the difference in methodology let us consider a customer wishing to process pay stub documents. An AI textual analysis implementation would typically be deployed with one set of completely generic rules designed to encompass all variations of the “Pay Stub” document type from any company.  Because alltext is evaluated by the AI engine, the rules can flex with the layout and verbiage changes just as a human does when reading the page. The solution is also capable of being configured to perform conditional processing for specific exceptions to generic rules (per-layout exceptions) but it is not typically necessary to do this.

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Most other modern advanced document recognition technologies treat document variations as semi-structured documents. These solutions typically either: 

a) Remember as many of the variations as is practical and process each variation with layout-specific templates for processing Or b) Apply a mix of layout-specific processing and some generic processing (usually the higher incidence layouts are processed with layout-specific or templated processing) 

The obvious advantage of the AI textual analysis generic approach is that, as new layouts appear or existing layouts change, the software is better equipped to deal with these new variations. This advantage was recently validated at an account with over 35,000 layout variations where rules had been in place for five years with not a single modification. An audit of this client’s processes after five years revealed an identical automation rate to that when the system was first deployed. This outcome was observed despite the fact that a significant portion of the originally dominant layouts had been transferred to EDI processingand were therefore bypassing the system now. 

As you can see the varying approaches often produce different levels of success.  But how can you determine which is best for your organization? Get a demo and hope that the solution is more than smoke and mirrors?  Up until now that is usually the case.  To overcome much of the confusion and disappointment, a better evaluation process in many cases may go a long way towards greatly minimizing the risks involved in choosing a vendor who can actually deliver AI-based textual analysis.

In order to quickly understand AI-based textual analysis and its capabilities, a blind test with several sample files should be considered the gold standard for an evaluation.  This is especially true when it comes to the challenges presented with the many and varying document types and quality levels of document images found in the mortgage industry.  Asking vendors if they are willing to perform a test on a never before seen sample set of typical loan files on site and in sight of your evaluation team, is a great first step in separating fact from fiction.

Ideally, an evaluation should be setup as a one day event to hedge against any vendor refining their results.  This kind of test is intended to demonstrate the validity of the vendors’ out-of-the-box capabilities so that prospects can be assured that they are considering a proven, robust and scalable solution ready to deliver productivity improvements in weeks rather than months or years.

For qualified opportunities, Paradatec will perform this process, which enables prospective clients to quickly understand the overall levels of automation, and speed improvements they will be able to achieve with their technology.  Download our whitepaper on AI based textual analysis

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St. Louis Loan Originator Bets On FinKube To Help Increase Home Finance Business

FinKube, a company that provides AI-powered Platform-as-a-Service solutions for a range of industries, announced that St. Louis-based LenderCity has successfully deployed ELSA, FinKube’s Electronic Loan Services Assistant. The mortgage industry’s first chatbot is already interacting with prospective borrowers on the LenderCity website.

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“Consumers want immediate answers to their home finance questions and ELSA is smart enough to provide the information they need and gather the information we need to prequalify the borrower,” said Gregg Harris, principal at LenderCity. “We know we need to respond very quickly to borrower requests for information, but we also want to capture as much information from them as we can, without taking up the loan officer’s time. FinKube’s ELSA is the answer.”

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ELSA is an intelligent assistant that uses AI and machine learning to enhance the origination process from origination to close. Her AI is powerful enough to gather borrower information, render decisions, automate time-consuming tasks and help lenders produce fully compliant mortgage loans in as few as 20 days, though she is well versed in any form of consumer lending.

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“American home buyers still want to visit with a live loan officer before signing on a new mortgage, but it’s not efficient to spend the loan officer’s time in conference with borrowers who do not qualify,” said Jorge Sauri, founder and CEO of FinKube. “At the same time, those borrowers who do qualify expect to have their questions answered immediately. They don’t want to wait for a call back. They want to feel like they are in control. Most chatbot technology cannot offer that, but ELSA does.”

