The traditional handshake deal associated with commercial banking isn’t going away anytime soon, but institutions can add some extra power to it.
Retail bankers are slowly starting to embrace integrated data and analytic ecosystems to drive better insights about customer behavior. Advanced practitioners are exploring how big data and AI can unlock even richer, previously hidden benefits.
Commercial banking, however, is moving at a slower pace. Perhaps that is due to the historical “handshake” relationship-based sales and service model. Or maybe it is because of the “lumpy” nature of commercial portfolios that make them tricky to analyze.
Regardless of that history, there are good reasons for commercial banks to start adopting these tools now.
The recent round of Fed rate cuts is already squeezing net interest margins at commercial-banking divisions. And while parent companies are still reporting a fairly positive outlook, there are warning signs on the horizon. Among them: the growing trade war, slowing economic growth around the world and the first blips that indicate the glory days of credit won’t last forever.
Furthermore, alternative lenders — including private-equity firms — are
increasingly casting their sights on commercial banking by grabbing traditional business and searching for new revenue streams that banks haven’t yet penetrated.
SEGMENTING CUSTOMERS TO DETERMINE PRICE
Novantas sees a number of solid use cases in which analytical tools can help boost commercial-banking profitability. Immediate benefits can be found by incorporating them into sales and pricing, portfolio management and advice. (See Figure 1.)
Price optimization will always be one of the quickest levers that can increase net interest margins. After all, the holy grail of commercial pricing is the development of relationship pricing across a client’s portfolio of loans, deposits and fees. While elasticity measures are often deployed, they are typically limited to individual products and don’t reflect the traditional trade-offs that inevitably occur in client negotiations, such as loan rates versus deposit balances and earnings credit rates versus fees.
While there are certainly credit-based analytics that can be used in discussions about lending, there are major holes when it comes to negotiating deposits and fees. Banks need to critically examine price elasticity to determine which customers should receive price concessions. They should analyze the long-term value of deposits and fees for specific customers to provide the salesforce with tools to make economic price concessions.
For example, Novantas recently determined that a regional bank could save $16 million on its overall commercial cost of deposits by segmenting customers to determine where pricing was out of line with a customer’s actual demand for rate and the value of the deposits that they brought to the bank.
These savings, which were based on a $25 billion portfolio, were calculated in a flat-rate environment with minimal attrition. The bank could save even more when rates eventually rise or if it sheds deposits from unprofitable relationships. (See Figure 2.)
THE CASE FOR BETTER BUSINESS INTELLIGENCE
Credit underwriting and risk ratings have benefited from advances in spreading software, the development of scorecards and industry datasets on which to make comparisons. The appeal of the current system is its consistency: specific customer attributes can be matched to a specific decision.
But competition for these customers is intensifying, making it more difficult to differentiate between a marginal “yes” and a marginal “no.” Banks can find pockets of increased profitability by using AI and machine-learning techniques that can incorporate more diverse and dynamic customer information. The addition of big data can help bankers supplement traditional credit analysis with unique customer insights to drive better and more profitable deals.
Banks can also use data to analyze how relationship managers are pricing similar deals within the same bank, as well as the overall market. “Educated” guesses just aren’t good enough in today’s economic environment.
ADVICE DRIVEN BY ANALYTICS
A recent Novantas survey of businesses found that 82% consider a bank’s ability to provide advice as an important consideration when choosing a financial institution. Nearly half of respondents also noted a preference to receive advice through channels other than a person-to-person conversation with their banker.
As banks seek to expand commercial-customer relationships, this insight provides an opportunity to kill two birds with one stone. Banks that deeply analyze customers to better understand their own risks, liquidity profiles and opportunities can start to supplement their customer interactions with off-the-shelf analytics that provide client value.
While it is true that customized advice may be most optimal at times, AI-driven advice delivered through a sleek online portal can provide timely and impactful insights that can reach a broad swatch of customers.
THE WAY FORWARD
Such examples only scratch the surface of the potential benefits that can accompany a commercial bank’s willingness to embrace data and analytics. Some banks are already starting the process, but it also may be difficult to justify new infrastructure investments at a time of NIM compression.
Still, banks should recall the valuable insights gleaned from the financial crisis stress tests: it is easier to collect and manage data on an ongoing basis rather than try to compile it retroactively.
And if the immediate benefits aren’t enough to be convincing, the long-term opportunities to both the bank and customers should be readily apparent to bankers with even the strongest handshake.
Director, New York