Why Corporate Banks should be embracing AI and Machine Learning

It’s a financial world: Why Corporate Banks should be embracing AI and Machine Learning

Why Corporate Banks should be embracing 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 there been a more
important time for banks to invest in fundamentally improving the ways that
they serve their corporate customers if they are to retain and grow their share
of their customers banking business and importantly delivering this profitably.

Unlike in the Retail Banking industry, where most customers
use only one institution for their banking services, Corporate Bankers have
always had to operate on the basis that their customers will have relationships
with several institutions and therefore they have to compete for share of
wallet.

There are two strategic questions that Corporate Banks need
to address.

What is it that
corporate treasurers (the principle owners of the relationships with the banks)
want and what will incentivise them to increase the proportion of their banking
business that they give to one institution over another?

A lot 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. When even in Retail Financial Services
where it is simple and cheap to make a foreign currency transaction using a
Fintech such as TransferWise, it raises the inevitable question of 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. Having 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 on-boarding
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 on-boarding that is frictionless, where the bank does most of
the work and where the time to on-board is dramatically reduced then the
positive impact on that bank’s share of the corporates banking business will be
huge. Introducing a standardised 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 is promoting 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 organised around product
divisions and it is left to the corporate treasurer to navigate around the
bank’s organisation or worse still fend off multiple sales people 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 does
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 the use of
Machine Learning to display the 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 seen 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 favoured by a corporate customer is a task highly suited
to Machine Learning. The key characteristics that lends 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 to
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 spelt 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.

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