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Using Analytics To Identify Valuable Customer Deposits

It has been nearly a half-century since U.S. lenders revolutionized the credit-card industry by making it a national business that provided specific products and offers to customers based on their individual profiles. The transition occurred when a few forward-thinking lenders started using data and analytics to rapidly identify creditworthy consumers, set pricing and reach out to customers.

This strategy finally is available for the deposit business, but are banks ready for it?

They should be.

In a world where customers shop for rates from their living room, effortlessly move money between accounts and don’t rely on local branches, banks need advanced intelligence to assess the quality of their deposits. Institutions that exploit these deep analytics will gather the best customers, leaving behind those that don’t invest.

We all know that credit scores can help identify risk of default, losses that will be incurred by default and early payment. Combined with elasticity models, they can also reliably predict the contribution of customer segments at different rate levels.

Similarly, banks need to understand the cost to acquire and value of deposit balances over time. After all, a lender would never offer a 5% credit card with a $10,000 limit to a customer with a credit score of 550, would they?

But what about offering a customer an interest rate of 2.5% to deposit $50,000 if there is a 1 in 10 chance that the customer would keep the deposit at the bank past the promotional period? If a bank needs the money quickly, maybe that offer makes sense. (See Figure 1.)


Like credit scores, deposit scores that measure the marginal cost of funds (MCoF) can rank customers based on their probability to move balances at a specific posted rate. Even more powerfully, the scores identify customer propensity to move large balances and the elasticity, or price level, required for a customer to bring balances to a specific bank.

Deposit scores also can rank the marginal cost of funds for a 12-month period, allowing financial institutions to become more practical in how they handle customers. (See Figure 2.)


These scores can help banks target a wide range of rate and non-rate treatments where they will have the greatest impact and value over time. Novantas has identified at least four areas that can benefit from the use of deposit scores.

First, banks can focus on post-promotion treatment to identify balances that are at risk of leaving after a promotion. Offers can be targeted to those customers alone, rather than across the whole customer base.

Second, banks can establish exclusion lists that identify customers who routinely move money out as soon as a promotion is over.

Third, deposit scores can arm front-line bankers with data that can help them make exception-pricing decisions based on the value that the customer’s deposit will bring to the bank over the next 12 months. Too often, bankers are forced to rely on intuition or rudimentary rules.

Finally, deposit scores can help marketing departments aim their direct-mail, email and other digital campaigns at the most valuable deposit segments rather than pursuing a broad-brush approach. Novantas estimates the broad strategy can be as much as 10 times more expensive on an MCoF basis than the targeted approach.

While MCoF scores represent a huge opportunity to build balances from existing customers who represent as much as 85% of new-balance acquisition at a typical bank, they can also be used to target prospects.

Using data about direct mail, email and other digital targeting activities, analytics can shrink the universe of appropriate offers by as much as a third. The data can also help differentiate the best types of offers, including cash and rate, as well as provide a more granular understanding of prospect elasticity.

This ability to segment customers, instead of making a general mass-market offer, can reduce cannibalization and make marketing departments more efficient. It can also reduce the potential that a bank overpays for deposits, which is one of the primary drivers of MCoF inflation.

Industry credit scores enabled challengers in the credit-card market to differentiate offers and products to customers based on behavior and preference. This resulted in a better fit between lenders and customers. It also ultimately played a role in consolidating the market into a few leaders.

Novantas believes that the banking industry’s transformation to a digital business is pushing the deposit market to an inflection point. Deposit scoring will enable banks to compete through this market disruption.

Banks that take advantage of deep analytics and proper tools will be better able to compete in this emerging environment.

By Pete Gilchrist | EVP |


The successful deployment of deposit scores will improve deposit-business profitability by decreasing interest expense and optimizing non-interest acquisition and retention expense. In addition, deposit scores allow the business to encourage deposit growth from stickier, lower-beta deposits, which will improve funds transfer pricing (FTP) crediting rates for deposits.

Under most FTP implementations, however, the business will have to wait years for deposit FTP improvement because methodologies are backward-looking. It will take many years for stickier and lower-beta behaviors to occur and be measured, modeled and implemented.

And even when FTP crediting rates do increase, they will improve broadly — with increases covering large segments of accounts rather than being tailored to individual customers with individual attributes.

There is no reason we should have to wait years. In the past, limitations of data, technology and systems constrained the ability to ascribe granular, forward-looking deposit value through the FTP system.

But with the advent of big data and AI, we know an incredible amount about individual depositors’ likely future behaviors and, therefore, the amount of value their deposits bring to the bank.

It is time for deposit FTP to begin reflecting this knowledge. Scores that project the likelihood of future behaviors are a key component of how the industry can migrate to customer-level deposit FTP. This can, at long last, break the historical bounds of an over-simplified deposit FTP crediting system.

By moving to customer-level FTP, treasury departments have the opportunity to encourage businesses to incentivize the right customer-level behaviors. Treasury can encourage businesses to attract, onboard, expand and retain the right customers by offering higher FTP credits for customers whose deposits are likely to be more plentiful, stickier and lower beta.

Conversely, Treasury can discourage businesses from bringing in customers with more volatile and price sensitive balances by offering lower FTP credits for these customers.

Without these customer-level signals, Treasury effectively encourages a race to the bottom since it offers “average” FTP credits to everyone, making it easy to attract and retain customers with the lowest value.

Hank Israel
Director, New York

For more information, contact Novantas Marketing

+1 (212) 953-4444

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