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With ELSA, LenderCity is able to hyper personalize the home finance transaction at every stage. Studies have shown that this:

>>Reduces customer acquisition costs by 50-80%

>>Increases engagement and conversion by 500%

>>Reduces customer service costs by 50-80%

>>Increases loan retention by a factor of 6

As an A.I. powered virtual assistant, ELSA works 24/7/365 pre-qualifying leads, communicating with customers and synchronizing outreach across chat, text, voice, email, and mobile wallet. In addition, FinKube can deploy ELSA ten times faster than generic chatbots that can’t speak mortgage out of the box.

Leading-Edge AI In Mortgage Lending

Previously CEO of HeavyWater Inc., the mortgage-focused Artificial Intelligence (AI) provider recently acquired by Black Knight, Inc, Soofi Safavi now serves as Managing Director of Black Knight’s Applied AI group, bringing leading-edge AI and computing capabilities to the Black Knight product portfolio. With over 20 years of experience in mortgage and banking technology, and deep expertise in IT strategy, architecture and machine learning, Soofi is uniquely suited to discuss AI’s role in the mortgage industry.

Q: It seems as though Artificial Intelligence (AI) has resulted in incredible advancements across so many industries, but we haven’t seen the same in the mortgage industry. Why is that?

SOOFI SAFAVI: I would say that we simply haven’t seen it –yet. The mortgage industry is a very complex environment that requires vast amounts of expert knowledge to navigate effectively. That is why there is still so much work to be to be done despite a never-ending quest for increasing efficiency through technology. AI will be key for not only increasing efficiencies, but also identifying and eliminating deficiencies.

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Every day in our industry, we have thousands of experts leveraging technology to perform a series of activities and tasks that are essential to the mortgage process, from origination through servicing. The goal with a mortgage industry-focused AI is to capture that knowledge and replicate it within algorithms via machine learning.

From a high level, AI is about using computer systems that are able to perform tasks normally requiring human intervention. Machine learning is a type of AI that allows computers to learn without being explicitly programmed. It is a technology that uses algorithms to learn from and make predictions on data. It reads, comprehends and draws conclusions based on context to mimic human thinking and build expertise over time.  

An AI that can learn from the experts who are currently performing all of the many tasks involved in the mortgage process can offload much of the manual effort to technology. The mortgage professionals doing that manual work today can then shift into a different role, one more suited to the knowledge worker-based economy of the 21st century. They become teachers and guides of the AI, and make the decisions only humans can make. 

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Q: That raises an interesting question. There is a lot of uncertainty associated with AI. Many feel that individual’s jobs may be at risk if AI takes over much of the manual processing associated with the mortgage industry. What would you say to these people?

SOOFI SAFAVI: My answer would be twofold. First, I’d say look to history. Repeatedly in our industry – and others – the introduction of new technology has spurred fears of machines replacing people. When automated underwriting systems first came on the scene, many underwriters feared for their jobs. In fact, what they did was reduce friction in the mortgage process, allowing for what had been an entirely manual process to become more streamlined. The end result was increased efficiency and throughput, calling for far moremortgage professionals, not less.

While AI is light years ahead of automated underwriting, I expect the same will be true today. In the current environment, there is a significant amount of rigidness in mortgage origination, and people tend to go through this process a handful of times in their life. The potential for reducing friction in the mortgage process has increased exponentially with the advent of AI. What has for many years been a long, exhaustive and laborious process – on the part of the homebuyer as well as those of us in the industry – will see reduced friction via the automation afforded by AI. 

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An AI that has been taught to perform traditionally repetitive functions can do so more quickly and accurately than traditional methods. For example, verifying income, assets and insurance coverage; all traditionally manual activities that take hours to complete and are prone to error. Putting AI to work on these stare-and-compare tasks frees up highly-skilled mortgage professionals to focus on creating value, enhancing the customer experience and expanding production rather than simply executing repetitive functions.

Reduced friction equates to increased opportunity, for borrowers and mortgage professionals alike. If you remove that friction, and the underlying operational inefficiencies behind them, the home buying process will become much more fluid. A smoother, simpler process augmented by technology becomes one that can occur with more frequency throughout an individual’s life. And that opens the door to more innovation around products – loan products, technology products, credit products, and more – to support that increased frequency.

