Imagine a mortgage process where the barriers for available and emerging technologies are removed. Interactions with consumers happen in real-time, minimizing the amount of work they must do and maximizing their satisfaction with the experience.
“As challenging as it is to keep pace with innovation and the speed with which new technologies are emerging, it is imperative for mortgage companies to stay a step ahead.”
The automation of verification services can be customized to suit your needs and specifications — not someone else’s — so data can be turned into meaningful insights. Human involvement coexists with technology, but only when it is preferred or necessary to ensure the experience is positive and compliant loans are properly processed.
Well, you no longer need to imagine such a process. All of this is possible with today’s emerging technologies. These innovations are in many ways replacing manual processes, something that can positively impact the mortgage process going forward — specifically when it comes to artificial intelligence (AI) and machine learning applications.
Sensible practices
To incorporate more technologies into today’s workflows, it is necessary to revisit why certain procedures and processes were adopted in the first place. Looking back over the decades, it has become apparent that some so-called rules are no longer applicable or even justifiable. Many have been passed down merely as a matter of routine.
To overcome this, the mortgage industry must continuously review and challenge the status quo and uncover innovative ways to apply emerging technologies. The end goal is to enable greater customization and the ability to change preferences, allowing lenders to minimize expenses and maximize profitability — all while enhancing the borrower experience.
In the quest to advance the modern mortgage, there are several manual areas that might justify a technology upgrade. For instance, the amount of time and labor that is spent on contacting borrowers and creditors to verify payments could be significantly shortened by applying technology. Automated tools easily allow applicants to upload documentation. It has transformed a process that caused a tremendous drag on turn times to one that now enables lenders to speed up loan closings.
Repetitive processes such as entering the same borrower information across multiple platforms can be eliminated with AI and machine learning tools. This also can greatly reduce errors. These tools often feature intuitive designs making it easier for all users to navigate, understand and complete their work without extensive training.
By using predictive modeling, lenders can learn about applicants’ financial habits, spending and saving patterns, etc. And technology can more effectively expand how income and employment are validated such as through paystubs. By working closely with lenders, the right partner can identify areas that can be vastly improved in terms of creating greater efficiencies and streamlining workflows.
As technology continues to evolve so, too, will the mortgage process. The key is to use it where it makes sense — and where it makes a positive and significant difference — and not to use it when human intervention is the better option.
Predictive insights
The adoption of AI has taken off at a pace unlike anything before in the past 30 years except for the internet, which AI is expected to catapult over in terms of its overall impact. The Stratmor Group reported in June in a survey that 22% of lenders have already started to use AI, but mostly for marketing instead of operations or fulfillment.
The mortgage industry isn’t the only one dipping its toe into AI waters. The clip at which large language models, such as ChatGPT, are already disrupting processes across numerous industries is staggering. What is particularly interesting is the enormous focus being placed on the “chat” aspect of GPT. This technology will allow tech companies to gain actionable insights through its ability to categorize data that matches certain patterns, and then assigns a confidence level to the output.
When you can put data through an AI model and it tells you, with a certain degree of confidence, what your next best action(s) should be, that is going to significantly impact businesses. For example, this should help lenders identify what action to take regarding specific compliance-related issues or identify loans that should be removed from the pipeline. This will be a game changer.
Increasingly, AI is also being used to gain predictive insights into operational performance and lending challenges, especially around fraud prevention. AI and machine learning models are being used to build a sophisticated benchmarking tool that will allow lenders who opt in to compare themselves to other, like-sized competitors in various areas — average FICO score, close rate, overlayed with unemployment and federal lending rate data, etc. There is a massive amount of industry data out there from which lenders can glean important insights, and work is being done to anonymize it so that it is not traceable to individual borrowers.
Objective decisions
By incorporating an unbiased approach with AI and embedding it right into a lender’s workflow, technology, or process, objective decisions can be reached more easily and confidently. Early adopters of this technology across the mortgage industry are planning to use it in some very specific ways.
For instance, AI and machine learning are exceptionally good at analyzing and verifying data and images in documents — so much so that the days of “stare and compare” between loan documents will soon be a thing of the past. Their capabilities go way beyond those of optical character recognition (OCR) technology. Machine learning is much more advanced, has a higher accuracy rate and works to compare data in ways the human brain can’t even comprehend. Machine learning is also very good at uncovering trends and patterns that are not easily identifiable for humans and can conduct these analyses over vast amounts of data.
ChatGPT AI technology is also being used by companies in closed-source environments (meaning private) to train chatbots on their operating procedures. Some are also working on offering live chat that will allow prospects and clients to actively chat online with someone within their organization.
AI will be used in ways that will improve employee output. For example, when a consumer is on a website and types in a question — AI can respond with several items pertinent to that question that the consumer can review, or the consumer can request to chat with a representative. This AI-infused process can enable reps to speak with and serve three to four borrowers at once.
Fraud detection is another important area for the use of AI and machine learning. Companies using AI in cybersecurity respond to data breaches faster and saved, on average, nearly $1.8 million compared to companies that don’t use AI, according to a report last year from IBM. In fact, in terms of cybersecurity, AI is already being used on email platforms to help identify whether a bad actor is sending an email. Soon, an AI model will be trained on millions of loans — some will be identified as fraud; the rest will be identified as legitimate — and then lenders will be able to feed each loan into that model to see if it matches a fraudulent loan pattern.
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Though the industry has made great strides in advancing the modern mortgage, there is more work to be done. As challenging as it is to keep pace with innovation and the speed with which new technologies are emerging, it is imperative for mortgage companies to stay a step ahead and be willing to incorporate new tools where and when they make sense.
This can best be accomplished by working with the right partner — one who understands today’s technologies and how to best apply them to your business. By doing so, workflows will be streamlined, greater efficiencies will be realized and profitability will be enhanced. What was once unimaginable — a mortgage process without technological barriers, with real-time consumer interactions and customizable, automated verifications, where human involvement coexists with technology — is finally becoming a reality. ●
