Why Corporate Banks Should Embrace AI And Machine Learning
With fintechs and other challengers threatening to pick off the most profitable parts of corporate banking, such as international money transfers and the provision of FX services, never has it been more important for banks to invest in fundamentally improving the ways they serve their corporate customers.
Unlike in the retail banking industry, where most customers use only one institution for their banking services, corporate bankers must always operate on the basis that their customers will have relationships with several institutions. Therefore, they must compete for wallet share.
There are two strategic questions that corporate banks need to address:
What is it that corporate treasurers (the principal owners of the relationships with the banks) want, and what will incentivize them to increase the proportion of their banking business that they give to one institution over another?
Much of what is driving corporate treasurers’ expectations today is coming from their experience as consumers. Given their experience of looking for a product on Amazon, choosing, ordering and paying for it and receiving their order the same or next day, their expectation of what a customer experience should be has been significantly raised.
Even in retail financial services, where it is simple and cheap to make a foreign currency transaction using a fintech such as TransferWise, the inevitable question is: Why does it have to be so difficult to do the same in the corporate world?
Even when TransferWise can’t make the payment instantly, the consumer has complete transparency on where the transaction is in the process and, importantly, in real time. The corporate treasurer is looking for the same level of transparency and ability to self-serve when they engage with their banks. Needing to phone their bank to find out the status of a payment is no longer acceptable.
Speed of execution is another expectation that has been changed by the treasurer’s consumer experience. They expect their banks to make decisions quickly and for transactions to be executed faster than they are today.
A frictionless experience in sharing data between the bank and the corporate is increasingly being demanded. Traditionally one of the reasons that corporates rarely change their banks is because the onboarding process by banks takes a long time, is error-prone, highly bureaucratic, and every bank has its own process requiring slightly different information.
If a bank can offer frictionless onboarding, where the bank does most of the work and the time to onboard is dramatically reduced, then the positive impact on that bank’s share of the corporates banking business will be huge. Introducing a standardized approach to switching (where every bank asks for the same set of information and not asking for what they already know about the customer), as has been introduced in several countries in the retail banking industry, should be introduced for the corporate banking industry.
If this was put in place, there would be a dramatic shift in the number of corporates changing their banks. It is understandable why the incumbent banks don’t want to do this for fear of losing customers. However, those who do, and do it well, will significantly benefit. If they don’t do it, then one or more challenger banks will and will pick off the most profitable parts of their corporate business.
For the corporate treasury teams, too much time is spent reconciling the cash accounts in their general ledger with the bank accounts that they have with their banks. Much as open banking promotes the idea of consumers having a single place where they can see all their accounts, regardless of which bank is the provider, so too corporate treasurers do not want to have to visit a different portal for each of their banks, but rather have one place where they can see all of their bank accounts.
Simplification of that whole process so that there is a simple matching of ledger cash accounts with bank accounts through the use of a virtual accounts solution allows the treasury team to focus on the important decisions about cash management. The bank that can offer this to their corporate customers will win a greater portion of their cash management and other banking business.
A frustration for the corporate treasurer is that their relationship manager often does not have a total view of the corporate’s relationship with the bank. Most banks are still organized around product divisions, and it is left to the corporate treasurer to navigate around the bank’s organization, or worse, fend off multiple salespeople from the bank trying to sell competing or overlapping products from the same bank.
The corporate bank customer’s requirements have evolved but are fundamentally straightforward and reasonable.
What role do artificial intelligence and machine learning play in delivering the corporate banking customer’s requirements?
Much as young children have grown up with the expectation that every device is touch-sensitive and there is an increasing acceptance of Alexa and other voice-enabled devices, it won’t be long before a bank (or more likely, a non-bank such as Amazon) will offer corporate customers a banking proposition where artificial intelligence and machine learning will simply and seamlessly be built into all business processes.
There is already evidence of it beginning to be used across the whole lifecycle of banking business processes: At the front end, for example, machine learning is used to display help pages in the order that they are most frequently requested, encouraging self-service by customers rather than them having to phone for assistance. In the back-office, it is beginning to be used for fraud and money-laundering detection along with payment instruction repair.
Due to the difficulties of switching banks (as mentioned above), corporate banking customers have low levels of churn. However, what they do exercise is the ability to flex the share of banking business that they choose to give to individual banks.
Identifying the leading indicators that a bank is becoming less favored by a corporate customer is a task highly suited to machine learning. The key characteristics that lend to this being solvable using machine learning are the large quantities of structured (e.g., transactions) and unstructured data (e.g., social media, emails, phone calls) from a large cohort of customers.
Looking back at common events that occurred before customers significantly reduced the share of their banking business with a bank should help to build an understanding of the leading indicators of business attrition. With significant returns if this potential loss of share of wallet is addressed prior to it occurring this makes it an ideal case for using machine learning.
The recent uncovering of large-scale money laundering being enabled by a number of banks such as Danske Bank, Credit Suisse, and HSBC and the subsequent consequences, both financially and reputationally, for the banks involved could have been identified earlier had machine learning technology have been applied to the problem. Machine learning is particularly appropriate for this type of dynamic problem where the money launderers adapt their techniques and approaches to avoid detection and the system to identify and respond quickly to these changes.
Understandably, one of the most frustrating experiences for corporate customers is when payments made are returned by the bank due to clerical errors such as incorrect IBANs, payee names, or account numbers being submitted. Increasingly, banks are turning to machine learning to fix these issues and allow the payments to go through without having to be returned to the customer.
This is because of the increased IT ability to handle fuzzy data, for instance, where there could be names spelled incorrectly or digits transposed. Given the high volumes of transactions and the varying nature of the errors, machine learning is far more productive at addressing this than manual intervention.
The changing demands of corporate customers, the increasing competition for the most profitable segments of banking business and the increasing cost efficiency of IT processing means that this is an ideal time for corporate banks to apply the power of artificial intelligence and machine learning to deliver a far better experience to their customers in a more profitable way.
For more on emerging technology in the banking industry, see Pinpointing The Value Of Intelligent Banking.