In the movie “Moneyball,” Billy Beane’s character tells a player, “You get on base, we win. You don’t, we lose.” He could have been talking about mortgage lending. Like baseball, if you don’t get on first base with borrowers, you’re out of the game, losing the business to another lender.
Just like baseball, there is plenty of data out there to tell management exactly how their loan officers are performing. Most just haven’t been using it.
Mortgage executives with decades of experience have a feel for the players on their team. They know their strengths, where they may fall short and ultimately how they would perform during the accounting period.
How many branch managers have that experience and expertise today — that “boots on the ground” leadership? How can an executive get it without years in the business? You can’t go down to the corner store and buy more experience. But you can invest in business intelligence.
Information overload
In the past, experience was one of the most valuable qualities of a leader. Over time, statisticians discovered that clues to future business performance could be found in the company’s data, and a new discipline arose.
Business intelligence, or BI, refers to the technology, applications and practices used to collect, integrate, analyze and present business information. Its main purpose is to support better decision-making within organizations. The primary goal of these systems is to transform raw data into meaningful insights to help business leaders understand trends, patterns and relationships within their operations.
That’s where it has fallen short. The rise of big data, cloud computing and artificial intelligence have revolutionized BI. The explosion of data from the “internet of things” devices — computing embedded in everyday devices to collect and send data — and digital platforms has led to the adoption of advanced analytics techniques, such as machine learning and predictive analytics.
Even with all of this, leaders have fallen prey to the overwhelming volumes of data these tools are dropping on their conference room tables. Making the next best decision is harder than ever because there is always some data point that warns against any decision a leader contemplates.
Actionable insights
As early as 2011, McKinsey & Company researchers were warning that information overload was a serious threat to business success. In an article entitled, “Recovering from information overload,” Derek Dean and Caroline Webb wrote:
“For all the benefits of the information technology and communications revolution, it has a well-known dark side: information overload and its close cousin, attention fragmentation. These scourges hit CEOs and their colleagues in the C-suite particularly hard because senior executives so badly need uninterrupted time to synthesize information from many different sources, reflect on its implications for the organization, apply judgment, make trade-offs and arrive at good decisions.”
Without proper support from data science departments, lenders risk drowning in data without gaining actionable insights. That’s exactly what is being seen with those lenders who have made big investments in BI.
Today, three in five organizations report using data analytics to drive business innovation, with many continuing to invest heavily in data science and analytics departments, according to data from blogger Josh Howarth.
This trend is expected to result in 1.4 million new jobs created between 2023 and 2027. According to Howarth: “Deloitte asked 29 (chief data officers) about their top-three priorities for 2023 and beyond. Sixty-eight percent said they wished to improve the way they used data and analytics, 61% named delivering on data strategy as a top-three priority, and 50% said they hoped to improve the data culture in their organizations.”
Of course, this isn’t occurring much in mortgage lending. But it should be. Without data science, leaders will find themselves floating in a sea of data that is overwhelming and may not make sense without a game plan.
Emerging field
Data science extracts knowledge and insights from structured and unstructured data. It combines various disciplines like statistics, computer science and domain-specific knowledge to analyze large amounts of data and create models that can predict outcomes and uncover patterns.
Data science is broader than business intelligence in that it focuses on advanced statistical methods, machine learning and the handling of large datasets (often called “big data”). While business intelligence is more about reporting and dashboarding historical data, data science emphasizes predictive modeling and discovering deeper trends that drive decision-making.
These are the people who make sense of big data. But with the average annual salary of these professionals currently at $122,529, according to Indeed.com, and almost certain to grow in the future, companies are already looking for ways to analyze more data with fewer people.
AI was an easy answer. Its ability to process and analyze vast amounts of data has made it a highly prized tool. The models are still under development, however. The systems are prone to hallucinate and come up with their own data if not carefully monitored. AI summaries condense data and can hide real insights by crushing them into an easy answer.
Finally, because data science and AI are still emerging disciplines, most companies are still learning to wire everything up correctly to deliver actionable results. Until lenders find a way to integrate data science into their organizations, they can become better competitors by simplifying their approach to both data gathering and analysis.
Thorough understanding
Success in other areas of mortgage lending, such as servicing, asset sales and securitization, can be very complex. Loan origination need not be. Production loan officer management should be all about data.
How many prospects are they touching daily? How long are they on the phone? How many business referral partners are they contacting? How fast are they responding to inquiries? What are borrowers saying about them in your consumer surveys? And, obviously, how many loans are they closing?
Lenders are pretty good at gathering information about their team’s performance, but that’s only part of the answer. An originator can do everything according to the playbook and still underperform when it comes to closing loans. To find out why, you need the other half of the picture.
If you’re not looking at your business from the outside in, you’re missing a vital part of the equation. You can’t get on base with a client if you don’t know exactly what that borrower is looking for and expecting. You won’t find that information inside your company.
Lenders must know exactly what the business looks like to the media, to past and existing clients and on social media. That’s great information but it’s meaningless if you don’t compare it to your toughest local competitors.
You need to understand what the outside world sees in them to perform a strengths, weaknesses, opportunities and threats (SWOT) analysis. Add a gap analysis — the difference between current and desired performance — to understand what it will take to win the game. Then, it’s just a matter of picking your best loan officer players.