I would imagine that, much like was experienced with automated underwriting, a frictionless process will result in more loans being made, and more jobs for skilled professionals using AI-empowered tools. Not only that, but it will result in more jobs across the housing spectrum. All in all, we’re going to be looking at a much more interactive, more fruitful marketplace.

Q: The promise of AI in the mortgage industry seems incredible, but realistically, what sort of resources are required for an implementation of that level? 

SOOFI SAFAVI: The benefits of AI are not dictated by the size of an organization. In fact, mortgage industry players of all sizes can benefit from AI today. Black Knight’s own AI virtual assistant – AIVA – can be brought into an organization in much the same way as any other resource. An originator, or servicing shop, can “hire” AIVA to assist with specific functions or tasks. 

Much like any other employee, AIVA arrives for work with a certain skill set – it’s why she was hired in the first place. Of course, there is also an onboarding period where AIVA learns what is expected of her in this specific role, and is taught the specific process intricacies of a given organization, but after that, she is then deployed in the same way as any of the organizations other employees. And the skills she develops in the process become part of her knowledge base moving forward. 

Of course, at the enterprise level, when an organization’s operations reach across multiple verticals within the mortgage arena, the potential benefits increase exponentially. Rich, deep data is the fuel on which an AI runs, and the more data is available to AIVA, the more implementations become feasible.

But it’s important to stress that AIVA is not something that is only available to the largest lenders or servicers, but it is a resource that can be made available to organizations of all sizes. AIVA is as applicable in origination as she is in servicing, or in other facets of the industry.

Q: So an AIVA in every shop?

SOOFI SAFAVI: Let’s back up a bit, because I think this will be helpful in painting the entire picture. At the point of origination, a great deal of information is gathered on a prospective customer. That information, or some subset of it, is of use throughout the loan lifecycle – from application, through origination, settlement, closing, servicing, and if need be, modification or default management. All of these different players are gathering and processing information, and there is a great deal of overlap.

Each player in the mortgage process needs to receive a full file, and extract their own role-specific data from that document and then push it through their core system, to effectively complete their piece of the mortgage process. When you stop to think about it, for many of the players involved, roughly 60 percent of the information they need, or the calculations they make, mimic – or at least closely align with – activity the originator has already completed. 

Not only can an AI do that analysis and ascertain the 60 percent of information and analysis that has already been done, but it can go further. Rather than starting fresh each time, with the time and cost associated with these activities, those conclusions are presented by the AI, because it’s looking at the entire process holistically. Which adds to the unseen value. 

This points to the larger benefits of AI, its ability to learn. The more information an AI has at its disposal, and the more skills it is taught, the more places in the process it can add significant value. That same 60 percent share of information – perhaps more – that carries over from origination to, say, a title provider, also has value across the entirety of the loan lifecycle, to multiple players involved in the process.  

Not only can AI cut significant amounts of time from the process, it can also make data-based decision-making much more easily accessible for the all of the constituents involved. And that improves the process all across the board.

Q: Any final thoughts on AI in the mortgage industry, particularly for originators?

SOOFI SAFAVI: Black Knight’s first goal for AIVA is to drive down the cost to originate a loan by maximizing efficiencies and eliminating inefficiencies through the introduction of cognitive automation.

Whereas today’s workflow orchestration engines do a fantastic job of increasing efficiencies by alerting users to tasks that must be completed – a bank statement or paystub has arrived and is ready for review – AI can proactively evaluatethat document based upon its understanding of the mortgage lexicon. It leverages that expertise – which is continually expanding via machine learning – and a deeper understanding of associated data/behavior to see if there are any red flags or missing elements and inject a sense of urgency in getting those things addressed. 

Orchestration engines exist to help humans work more efficiently. The intent of AI, and particularly AIVA, is to help them to work less on mundane tasks so their capacity grows.  In short, work less, work better, by delegating some of the work to your virtual assistant. Then, the mortgage professional and his or her expertise shifts to verifying what the assistant has produced, providing the all-important human level of interaction our industry depends on.

Other goals focus on improving servicing functions and creating actionable intelligence, for improved efficiency across a company.

