Loan Officer

Using Big Data for Big Wins

5 mins read
October 23, 2017
By
Total Expert

What if you could make contact with a customer at the precise time that person was thinking about doing something that involved your services? What if you could multiply the referral business from your portfolio of past clients by three to four times?  

With the tools, data points and integrations available today that are constantly growing more powerful and precise, the answer to both questions is: “It’s entirely possible.” To thrive – not simply survive – in today’s industry requires understanding and awareness of our business landscape: Consumers are being marketed to at an alarming rate.  

Mortgage and real estate professionals can no longer send emails and postcards by rote and expect to remain relevant. Lenders and mortgage loan officers (MLOs) with robust post-close marketing programs who are consistent in their client follow-up can’t assume that they will come to mind when consumers are ready to do business.  

The speed of technological development – and the nearly instant affect it has on consumer demands – have reduced what were formerly robust marketing plans and efforts to mere basics in danger of being tuned out or ignored altogether. Automation and “big data” will help you stay in front of people and connect with them when they are considering something they want or need that involves your services, and help you avoid getting lost in the masses of marketing clutter coming at your coveted clients every day.  

Using Big Data Effectively

Using big data effectively – or at all – requires a clean database. This can be challenging for MLOs who have been at multiple companies that have not unified their marketing systems and tools. However, it’s critical to have a data warehouse or CRM set up and in use.  

Once you have your data in place, you can set various triggers to alert you when your contacts are engaged in various activities and integrate services that will allow you to overlay supplemental data to generate opportunities. Depending on how your technology stack is set up, you may get a notification that someone in your database just filled out a mortgage application that resulted in an inquiry. This person could be considering a move up or down, or a refinance, making it an ideal time to reach out in a personal way via phone, text or video.  

Used properly, big data is far less labor intensive and more reliable than analyzing a database by hand, sorting through past clients and choosing to contact people based on the number of years since a customer has moved or done a refinance.

Keeping Your Communications Customized and Personalized

Keep in mind that consumers still want a relationship when completing a complex financial transaction. Communication based on big data alerts must be easy and natural – as though you just happened to be thinking of someone.  

Calling and saying, “Hello – I just saw that your credit was pulled, is there anything I can help you with?” will make people feel data-mined and uncomfortable. Blindly dialing your way through your database and calling “just to see how people are doing” can also be annoying. But current, relevant market information such as home appreciation rates, interest rate comparisons and reasons why an equity review might be helpful can be a welcome notification of an opportunity a consumer might not otherwise know was available.  

Using big data to connect with clients at the right time and place of decision-making is not a substitute for proper, human-to-human relationship management, but it is an extra layer of insurance that can boost your production.  

Automated big data is not an excuse to be lazy. In fact, it should empower you to increase production. MLOs who have been in the business for five to ten years know there is a level of predictability within a past customer database. Adding automation allows companies and producers to achieve an upswing in efficiency with data intelligence.  

Setting MLOs Up for Success at the Organizational Level

Ensuring past client retention and new client capture using a combination of technology and personal outreach requires tools put in place at the organizational level that set MLOs up to win. The right combination of structure, services and APIs will put MLOs in front of people at the moment of consideration and decision, which is exponentially more profitable than random, generic marketing.  

The key to winning with big data is to make sure you use it effectively as a supplement to other things you’re already consistently doing to maintain client relationships. That’s the difference between offensive/ intrusive communication and that which is welcomed (and extremely profitable). Don’t replace personalization – enhance it and add efficiency. If you do it right, you’ll win customers for life.

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AI is no longer a future state—it’s already here, embedded in everything from ride-sharing apps and food service to factories and farms. In the world of financial services, though, this ubiquity comes with pressure to integrate AI fast, appear innovative, and keep up with competitors—all while being mindful of evolving federal and state compliance requirements. Moving fast without a plan or awareness of up and downstream implications often leads to AI-enabled solutions that either underdeliver or don’t deliver at all.

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Where enterprise AI goes wrong

Too many financial services leaders have experienced what I call “AI failure to launch (and scale).” They’ve rushed to try unintegrated AI-enable offerings and bolt on AI tools—often generalist chatbots, white-labeled versions of generative tools, and/or hooking up to MCP servers—without a clear sense of how these tools will solve their business problems or add potential risk. The result? The occasional value-add result. However, what we see more is poor user adoption, wasted spend, and limited impact.

This is the same trap we saw with “digital transformation” a decade ago, or the original horizontal SaaS applications that evolved or were replaced by vertical-specific solutions. AI-enabled solutions offer tremendous, generational promise but they risk becoming vanity-first, value-later tools. We are focused on the former.

AI that thinks and adapts: Welcome to agentic AI

Let’s make one thing clear: not all AI is created equal.  

