When, Where, And How To Incorporate AI Into Your Mortgage Business

The impacts and future implications of artificial intelligence and generative AI

Incorporate AI

Artificial vs. Automated Intelligence

For example, we asked the management team of an AI-focused start-up a simple question: “Why had the company been using the term “Automated Intelligence” in their older marketing materials rather than the more recent term, “AI” (Artificial Intelligence)?”

Management’s response was that most of their early customers were entirely unfamiliar with the term AI and using the word “automated” was an easier way to frame their technology solution. From a marketing perspective, that made good sense. But if management begins to look at AI as just a better mousetrap for mortgage automation, they are likely to miss out on much of what the technology offers.

Due to the unprecedented pace at which AI is evolving, our view is that it is essential to shift from reacting to the FOMO (Fear Of Missing Out) flavor of the quarter, such as, ”We need to integrate with ChatGPT (or its equivalent),” to a robust framework for AI deployment which incorporates point solutions, as appropriate.

Differences Among Automation, Artificial Intelligence, and Generative AI

Automation uses computers and technology to raise human productivity by performing well-understood, manually repetitive tasks.

Automation is generally based on pre-defined and coded “if this condition is met, then take that action” types of rules on the input data. Any changes to the automated process or inputs require some level of rework on the automation rule set. For example, a mortgage application form that has an address written in a format that doesn’t meet the programmed rule set is kicked out as an exception for a human to resolve.

One of the most significant implementations of automation at a broad scale in the mortgage industry was the launch and adoption of the automated underwriting systems (AUS) with Fannie Mae and Freddie Mac in the late 1990s. Desktop Underwriter and Loan Product Advisor provided a level of automation that had not yet been seen and enabled significant efficiencies in the industry by reducing manual effort in the underwriting process.

Artificial Intelligence (AI), in simplified terms, can go further than automation by mimicking the problem-solving and decision-making capabilities that humans use in a particular space, such as mortgage processing. That means AI can solve problems that require some level of cognition. This capability is more powerful than automation.

Machine Learning (ML) is a subset of AI that solves problems by first digesting vast data sets within a specific area (e.g., recognizing cats in images) and creating its own internal mathematical model to “understand” the data set. In this step, known as “training,” experienced developers work with the AI/ML technology to shape the mathematical model to understand and analyze a range of input data relevant to the problem area.

In the cat example, the developers shape the model by iteratively teaching it the difference between “good” vs. “bad” results until the model produces relevant results that are as good or better than a human is likely to produce. The “learning” aspect is when the underlying model adapts and improves over time with little to no human intervention. In the cat example, the model, as it learns, should be able to recognize cats in previously unseen images.

In mortgage, lenders have traditionally applied ML to solve task-specific problems. One use case is classifying, indexing, and extracting essential data from loan documents/data. The inputs can come from various structured data (e.g., different types of written forms where fields for date, name, address, etc., may not be in the same place on the form) and/or unstructured data (e.g., emails). This use case is an example of where ML shines compared to rules-based automation, as with supervision, the ML-based solution can learn from each exception so that it automatically handles it the next time it is encountered.

Generative AI

David Karandish, CEO of Capacity, simplifies the different approaches into the following three categories:

  • Automation is analogous to ”doing” — the solution executes one or more manual tasks more efficiently
  • Machine Learning is analogous to ”thinking” — the solution displays cognitive capabilities to raise both efficiency and effectiveness of the task(s) at hand
  • Generative AI is analogous to ”creating” — the solution displays the ability to be creative by synthesizing what it has learned often into net new output

Generative AI simulates human creativity and is based on an advanced form of ML, called Deep Learning. For example, based on natural language narrative as its input, Generative AI has the ability to create novel/new content such as poems in text form or synthetic art in the form of an image, read a written speech in a voice and tone that is indistinguishable from a real-life personality, and even write programming code — hence the term “generative,” as it is capable of creating (generating) output based on directions (e.g., a narrative) it has not received before.