Ultimately, the name of the game is applied AI, not simply AI for AI’s sake. With applied AI, our goal is to bring cognitive automation to where the bulk of work and activity is happening. Our industry has come to accept a 45-day average mortgage cycle time, an $8500-$9500 average cost per loan, and the need for some 15-20 people having to touch a loan to get it through closing and beyond. What we’re trying to do is shake that acceptance and teach AIVA to automate the bulk of that work is being done. Again, rather than simply using AI for AI’s sake, we’re trying to introduce cognitive automation where it will have the biggest possible impact in terms of reducing cycle times and costs. And thatwill transform the mortgage industry.


Soofi Safavi thinks:

1: The industry will face a human resources challenge, as it will become increasingly difficult to entice the digital native college graduate who leaves her smart home to commute to work in her self-driving car into processing underwriting documents all day long. 

2: Technological innovation – and perhaps more importantly – adoption will continue to accelerate; expect more cutting-edge innovations. 

3: Along the same lines – much of the technological innovation in the mortgage industry has been on low-hanging fruit (point of sales systems, etc.); we will see innovators begin to tackle the more complicated parts of the process.


Previously CEO of HeavyWater Inc., the mortgage-focused Artificial Intelligence (AI) provider recently acquired by Black Knight, Inc, Soofi Safavi now serves as Managing Director of Black Knight’s Applied AI group, bringing leading-edge AI and computing capabilities to the Black Knight product portfolio. With over 20 years of experience in mortgage and banking technology, and deep expertise in IT strategy, architecture and machine learning, Soofi is uniquely suited to discuss AI’s role in the mortgage industry.

AI-Driven Lead Distribution For Mortgage Lending

ProPair, a mortgage-industry technology start-up based in Silicon Valley, launched an AI-based lead distribution solution that eliminates the uncertainty of the lead assignment process while optimizing results and ensuring fairness in the assignment process. Using artificial intelligence to correlate lead data with information about individual loan officers, ProPair facilitates the lead assignment process to allow lenders to distribute leads to maximize the performance of the entire loan team.

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ProPair is led by former mortgage industry executive, CEO Ethan Ewing and an engineering PhD, CTO Devon Johnson. The company uses AI-driven software to automatically match prospects and loan officers based on dozens of different factors. Capturing and analyzing multiple information sources provides a level of visibility not previously possible, making lenders more efficient and delivering better overall outcomes. ProPair also improves loan officer performance by assigning prospects based on the likelihood of success rather than seniority or guesswork. The result is more closes and a more fair system for all loan officers.

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“I am passionate about helping companies in the mortgage industry maximize the impact of their sales professionals,” explains Ewing. “By providing an AI-driven software solution to lenders massive data assets, we can replace gut instinct and spreadsheets with hard science and increase close rates and ROI in the process.”

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Designed with the everyday needs of lending organizations in mind, and optimized in conjunction with mortgage industry leaders, ProPair’s AI-based platform replaces outdated manual processes with data-driven lead assignments that improve productivity across the board. “ProPair has fundamentally changed how we look at our lead distribution methods,” says Dan Stevens, SVP Mortgage Strategy at NBKC Bank. “In the not too distant future, we will look back at the days before working with ProPair and wonder why we used gut feelings to make so many important decisions around our leads.”

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The Power Of Artificial Intelligence In The Mortgage Industry

These days, it’s hard to miss the buzz about artificial intelligence (AI) and its impact on industries such as health care, automotive, education, financial services and retail, to name just a few. From the ability to diagnose diseases – to the development of driverless cars – the potential applications of AI are extraordinary. In our daily lives, we already are experiencing the use of AI when we communicate with customer-service chat bots, ask Apple’s Siri for information, perform Google searches, or use navigation apps to help avoid traffic, as a few examples.

Despite all the recent discourse about AI, this technology is certainly not new. There are countless examples of AI use over the past several decades, including the reliance of commercial jet flights on AI to power autopilot, and internet bots that index web pages. But the more recent interest, innovation and investment in AI are due to a combination of factors – including greatly increased computational power, big data, greater infrastructure speed and scale, open source technologies and advancements in machine learning techniques.

