Banks are awash with data of different types and formats, as well as different levels of detail. While all data is stored, banks use it for different purposes, such as regulatory compliance, customer behavior analysis, and other strategies. However, they also leave much of their data idle.
When it comes to understanding customers, compliance data (e.g., anti-money laundering and fraud data) typically is the best source of information because this type of data tracks everything a customer does within a financial institution—from making a payment, to executing a trade, to applying for a loan. Payments data also is extremely valuable (and often easier to access), even though it only tells part of the customer story.
Making better use of data to identify customer needs, especially with the advent of open banking, is critical in today’s banking market. By analyzing and comparing customer data, banks can identify ways to serve customers better. In addition, they can drive business value in many other ways.
Driving customer centricity
Let’s say a customer is making cross-border payments with a volatile currency. The bank can deliver an alert, saying, “We see you are paying a high price for currency in the spot market. We can help by selling you a foreign exchange contract.” To do this, intelligence is required to identify that the payment is cross border, the type of currency used, how much the client is paying in the spot market, and the best foreign exchange contract to offer. Each one of these steps is easy to code, which can lead banks to assume this is a simple undertaking.
However, in reality, the hard part is asking the right questions and knowing where to start. How can a bank better monetize data for customers? The first step is to identify the customer’s need, which certain behaviours will indicate, all of which happen behind the scenes.
A branch manager may know, for example, what is happening with a customer but has no bandwidth to offer the right products instantly. As an alternative, the bank can use data to know what offers to make and when, and then send a notification to the account holder, along with a message to the branch manager or account officer for follow-up purposes.
Banks also can use a mix of both internal and external data to better understand and serve customers. In the past, approximately 80% of data used by banks was internal, with the remaining 20% coming from external sources, but this is now shifting to a 20/80 model. For example, a bank can pull a customer’s property tax bill to determine the value of the customer’s house when considering a lending opportunity. Using this information, the bank also can create a reasonable estimate of the borrower’s net worth to supplement or even replace financial statements, depending on the loan amount.
Mixing this data with peer group data enables banks to make a pitch for a variety of financial services in an automated and targeted way. Sending general, non-personal customer emails—or worse yet, physical mail—can be inefficient. Data can enable banks to market specifically to a customer’s needs by comparing the customer’s behaviour to peers. The bank can model a representative product portfolio based on accounts typically held by the peer group and then make recommendations for products the customer is missing.
Taking advantage of this opportunity requires a very special kind of expert—a data scientist. Data scientists are difficult to find and command a premium, but they are an important part of any data-driven strategy. This is because bank data often is in a poor state (full of errors, incomplete, etc.), and, frequently, a data scientist is needed to cleanse, enrich and interpret the data in a way that can generate significant value.
Using data to drive other business value
In addition to using data to learn about customers, banks want to know if they can use data to drive business value in other ways. For example, how can they use data to make better lending decisions? This requires an understanding of the current economy and local markets. As such, the bank also needs a sense of the risk in lending to customers in a particular market or industry. A bank may think of a great product to increase loan volumes in a particular market over the next 60 days. However, if it looks more closely at the data, it might identify economic challenges that would make it unwise to move forward at this time. In the alternative, the data might show it is an ideal time to grow market share because of other factors, not only creating opportunities to increase revenue, but also lowering risks and costs, creating a win for both the bank and customers.
Another common example and challenge is assessing the profitability (and, ideally, the lifetime value) of an individual customer. It seems like an easy undertaking, but to build accurate and effective customer profiles requires looking across a variety of product and data silos, not just at payment or loan data only, for example. Next, grouping data appropriately (e.g., private wealth, retail, or small business owner data) is critical. For example, is the customer a high net worth customer, or does the customer generate significant transaction revenue? If so, a dedicated account manager may be required. Is the customer a self-service customer with low balances? If so, then perhaps digital banking is the best option for the customer.
In addition, many banks want to move to a more predictive model for targeting individual customers. For example, if a customer has a certain set of products, what is the next product the customer is going to need and when? If the customer is acting in a certain way, does this mean the customer is more likely to leave or stay? Gaining a better understanding of what might happen next gives banks flexibility on when and how they launch products into markets, making risks more visible and pricing more appropriate.
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Feel free to contact me to discuss your data challenges, and CGI’s work in this area.