Did you know that Play-Doh originated as a wallpaper cleaner, to remove the stains produced by coal-powered furnaces?
That synthetic dye was first produced by a 19th century British scientist attempting to create artificial quinine?
That the melting chocolate bar in his pocket led American engineer Percy Spencer to realize the magnetron his factory produced for combat radar operations during World War II could cook food, leading him to develop the first microwave?
Sometimes, innovations have applications that extend far beyond their primary purpose. And while a lending portfolio data analytics platform can’t cook your frozen chicken a la King in 2½ minutes, your platform possesses capabilities that extend far beyond tracking and optimizing your credit union’s assets.
Here are seven unexpected uses:
1. Confirming data integrity. How many times have you opened a report, only to discover that certain information is outdated or nonexistent? An integrated system automatically reflects changes to members’ data across the entire spectrum of their interactions with you, and alerts staff when information is omitted. Doubts about the validity of your data undermines your entire investment in collecting, maintaining, and analyzing databases.
2. Breeding brainstorming. Providing large swaths of your staff access to comprehensive data sows the seeds for innovation by enabling employees with varied points of view to theorize and test hypotheses. This exploration sets the table for in-house creations of new products and more efficient processes.
3. Gauging product performance. Stratification serves as an excellent quality control tool. It allows you to analyze data by separating it into distinct layers — as with rock formations. This exercise, which puts a magnifying glass on elements such as loan type and duration, can isolate strengths and weaknesses throughout your financial institution, paving the way for changes that increase efficiency.
4. Gauging internal performance. Quantifying profitability at all levels of the organization has become the norm. Are certain branches carrying their weight, and why or why not? Are various loan types performing well among specific demographic groups in specific areas, but falling short among a similar cohort elsewhere? Why are some loan officers “closing machines” while other, equally talented staff slog in mediocrity? Lending analytics programs allow you to slice and dice data for statistics that provide a road map to these answers.
5. Calculating compliance. Loan portfolio analytics platforms maintain comprehensive records of transactions and interactions, certifying the organization’s commitment to documentation. These systems also monitor for signs of disparate impact and other potential Fair Lending Act tripwires, assure compliance with newly effective regulations such as the Truth In Lending Act/Real Estate Settlement Protection Act Integrated Disclosure Rule (TRID), and enable you to maintain a robust Bank Security Act and Anti-Money Laundering stance.
6. Marketing magic. Amalgamating deep, informational and behavioral member data from internal and external sources allows you to assess the likelihood of members’ interest in various products and services, and tracks the productivity of messaging and offers. This information also clarifies the lifetime value and risk of members. Maintaining nonproductive relationships can be an anchor on a credit union’s ledger. To assure that you move all members toward practices that benefit them and the credit union, you must develop a system that closely monitors their behavior and prompts both cross-selling opportunities and interventions.
7. Ensuring technical proficiency. Reliance on modern computational software brings staff onto the same page and demands that employees acquire a level of computer savvy necessary for 21st century world of financial services.