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And today, the mortgage industry is able to reap the benefits of this incredible technology. For example, HeavyWater, which was recently acquired by Black Knight, is a provider of AI and machine learning-based capabilities specific to the financial services industry. The company has already built a platform that completes business tasks using synthetic read-and-comprehend analysis and conclusion skills, and applied these capabilities to the loan origination process.

What is Machine Learning?

The terms “machine learning” and “artificial intelligence” are often used interchangeably, however, there is a distinction between the two. Using a very broad definition, artificial intelligence replicates human reasoning through learning, problem-solving and pattern recognition. Machine learning is a subset of AI and is a process by which AI deepens its knowledge through continually performing tasks and processing information.

Let’s consider a simple, industry-specific example. AI-powered machine learning enables technology to “remember” standardized forms. For example, it can review thousands of paystubs and determine exactly where the pertinent income data is located. When the system comes across a paystub that presents an anomaly, it will apply its previously gained understanding to infer the location of the income data needed. Once the technology receives feedback that its inference was correct, it incorporates that information into its knowledge base. The next time it comes across that type of paystub, the system will automatically know where to find the pertinent data.

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Machine learning also leverages big data to gain insights. The more data that is collected and reviewed, the better machine learning solutions become at making predictions.

Applying AI and Machine Learning to Reduce Costs and Turn Times

AI and machine learning already can make a difference in two of the biggest challenges faced today by mortgage originators: costs and cycle times. With the ability to read, comprehend, and draw conclusions based on context, AI and machine learning can perform operational functions more efficiently and at scale.

In fact, machine learning can work on many of the labor-intensive, “stare and compare” tasks performed by humans – such as verifying income, assets and insurance coverage. Machine learning is used to perform these manual activities much faster and more accurately than humans – a task that takes employees hours to complete can be reduced to just seconds with machine learning.

By automating manual routines, machine learning not only expedites the origination process, but also increases volume. While humans can only work a certain number of hours before mistakes begin occurring, machine learning has no limits to the time or energy it can spend performing these tasks. By increasing loan processing volume and reducing mistakes, imagine how machine learning can drive down origination costs – and risk.

AI-powered systems enable processors and underwriters to dedicate more time to addressing exceptions and solving problems, which will improve transaction turn times. Also, AI can help avoid last-minute delays by prompting a lender’s staff to take early action when there is an issue, keeping the origination process moving forward. Additionally, by delegating work to AI-powered technology, a lender’s staff can focus on delivering a more positive and personalized consumer experience.

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As AI and machine learning are used to perform manual, repetitive tasks, allowing mortgage professionals to work on more value-added responsibilities, lenders can increase their focus on their company’s growth strategies. As they scale and reduce the cost per loan by keeping staffing levels flat, lenders can invest more in product development, marketing, infrastructure, and other growth-oriented initiatives.

Additional Applications of AI

AI can also leverage visual recognition to image and index a wide variety of documents that are typically reviewed by processors and underwriters, such as tax returns, W-2s, property titles and appraisals. A lender could even use AI and machine learning to better manage vendors. Based on past performance and cost, AI could provide recommendations on which vendors would be optimal for each loan going through the origination process.

Voice-integrated AI brings further opportunities to create efficiencies. This technology could look at information under review, evaluate results and automatically employ interactive communication bots to advise employees of any issue that may need attention. Additionally, via a conversational interface, processors and underwriters could ask for information they need – just as we use virtual assistants like Apple Siri, Amazon Alexa or Microsoft Cortana to get answers. These capabilities certainly could help move a loan through the origination process faster.

Leveraging AI to Enhance Customer Service

Of course, most of us have experienced first-hand how AI is applied in retail to deliver a more personalized consumer experience. For example, when we shop online, we receive targeted product recommendations the next time we visit that site; or receive faster service though chat bots.

To help personalize and enhance the borrower’s experience, lenders can leverage voice capabilities. A mortgage virtual assistant that engages customers by answering questions, walking them through the application process and even offering advice could be employed using voice-integrated AI.

Impact on Jobs

When the subject of AI in the workplace is discussed, it inevitably raises questions about its impact on jobs. Will jobs be lost as a result of these technological advancements?

