Digital Transformation and Data Strategies Should be Top of Mind in Tech Budgeting (Part 2)

by Brian Hamilton
Digital Transformation and Data Strategies Should be Top of Mind in Tech Budgeting (Part 2)

In part one of our two-part article series, we discussed the importance of innovation over three horizons ranging from 12 months to five years out. With ďHorizon 1Ē over the next 18 months being the most critical for credit unions to focus on. This includes aligning digital transformation with your overarching business strategy, refreshing legacy processes, and transforming the entire lending experience for all stakeholders.

As we continue our focus on the first Horizon/tier of digital transformation, letís look at how modernizing data analytics capabilities and implementing automated decisioning are vital elements in todayís hyper-competitive market.

Itís in the data

Data analytics is certainly not a new concept, but many credit unions fall short on effectively leveraging their existing data to glean actionable insights that creates a sustainable strategic advantage.

Compiling data from multiple systems, then normalizing and structuring it in a way that can be readily analyzed is a challenge for many credit unions with limited information systems resources or challenging integrations with disparate systems. Investing in data aggregation tools and an experienced business intelligence officer may prove to be a self-funding investment in the long-run.

Alternatively, consider the option of bringing on a partner for professional services to avoid the sunk-cost of investing in software and employees.

With proper access to comprehensive data and sophisticated business intelligence software and professionals, a credit union can shift from descriptive analytics to predictive analytics, and ultimately prescriptive analytics. Descriptive analytics describes what has already happened; delinquency and charge off reports are an example of descriptive data.

While predictive analytics provides insight into what may happen based on historical data; credit scores and in-market propensity scores are two examples of predictive data. Most credit unions leverage descriptive data, and many use limited predictive data, but very few have reached the realm of perspective analytics.

Prescriptive analytics continuously analyses large amounts of data to recommend the best course of action in a myriad of situations. Reaching this level of analytics typically requires investment in data aggregation as well as a highly sophisticated analytics tools and data scientists that understand how to effectively use the tools.

In this example, your credit union would use its descriptive and predictive data, combined with other data at your disposal, to determine exactly how to best adjust everything from automated decisions, to interest rates, to marketing investments, to which collection calls to make, and which loans should be sold on the secondary market.

Using automated decisioning to capture more loans

Rising consumer debt, the inverted yield curve and even a slowdown in RV sales all point to a market correction and potential economic downturn. When it will happen; how long it will last; and how deep it will be are all unknown. Regardless, this is when it makes the most sense to intelligently leverage data to increase your automated loan decisioning.

Too often, credit unions associate automated decisioning with simplistic decisioning, only using system approvals for super-prime and some prime tier members. These decision models miss-out on loans that will perform well, and worse yet, miss critical red flags (e.g. rising credit card debt) that should be reviewed further as the loan may not perform in a downturn.

Credit unions in todayís market have the data and system sophistication to be well north of 40% automated decisions.

Consider this: during a recession you have compressed margins and are funding fewer loans. To be profitable, you have two choices: raise rates or improve efficiencies.

When you optimize your loan origination system and back up your decision logic with meaningful data, your system can make the same decisions a human underwriter would. With the application of emerging models, systems could arguably make the best decision more consistently — while increasing efficiencies and reducing operational expense

Plus, automated decisioning allows you to capture more loans in a down market because of a faster member experience. You improve your chances to get the memberís loan instead of delaying the decision by kicking it back to an underwriter, only to have the applicant move on to shop for financing somewhere else.

Prioritizing your technology needs according to the innovation horizons planning model will provide the clarity you need to make the most of your technology budget.

Focusing on optimizing existing technology that drives your most popular products and serves your current markets will allow you to meet your growth goals, improve efficiencies and control costs. In turn, your organization will be in the best position to make the right move when new technology and market opportunities present themselves in 2020 and beyond.

About the Author

Brian Hamilton
Brian Hamilton has 25 years of experience with financial institutions and fintech start-ups, where he has managed all facets of consumer lending operations, and has led key initiatives in the development of groundbreaking on-line applications, custom scorecards, and loan origination systems. He joined CU Direct in 2017 as the Vice President of Innovation, where he leads research efforts on emerging trends and product innovation. Prior to his position with CU Direct, Brian was Vice President of Lending at UNIFY Financial and Chief Credit Officer at BlueYield. He previously served as VP of Lending at SchoolsFirst, First Tech, and the Golden 1 Credit Union.