Bank executives, analysts and investors understand the importance of the efficiency ratio. AI carries great promise in both reducing the numerator and increasing the denominator. As competition for deposits continues to accelerate and net interest margin pressure grinds higher (both for a well-covered set of reasons), improving service delivery, reducing errors, and taking out operational costs are critical.
The banks that thrive will be the ones that embrace the initiative to lower the efficiency ratio and put the gains to work. I have seen this firsthand. Over the past two decades, I have helped some of the largest organizations streamline and automate their operational processes, among them NASA, IBM, Apple, Intel, PayPal, GoPago (which I sold to Amazon) and Fiserv.
Efficiency first, then growth
Efficiency is where the return shows up first. Growth follows. As AI takes over work that once consumed staff time, customer service can improve in specific ways: faster cycle times on account openings and loan decisions, fewer errors for customers to chase down, and a better understanding of client needs and product fit. Banks can also offer products and services they were not able to support before. A number of opportunities open up as a result, but none of them are within reach for a bank that is still buried in manual work.
That is why the efficiency ratio is the right scorecard. It forces a plain question: what does it cost to produce a dollar of revenue? Every manual handoff, repeated data entry and rework loop adds to the answer. AI aimed at those costs moves the number in a way the board and investors can see.
It starts with knowledge capture
All of this depends on knowledge capture: an accurate understanding of how your business is actually performing. Many banks do not have enough time for it. The people who know how the bank runs are the same people keeping it running.
Knowledge capture is not documentation for its own sake. It shows where work gets stuck, where handoffs break down and which steps are worth automating. Without it, a bank is guessing at where AI will pay off, and guesses are expensive. With it, leadership can see how the bank runs, make decisions on evidence and move faster than competitors still working from instinct.
Across all of that work, I have noticed that technology is rarely the thing that fails first. What fails is the knowledge around it. Banks need to understand how they run, learn where to optimize and apply automation to get there. Done well, that takes months, not years, and the effect on how these organizations operate is drastic.
Read More on Fintech : Global Fintech Interview with Rob Young, Managing Director – UK at InDebted
A double-edged sword
AI will cut both ways for banks, and it will ultimately determine whether they are on offense or defense. Adoption will have an overall impact on deposit costs. Stablecoins are going to take some of those deposits out of the system, which makes it all the more important to improve your efficiency ratio now, while there is time to do it deliberately.
Banks that act early will be on offense, with a lower cost base and room to price competitively. Banks that wait will be defending a higher cost base against rising deposit costs.
AI also offers a way to do a more compliant job at a lower cost. For an industry where regulatory work is a major expense, that is a meaningful shift in how the economics of a bank can look.
Do not take your customers for granted
Banks are businesses. In any business, you do not want to lose the existing business you have, because it is easier to keep what you have than to grow something new. Number one is not taking that for granted. Make sure the service you provide is excellent.
That calls for a heightened sense of urgency. Eradicate the bugs and challenges customers are experiencing. Your applications should be fully up and running. Onboarding a new client cannot be cumbersome. It must be streamlined and smooth. The time AI frees up should go here first.
Community banks and regional banks
For regional banks, the path to survival runs through using AI to free up time to be more involved in the community. Community banks have long held that advantage over their larger regional peers. Efficiency is what lets a regional bank compete for it, because it buys back the time that relationship banking requires.
First you must crawl, then walk
There is one more angle worth stating plainly. Adopting one of the major AI tools before you have done effective knowledge capture actually degrades your knowledge capture. It costs money and causes chaos.
A simple framework applies: crawl, walk, run. Crawl is capturing how your bank really works. Walk is applying automation where that knowledge shows it will pay off. Run comes after. Banks that skip ahead find out the hard way that you can’t just run.
The banks that win will treat AI as an efficiency program first and a growth engine second, built on knowledge they have captured and understand. Lower the efficiency ratio, protect the customers you already have, and the growth opportunities follow.
About Confidence Systems
Confidence(R) captures the value locked inside your most productive people and puts their knowledge to work in repeatable processes you can rely on.
Catch more Fintech Insights : The AI Shift in Fraud: Why Banks Need a New Playbook
[To share your insights with us, please write to psen@itechseries.com ]