There is no perfect answer to this question since the utilization of AI is different from company to company. But, it seems certain that future skill sets will be required to support this shifting technological paradigm. As it applies to the mortgage industry today, however, AI can enable professionals to spend less time on remedial work, becoming knowledge workers instead of task executors, and provide additional value to a company.

The Future Is Limitless

AI and machine learning offer tremendous potential to advance the mortgage industry, and we are just beginning to experience the technology’s capabilities. As AI-powered systems ingest more data and perform an increasing number of tasks though machine learning and other techniques, the possibilities are unlimited.

Imagine the power of AI as it learns to handle the entire point-of-sale process and speaks to an applicant directly through a mobile phone; or as it systematically searches a lender’s portfolio for qualified prospects and offers a customized home equity loan or line of credit, and so on. As we all know, the average cost to originate a mortgage loan is exceptionally high – today it is nearly $8,500 according to the Mortgage Bankers Association’s Quarterly Mortgage Bankers Performance Report, and the typical time to close a loan is 41 days. Any opportunities to reduce costs and increase process efficiencies will add value to lenders and consumes.

What’s more, the transformative power of AI doesn’t stop in the originations space. Servicers will also be able to reap the benefits of this advanced technology. For example, the technology could learn how to detect risk and any compliance issues before they occur, enhance loss mitigation decisioning, provide voice integration capabilities to help staff work faster and smarter, and so on. What’s amazing is that these examples only scratch the surface.

Of course, human interaction will always be needed to originate and service loans, as people will still decide how they want to leverage technology and determine the problems that must be solved. Humans must also still play an active role in loan decisioning, identifying which kind of data to consider and determining risk appetite. Furthermore, research indicates that despite all the advances in point-of-sale technology, consumers still want the comfort of human interaction at some point in the process of purchasing what is most likely their largest and most important investment.

AI and machine learning offer great promise and will likely usher in a new era of production excellence. Lenders that take advantage of this advanced technology will be choosing a bold new way to address origination costs, improve turn times and transform their origination processes to support a brighter, more successful future.

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Not All Banking Interactions Of The Future Will Be Handled By Bots

With Mastercard Inc. set to release its first-quarter financial results, and Finovate Spring 2018 approaching next week, fintech continues to lead the disruption charge in banking. US banks remain under pressure to stay ahead of what’s in store during the next wave of innovation, which is being led by AI advancements in the industry.

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Gartner’s research shows the global banking industry will spend $519 billion on IT in 2018, up 4.1 percent year over year from $499 billion in 2017. Features like chatbots are changing banks’ relationships with customers, and digital players are increasingly integrating social media to interact with clients.

But despite the increase in technology adoption in the sector, the need for human contact persists, with voice recognition and chatbot technology still in its infancy.

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Traditional banks must heavily invest in upgrading contact center environments to accommodate anticipated customer needs, in conjunction with embracing advanced technology platforms.

Banks can reap the benefits of AI and automation to improve interactions between staff and customers, says Intelenet Global Services, whose tech innovations for banks have seen them win recognition in the 2017 IDC Fintech Rankings.

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Tony Antenucci, VP of Banking, Financial Services and Insurance for Intelenet Global Services, comments: “The way customers interact with their bank in the U.S. is changing, as branches close down and more people switch to online banking, communicating through contact centers via phone or online if they need more help.

“Keeping up with new technology is vital to keep the processes behind these operations running smoothly and maintaining a relationship between banks and their customers. AI tools can help personalize banking for the customer, and automated processes free up staff to focus more on customer service and complex question resolution.

Tony continues: “For example, voice-recognition programs can automatically recognize an individual by their voice, predicting the subject of a call before the customer has to explain it. Each customer can be automatically sent to the right department, or the person they spoke to before, without having to be passed between contact staff.

“This dramatically improves the experience for the consumer. One leading bank was able to reduce the average handling time for customer calls by 40 percent.

“Banks will need to keep up this lead in innovative technology, to fend off growing competition for customers from Fintech challengers.”

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The Risks And Rewards Of Artificial Intelligence For Lenders

In looking at this, the recent debut of self-driving cars could transform a stressful commute into an opportunity to tackle emails and reading lists while making suburban long-distance travel great again. Americans are poised to gain more than 100 hours per year in free time by relinquishing the wheel to smart cars. The downside, though, lies in inevitable vulnerabilities like security threats, job loss, and environmental impact. Is the reward of AI worth the risk?