Chatbots have been commonplace in financial services for a decade now, but remain rigid, rule-based tools that handle repetitive tasks.  I’ve worked with “AI” services for more than 15 years and each had their own place and potential when used properly. Herein lies the opportunity. Modern lenders that are focused on retaining and growing their customers in an ultra-competitive market need something more dynamic. Enter AI agents that can understand context, adapt on the fly, and speak in a human-like way. These agents are coachable, brand-aware, and learn from every interaction. They don’t follow scripts—they think in real time. And when built correctly, they become a seamless part of your customer experience.

This is the evolution from AI as a support function to AI as a trusted team member.

Total Expert recently launched an AI Sales Assistant that puts this principle into action. It functions as a scalable, intelligent teammate—able to engage leads, deliver personalized conversations, and identify high-potential opportunities—all while staying aligned with your brand voice and compliance requirements. It’s not a chatbot bolted onto a CRM—it’s a fully integrated AI-enabled solution, utilizing data, embedding within workflow orchestration, and playing nice with application logic because it has the necessary context to work within your lending ecosystem.

The real “why” behind AI adoption

Before choosing any AI solution, or any technology solution, financial services firms must ask themselves: What business problem are we solving?

For example, when mortgage rates dropped for a few weeks in September 2024, our customer intelligence capabilities identified nearly $2 billion in immediate refinance opportunities. But no team of loan officers could scale quickly enough to reach every qualified lead. That’s where AI tools prove invaluable—automating first-touch outreach at scale, surfacing the best opportunities, and empowering human teams to scale up execution to drive retention and growth.

Why embedded beats bolted-on

The types of AI-enabled solutions we are talking about can’t function effectively in isolation. Without access to timely and accurate customer data, and invoked within a specific workflow process, it can’t personalize interactions, anticipate needs, or drive conversions at the right time.

Picture an AI assistant offering a refinance to a customer, only to stall when asked for more details. If it doesn’t know the customer’s current rate or financial profile, the experience feels hollow. That’s not just ineffective—it damages trust.

By contrast, when AI-enabled solutions are embedded within a unified customer experience platform like Total Expert, it draws on a 360-degree view of the customer. It knows the data, understands the history, and delivers contextually rich conversations that convert.

This is why we’re designing our AI capabilities with a focus on the unique needs of financial services organizations. The same purpose-built approach has earned the Total Expert platform its unmatched reputation for usability and time to value.

Generalist AI offerings can be a gamble that increase costs—and time to value

Implementing AI that’s not purpose-built for financial services introduces two major risks:

1. Usability failure: Your team must spend months customizing and configuring a generalist AI tool to make it work for your specific needs—if it will ever work at all. For example, imagine you’re a loan officer and one of your referral partners introduces you to a borrower. Now, you have to choose the best way to approach the first conversation with this borrower. There are countless permutations of questions and answers which all require deep personalization, compliance awareness, and consistent representation of the sales processes and brand tone of the lender. Generalist AIs will quickly reach their limitations in these complex use cases.

An industry-focused AI offering will be trained on this specific use case and provided with the context needed to hold a dynamic conversation with the borrower. This type of AI learns and adapts with each interaction, performing the most time-consuming tasks so you don’t have to.    

2. Compliance risk: Without built-in industry guardrails, you’re gambling with regulatory violations and brand safety.  As we know, the compliance landscape for financial services is broad and evolving at the federal and state level.  Look for AI offerings that are regulatory aware and enable you to configure them based on your organization’s risk tolerance and interpretations.

Lenders don’t need more tools—they need the right tools—ones that work out of the box, understand industry nuances, and deliver immediate, compliant value.

Ask these questions before you commit to an AI offering  

To maximize the probability of success, here’s a quick checklist for vetting solutions:

  • Can it solve a real, high-value business problem, and how? Review specific examples and ask to speak with other organizations that have implemented the tool.
  • Does it function as a true AI agent, not a static bot?
  • Can it be deeply integrated into your core system(s), workflow orchestration, and data?
  • Does it include financial industry compliance and brand guardrails?
  • Can it scale without sacrificing quality or regulatory integrity?

Building the future with purpose-built AI

Total Expert has always designed technology with financial services in mind, and our approach to utilizing AI is no different. We’re not chasing hype. We’re solving problems.

Our focus on AI isn’t simply building standalone features—it’s about embedded, intelligent, and deeply integrated AI solutions. It’s helping lenders scale smarter, engage more meaningfully, and turn data into action. Our AI Sales Assistant is just the beginning—an example of how purpose-built, AI-enabled solutions can solve real problems and deliver tangible value. We are already testing and exploring other AI-enabled solutions and I could not be more excited about the current and potential value our clients and our market will achieve.

Because when AI works, it’s not just impressive—it’s indispensable.

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