Implications of artificial intelligence

There are different types of Generative AI technologies. The two currently making headlines are Large Language Models (LLMs) used for natural language applications and Diffusion Models for image-based applications. These models are orders of magnitude more mathematically and computationally complex than previous ML models. They are typically self-trained on a massive spectrum and volume of data. For example, LLMs such as ChatGPT, BARD, Llama, or MosaicML are trained on years of historical internet content, libraries of books, industry journals, etc. As a result, the base knowledge set from which they can draw is not limited to a specific domain and, therefore, they have broad applicability.

Applications of Generative AI

Advances in Generative AI enable us to go from structured data and human-supervised ML models, to unstructured data and unsupervised ML models that produce ”human-like” outputs. There is a rising volume of companies touting the use of Generative AI across various areas within mortgage. As we dig a level deeper though, we see relatively limited adoption by IMBs, banks, and credit unions. Regulation, compliance, agency guidelines, and a deep enough understanding of the capabilities and risks of Generative AI are some of the impediments to more rapid adoption.

In addition, there are technical downsides at this time that limit adoption:

  1. Data, Integrations, and Cost of Inputs: Generative AI solutions require extensive data inputs, expensive, high-powered specialized computing (known as Graphical Processing Units or GPUs), and integrations to develop and tune the models and then execute queries over the these models;
  2. Quality: Generative AI may respond to queries with truly creative answers, i.e., not based on factual data (a situation known as “hallucination”), and
  3. Predictability: the output generated may or may not be deterministic, i.e., an identical query may or may not produce an identical answer each time. However, performance improvements on these dimensions are moving at unprecedented speed — hence, we expect increased absorption into applications and a steep adoption curve of those applications in the near future.

One of the more significant issues, however, is that Generative AI is effectively a “black box” of algorithms. The mathematics and logic of the underlying models cannot be deciphered definitively today. The inability to comprehend and, therefore, explain the workings of the underlying Generative AI models is recognized by the technical/scientific community but, as of today, it remains a hard problem.

The above context explains why in the mortgage and real estate sectors, the application of Generative AI has largely been to assist the creative process, marketing, and customer service bots and to inspire humans with prompts through platforms like ChatGPT.

Today, we see use of Generative AI to create blog content for Search Engine Optimization (SEO), ad copy for Facebook and LinkedIn ads, email content, thought-leadership pieces, and website and landing page generation — all of which are often advertising and marketing focused applications. Generative AI is also being used to assist developers with coding, data scientists, and marketers to analyze results, and management to write company newsletters. In all these cases, it is being integrated into business processes to increase and raise the bar on productivity and creativity, but not yet displace humans.

Will Generative AI Rule The Mortgage World?

At this point, broadly applying Generative AI to disrupt business models or completely reinvent end-to-end business processes, such as mortgage underwriting, will require the ability to address the black box problem, reduce hallucinations, and remove elements of learning bias, all of which can produce unintended consequences, as well as reduced cost of specialized computing. As we recently saw with the Consumer Financial Protection Bureau (CFPB) issuing guidance on credit denials saying that creditors “must be able to specifically explain their reasons for denial,” the industry is not there yet.

Over the next few years, due to the current state of the technology and the industry impediments we have laid out, we envision an evolutionary (not revolutionary) adoption curve for Generative AI across the mortgage and real estate industry. We also anticipate the majority of the adoption of these technologies will be through vertical-specific applications by technology companies already operating in the mortgage and real estate ecosystem.

As we envision the potential that Generative AI offers in the longer term, we are strong believers in the possibilities it presents to design and deploy disruptive business and operating models across the mortgage and real estate industries, but it will require bridging the knowledge gap and dedicated investment in these technologies.

This article was originally published in the NMP Magazine April 2024 issue.
About the authors
Managing Director
Chris Bixby is managing director of Venture Capital Strategies of Rice Park Capital Management (“Rice Park”), a private investment firm managing capital of institutional investors, family offices and high net worth…
Ajit Prabhu is an advisor to Rice Park Capital Venture Capital Strategies.
Published on
Mar 28, 2024
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