Artificial Intelligence is broadly defined as a computer’s ability to perform tasks normally requiring human intelligence. Ever since Alan Turing developed a test to determine a machine’s ability to exhibit intelligent behavior in 1950, roboticists and scientists have sought to pass it. In 2014, a computer program called “Eugene Goostman” succeeded by convincing 33% of the human judges that it, too, was human. Since then, companies like SAP, General Electric, and MasterCard have utilized machine learning and artificial intelligence (MLAI) to identify trends and insights, make predictions, and influence business decisions.

Artificial intelligence offers new opportunities to revolutionize operations in the financial services industry. Machine learning can process terabytes of data in seconds – volume which a horde of humans with older machines or methods could not process in a lifetime. The sheer power of modern computing in assessing an increasing number of variables with speed and accuracy gives a financial institution the ability to have rapid and strategic insights about customer behavior, reporting errors, and risk patterns. In the area of loan decisioning, this should lead to faster loan origination, fewer compliance problems with regulatory fines, and more inclusive lending overall.

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We wanted to know: What are the risks and rewards of AI in lending, and is it an inevitable next step for compliance management?

Bob Birmingham, CCO of ZestFinance, and Dr. Anurag Agarwal, President of RiskExec at Asurity Technologies, explore.

Which Discrimination Would You Prefer: Human Or Machine?

Anurag Agarwal: With machine learning, the decision-making is supposedly agnostic to overt biases. Humans, by definition, are free thinking but with biases that interfere with decision-making, especially in lending scenarios.

Bob Birmingham: AI is not a product but a solution, another approach to problem solving using advanced mathematics, analytics, and data. It’s not a magic fix but can remove some of the human discretion that leads to discrimination.

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AA: The problem is that computers could reinforce societal disadvantages by relying too heavily on data patterns. When a machine imports data, it creates an algorithm but cannot explain its methodology. It may introduce variations in the mix without understanding the broader implications.

Say you are trying to determine the risk of repayment for a borrower. The machine identifies physical appearance as a decision point. Based on data patterns, it detects that blue eyed individuals are more prone to timely repayment of loans. Noticing a correlation, it elevates that decision point to a higher influence in future lending. By taking an apparently agnostic data element and making a correlation it has in essence created discrimination because it doesn’t understand that blue eyes are mostly representative of Caucasians and the societal implications of making that borrower preference as a result.

BB: We’ve been here before as an industry. When regression modeling first came out it was confusing, flawed, and its accuracy questioned. At the end of the day, it was actually a more accurate process than purely judgmental underwriting because it followed a clear set of instructions to find answers. MLAI creates an opportunity for continued improvement to mitigate and eliminate discriminatory lending practices.

Risk: Machines can learn the wrong lessons from data.

Reward: Machines don’t have overt biases.

Alternative Data

AA: AI is very new. We don’t yet know how to regulate it or what its long-term impact is. As soon as data began generating, we followed this “go forth and multiply” pattern. Now we have more data than we know what to do with. Also, a lot of this data is unstructured.

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BB: Alternative modeling and alternative data are often lumped together, but that shouldn’t always be the case. MLAI alternative modeling may be used on the same data that current regression models run on and provide lift. Alternative data may be used with your existing models too but is more often used in conjunction with alternative modeling due to MLAI’s ability to handle large data sets. MLAI also has the ability to identify missing, erroneous, or wrong data and solve problems related to the inaccuracies.

AA: Alternative data, such as Facebook profiles, Twitter feeds, and LinkedIn pages, are already used in employment. HR departments use this publicly available information to determine if you are a qualified candidate. By knowing this in advance, you can change your online behavior to skew these data points in your favor. That’s the big question with alternative data in the lending space, along with privacy and transparency issues. We don’t know who gets to manipulate the data, where it comes from, or who has access to it. There’s no established system or standardized data points, which means most companies must follow their own proprietary lending rules, making it a regulatory free-for-all.

