Author:
Nexval Infotech
Artificial intelligence (AI) in mortgage adoption is growing. According to a 2022 survey, 49% of mortgage companies expect AI to help reduce headcount; 20% said that mortgage AI/ML would significantly trim their payroll. Indeed, the technology has widespread applications in financial services, from enabling more intuitive mortgage RPA to improving borrower experiences.
What’s more, advancements in mortgage AI/ML show no signs of stopping. From basic data analysis, we have progressed towards ideas like individual artificial intelligence (a combination of machine algorithms and human brain synapses) and adaptive AI. These can perform much more than high-volume data processing and predictions. Adaptive AI, in particular, has the potential to be the defining mortgage technology of our time due to its incredible flexibility.
What is Adaptive AI?
Adaptive AI is defined as an artificial intelligence-based decision-making framework that can ingest new data at runtime and quickly make new decisions that adjust to the most immediate and relevant issues. It operates on the same principles as decision intelligence — but the only difference is its ability to take in new data feeds at the time of the AI program’s execution.
Adaptive AI was mainly popularized by Gartner in 2022, as part of its top 12 data and analytics trends for business leaders. These are specifically designed to help you “anticipate change and manage uncertainty,” notes Gartner, in the context of a post-pandemic world, recession, and inflationary pressures, and geopolitical tensions.
AI was always widely used in the financial services sector to make sense of market intelligence and scale. Mortgage AI/ML performs a number of critical functions, like automating the underwriting process, creating new loan products, and personalizing CX.
In mortgage, robotic process automation (RPA) applies simple bots to replace human workers in repetitive tasks. Mortgage AI/ML makes RPA smarter by giving bots human-like capabilities, such as reading unstructured data and coping with exceptions. Now that we are in the era of adaptive AI, these technologies will evolve further.
Notable Adaptive AI Use Cases in Mortgage
Even as adaptive AI is classified as an emerging technology, several platforms are already taking advantage of this idea. Essentially, it comprises high-velocity data streaming into the AI model so that it is constantly trained on the latest data set for optimal decision-making. Some of its key uses cases in the mortgage industry include:
1. Stay ahead of model risk deterioration
An important use case for mortgage AI/ML is credit risk modeling. These are data models that calculate and predict borrower risk based on countless financial variables. Over time, these models tend to deteriorate, and lenders must look out for signs of dipping performance. Credit risk models need to be redeveloped (and retrained) with new data, which can take several months or even a year. In the meantime, the mortgage provider must make do with a suboptimal model.
Adaptive AI changes all this. It can dynamically process new and incoming data to adjust to real-world conditions. Using adaptive AI, you can have a new credit risk model up and running in less than a month, thereby minimizing the risk exposure for your business.
Read more: 7 Factors to Consider When Choosing a Risk Management Platform
2. Take on new customers
When it comes to underwriting, adaptive AI tends to be more discerning, fair, and objective than traditional mortgage AI/ML. It can look for signs of financial health that would be otherwise ignored, so you can widen your borrower base without taking on added risk.
For example, a traditional system may reject an applicant who recently lost their 9-to-5 employment. But an adaptive AI system will know that the individual earns a regular, steady income from unconventional sources like freelancing, cryptocurrency trading, etc. Lenders can unlock new revenue streams in this manner, while borrowers also receive a better experience, in line with their actual financial status and expectations.
3. Detect financial fraud with greater accuracy
A massive challenge with fraud detection is that criminals are constantly changing their tactics. For example, a financial institution might flag transactions taking place during a certain kind of day as anomalous — fraudsters will get wind of this traditional mortgage AI/ML rule in no time and find ways to bypass it.
On the other hand, adaptive AI can learn from changing fraudulent behavior at scale. It can scan a variety of third-party databases, and threat intelligence feeds to stay a step ahead. New fraud patterns that would typically be overlooked for weeks or months are now accommodated into mortgage AI/ML models in a matter of days.
4. Improve conversational CX for borrowers
Chatbots and conversational experiences are now a staple application of mortgage AI/ML, but their capabilities are often limited. Chatbots are connected to various data sources, and if they are not updated dynamically, they will respond with outdated information. Traditional chatbots also find it difficult to deal with new customer demographics and unexpected queries.
In contrast, chatbots powered by adaptive AI are continually trained on millions of interactions taking place across geographies, products, and demographics. They also update in near-real-time whenever the data source changes. As a result, borrowers always get a prompt and accurate response to their mortgage-related queries with a much lower failure rate.
Human mortgage executives also have fewer queues to deal with manually, since most issues are automatically resolved through adaptive AI.
Read more: Strategies to Improve the Customer Experience of your Mortgage Services
Starting Your AI Journey
Mortgage RPA and Adaptive AI are among the top technology trends for 2023. Mortgage RPA represents high backend efficiency with low upfront costs, while adaptive AI is the next generation of machine intelligence evolution. The journey toward adaptive AI begins with two pillars: the digitization of mortgage systems and robust data sources. Existing systems like business analytics and data fabrics will prove instrumental for lenders looking to invest in mortgage AI/ML and adaptive AI.
At Nexval, our team of 1000+ SMEs builds tailored tech solutions, backed by years of mortgage industry experience. To know how adaptive AI can propel your 2023 roadmap, talk to our Tech Gurus today.