What’s In A Name: Senior Vice President Of Loan Analytics And Automation

In today’s fast-faced, competitive lending climate, combining data analytics with process automation helps credit unions achieve higher efficiency and member satisfaction while reducing risk. But what does this look like in action? And how can credit unions balance high-tech with a personal touch?

“If we can’t approve a loan, we want to explain why to the member and offer them counseling, tools, and resources to improve their financial wellbeing so the next time they need a loan, we can approve that request,” says Andy Henline, senior vice president of loan analytics and automation at State Employees’ Credit Union ($56.4B, Raleigh, NC).

Here, he talks more about how his role influences strategy and operations at the Tar Heel State powerhouse.

Describe the primary responsibilities of the senior vice president of loan analytics and automation?

Andy Henline: My primary responsibilities include ensuring loan-related reporting to the board and senior management is accurate and timely; and back-office staff in loan administration have the tools, information, and process automation they need to better complete their daily tasks. I also serve as the subject matter expert for loan data for our data warehouse.

What does a typical day look like for this position?

AH: My calendar tends to be crowded with meetings, but if I’m able to spend some time in front of my computer, I’m usually building reports from our data warehouse and core loan applications or writing code to perform analyses on that data. I tend cover the ad-hoc reporting requests from senior management while my staff handles the formal project requests from all the groups in loan administration.

How does your role contribute to the overall lending strategy of the credit union?

AH: I help to supply information on the composition and performance of our loan portfolio and assist in adjustments to our automated loan approval parameters. I also perform many “what if” analyses as policy changes are being discussed.

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How do you leverage data analytics to improve loan performance and member satisfaction?

AH: We use loan data to build parameters in our loan decision engines. We monitor the performance of loans that are system-approved versus those that have been approved by a loan officer. We make adjustments, when necessary, with the goal of automatically approving as many member loan requests as possible without introducing undue risk into the portfolio. We also use analytics to inform our lending policies — such as limiting the age of the vehicle for a used car loan — to reduce credit risk in the portfolio

What types of loan data do you analyze? How do you use this data to make decisions?

AH: We tend to use data across the entire lifecycle of a loan, from application data in the LOS to performance data from our servicing systems. In addition to in-house data, we get updated credit scores each month from our bureau partner for every member with a lending relationship, and we use anonymized data from other lenders — HMDA, etc. — to analyze the performance of SECU relative to others. We also have more than a decade of loan-level historical monthly data in our warehouse that we use in building machine learning models.

How is automation transforming the loan process at your credit union?

AH: We are working to eliminate manual and laborious processes and reduce errors. Simple changes like having systems integrated so no data needs to be rekeyed make a tremendous difference. We are also building in-house applications to supplement our LOS and loan servicing systems with additional functionality for the way SECU does business.

What role does artificial intelligence play in your loan automation processes?

AH: We haven’t really implemented much AI in our lending processes yet. Our decision engine is a simplified decision tree — not really true AI. We are talking with several vendor partners to enhance our decision engine(s) with more robust machine learning models, but that has not entered production yet.

How do you ensure the credit union’s loan products remain competitive in the marketplace?

AH: We partner with several credit bureaus to obtain data on the borrowing habits of our members, particularly as it relates to the “off us” loans members have. We’ve also developed partnerships with several industry data groups to allow us to compare our loan terms and offerings to those of our competitors.

We also want to listen to our members. If there’s a loan product they want, we want to know about it. Each of our branches has a member advisory board that meets quarterly to help inform us of new products and services our members would like to see.

How do you balance automation with maintaining a personalized experience for members?

AH: We want automation to enhance the member experience but never replace the personal touch our people can provide. For example, we don’t have automated denials with our online applications. If we can’t approve the member automatically, we want them to be able to talk to a real person who will listen to the member’s story, understand where they are on their financial journey, and approve the loan if it’s in their interest to do so.

If we can’t approve a loan, we want to explain why to the member and offer them counseling, tools, and resources to improve their financial wellbeing so the next time they need a loan, we can approve that request. However, we also want to serve members in the way they want to be served — if a member wants to apply online, get an approval, and close their loan without ever talking to a loan officer, we want to make that option available.

What challenges do you face when integrating automation into the loan underwriting or approval process?

AH: We’re being very deliberate in our integration of AI and automation into the lending process. We want to make sure any AI or automation enhances the ability of our staff to serve the member.

How does your team collaborate with other departments, such as risk management or compliance, to ensure a smooth loan process?

AH: My team works closely with our management analytics group, which is the team that maintains our data warehouse and ETL processes. We also collaborate extensively with risk management to ensure the data and analyses produced is as accurate and complete as possible. And we serve as lending subject matter experts for our financial modeling group, which handles our CECL models.

What metrics do you use to measure the success of your analytics and automation strategies?

AH: The best measure of success of our analytics and automation strategies is the feedback we get from users. We always strive to make processes easier, whether that’s with a simple database application to enhance core systems, a new report, or an in-depth analysis of data for a particular lending group.

Where do you see the future of loan analytics and automation going, particularly within the credit union space?

AH: At least at SECU, I would like to introduce more machine learning and automation into the loan application and underwriting process to give the loan officer additional tools and information to assist with their decision-making. If a loan can’t be automatically approved, I’d like our AI/automation to show the loan officer the reasons why and make suggestions on things we should discuss with the member to potentially approve that application. Automation is a tool that should reduce friction in the process but never replace the personal touch we can provide.

— This interview has been edited and condensed.

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