By Tim VanTassel
Banks have access to a vast amount of customer deposit data that can be used to build stronger relationships. Yet, instead of tracking it and using it across product lines, many banks allow their deposit data to remain siloed.
It’s a risky dilemma. Here’s why.
- Deposit-related innovations like checkless checking are vying for customer attention. Banks will increasingly need to understand customers’ individual spending habits and preferences to know how best to serve them with such options.
- Given the long period of near-zero interest rates that we’re emerging from, the newest generation of deposit and loan product managers may have limited experience operating in a highly competitive market.
- Competition for deposits has grown to include a variety of players, including fintech firms, online banks—and even telecommunications providers working with an online bank. Strong customer relationships will matter more than ever to traditional banks.
- Customers have become increasingly comfortable “opting-in” and connecting their bank accounts with third-party financial management services. In the U.S., technology companies like Plaid, Finicity, MX and Yodlee are enabling developers to build applications that bring customer data together. As open banking takes hold in the U.S., banks will need to communicate its value to customers and engender trust, or risk losing them to new competition.
The question then becomes how to use existing data to engage customers differently.
Deposit account data provides another dimension to assess credit risk beyond traditional credit bureau data. Consumers’ cash flow behaviors can serve as indicators of their potential positive and risky habits. They can act as a precursor to delinquency and provide indications of changes in financial stability.
Deposit data has also been successfully used to improve offerings to underbanked and thin-bureau-file consumers, such as recent immigrants or consumers new to or re-entering the workforce. By tapping into consumer-contributed data that reflects responsible financial management activity—such as checking, savings and money market account data—you can help consumers improve access to credit.
The possible uses for deposit data also include:
- Understanding the source and use of the consumers funds
- Developing deposit behavior scores or enhanced credit risk customer-level scores to manage strategies across the business
- Predicting risk via a payment-based score without use of income source data
- Estimating income through the automated payroll or direct deposit for use under requirements of the Credit CARD Act
- Helping to determine lending terms and limit capacity
- Measuring lifecycle changes and determining pre-delinquent collections actions
- Signaling fraud
- Creating dual score strategies with credit and deposit risk ratings used in tandem
Similarly, credit scores and credit bureau data can be used to enhance deposit account decisions.
Deposit behavior—looking beyond risk.
The data relationship between deposit behavior and risk assessment extends to general consumer behavior indicators such as revenue, spend, attrition and response to offers. Consider the key decisions made by deposit product managers:
- Who to market a cash-bonus checking account offer to
- How much cash-bonus to offer
- What courtesy overdraft limit and funds availability to set for checking accounts
- What interest rates to offer on money markets, savings and CDs
Lending behavior data can serve tremendous value to deposit product managers when they are making these decisions. Hold strategies, for example, should vary widely across the customer base, and limits should be intelligently enabled—and lifted—based on experience with the customer across both credit and deposits.
Using deposit data to design the products people want.
For many banks, the same deposit interest rates are currently available to all customers. Yet as interest rates rise, banks need to consider more targeted, segmented pricing strategies. Today, when banks need deposit liquidity, they often raise rates higher than competitors to bring in large deposits. However, this brute force method does not create long-term stable liquidity. When deposits leave, the bank rarely discerns whether the balances are moving for better rates or for paying down debt. By looking at lending data, it is possible to differentiate customer deposit outflows among those that are rate seekers versus debt paydowns. This can help banks set effective, customer-level rates that deliver long-term liquidity.
Segmenting deposit customers can also help determine cash incentive offers for opening new checking accounts. Incentives can be tailored to be more effective by looking at a customer’s credit behavior—including payment history, credit utilization and debt-to-income.
Data and scores can also be used to establish and reassess courtesy overdraft limits for checking accounts. By evaluating credit payment and default behavior, it becomes possible to offer more effective overdraft limits that balance charge-off losses and fee revenue.
Strategies for a more stable portfolio and a more satisfied customer base.
By making the connection between deposits and credit, it becomes possible to make effective granular decisions—down to levels of segments, customers and accounts. Analytically-enabled relationship banking can use deposit data to enhance credit decisions in origination, overall exposure, cross-sell and collections.
But while deeper and wider data sets with advanced analytics can yield more sophisticated strategies, banks also have the challenge of executing such strategies.
Banks should not feel beholden to the simplistic strategies enabled by their legacy core systems. Using deposit data does not generally require changes to core banking systems. Using available data—such as ledger daily balance and transactional debits and credit tables—data aggregation and transformation programs can be introduced to mine the data for enhanced forecasting. This sets the stage for lightweight rules technologies that allow granular decision execution and model calculation, working around legacy system limitations.
Bank marketers can then focus on improving customer-facing decisions with a deep understanding of customer data that matters. With increased availability of cloud-based options, capabilities around price optimization, execution, data ingestion, machine learning, model execution and adjudication have become significantly cheaper. These modern decisioning platforms allow banks to make truly customer-centric decisions, helping them provide the right product features that delight customers and improve financial outcomes.
Tim VanTassel is vice president and general manager, solutions, advisory, and specialty sales group at FICO.