BB: While there are caveats, the rewards significantly outweigh the risks. There are many underserved individuals, businesses, and micro-borrowers with little or no credit that could benefit from alternative data. If an applicant doesn’t have good credit, or any credit at all, lenders can use MLAI techniques to paint a richer portrait about the borrower’s reliability using nontraditional factors like e-commerce histories, phone bills, and purchasing records. MLAI can open up the credit market, measure patterns, and fill in the data gaps, giving lenders a more holistic view of an applicant. Alternative data doesn’t have to be “creepy” data and doesn’t have to be social media data. Responsible and transparent use of alternative data to expand access should be encouraged.

Risk: The use of alternative data in credit underwriting raises privacy, transparency, and data integrity issues.

Reward: Alternative data can increase financial inclusion by granting access to capital to individuals and businesses with no traditional credit history.

Hackers vs. Trackers

AA: As we saw with Equifax, any time data is automated through the cloud there is the risk of data hacking. Now we are asking: who is responsible for protecting all of this data? Using such a detailed lending profile increases the necessity for data privacy and security.

BB: These are risks but the industry has always been responsible for protecting sensitive data.  The good news is, the more information we have about a borrower, the quicker we can identify errors and anomalous behavior. This is a great consumer benefit. Using the same Equifax example, imagine if we could say “this person was affected, and these actions are very different from their previous activity. This is a red flag that their information was stolen.”

Banks raise red flags when uncharacteristically large payments are made or a card is used in a different country, but imagine how much more effective these alert systems could be with additional insights into an individual’s unique behavior patterns.

Risk: More data in the cloud means a higher risk to consumers in the event of a data hack.

Reward: Additional data can identify unusual behavior quicker, which is a benefit to consumers.

The Regulator’s Dilemma

AA: Regulators have a big job ahead of them figuring out how to regulate companies using this information. Many of these companies aren’t sure how to use it themselves.

BB: Initially, users of MLAI in high stakes applications have an obligation to educate the public, create a set of best practices for their industries, and be transparent. Financial inclusion is something regulators support and this technology can help lower the barriers to entry.

AA: In late 2017,the CFPB issued a no-action letter to Upstart Network, an online lending platform that uses both traditional and alternative data to evaluate consumer loan applications. The terms of the letter required Upstart to share data with the CFPB about its decision-making processes, consumer risk mitigation, and methods for expanding access to credit for traditionally underserved populations.

By studying these companies, regulators can better understand the impact on credit in general, on traditionally underserved populations, and on the application of compliance management systems.

Risk: Regulators are still learning about AI and how to properly monitor it.

Rewards: Regulators support consumers and want to make access to credit more inclusive.

The elephant in the room: Jobs.

BB: Typically, Financial Investigative Units (FIUs) are looking at alerts 24/7, researching a person, tracking where money is going, and determining if they should file a suspicious activity report. It’s the banks biggest compliance cost and their highest area of employee attrition with numbers ranging from 15 – 35% turnover in the FIUs. With AI, FIUs can focus on stopping financial crimes rather than toggling back and forth between 15 screens and parcelling through tons of information. This should free compliance personnel up to do higher value, more rewarding work in addition to driving more efficient outcomes for their organizations.

AA: I prefer a human loan officer over an automated machine-learning system. If something happened six months ago that caused your credit to go down, you can explain that to a loan officer. How can you convey that to an automated system? Those intangibles make human interaction necessary. I believe there is something valuable in interpersonal interactions that can never be captured in a truly automated fashion.

Risk: Headcounts in certain departments that are reliant on manual processes could decrease.

Reward: New and more rewarding job responsibilities will result in less turnover, but ultimately there is no replacement for interpersonal interaction for certain positions.

Closing Arguments

BB: Currently, the industry operates with a “look back” approach for Fair Lending, which doesn’t really work. The whole process of defending and explaining discrimination after a model is put into production feels outdated. Today, we can and should build AI models to proactively remove discrimination before ever putting a model into production.

AA: This technology is still very new. Medium to big size lenders should let the technology emerge and wait to see what the regulatory landscape looks like. Regulators still have a ways to go before any of us will fully understand what the future looks like for AI in the lending space.