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AI helps treasurers keep pace with changing demands of role

The role of corporate treasury has evolved considerably in recent years, observes Pankaj Gupta, managing director at Synechron.

Once, treasurers were chiefly concerned with asset liability management and liquidity, but today they are concerned about the impact of regulations, such as Basel III, and their implications for capital adequacy and risk management.

Banks and technology companies have been working on a number of systems to help treasurers and CFOs stay on top of these changes. Few technologies have done as much, or have as much potential to deliver future efficiencies, as AI.

AI can help banks evolve their understanding of their clients, from what they want to why they want it, says Alenka Grealish, senior analyst for corporate banking at Celent.

“This has profound implications for the nature of customer engagement, moving it from being tactical to being strategic,” she says.

By understanding why a client chooses certain services, banks can make better recommendations, acting more as an adviser and less about pure execution, she explains.

AI can help banks and their clients in a myriad ways, such as reducing operational costs, ensuring regulatory compliance or improving analytics, both to better understand existing data and produce better forecasts.

Leap in the dark

However, there is a leap in the dark required. The technology is new and in some circumstances unproven. The return on investment is hard to measure and complex, being part cost-growth minimization, part indirect revenue and part lower fraud losses, says Grealish.

Given the high rates of CFO turnover, this can make it particularly difficult to plan strategically and make the right long-term investments.

However hard it may be to measure with precision, the case for investing in AI seems clear.

Dean Henry, head of innovation in global transaction services at Bank of America Merrill Lynch (BAML), says: “We look for three things in any kind of technology, not just AI. We want it to make client interaction easier, to increase efficiency and improve risk management.”

Without coming up with a single number to quantify it, AI can clearly deliver these benefits in a number of functions. Cash forecasting, for example, is a traditionally manual process, involving examining paper statements for different account details, as well as aggregating things such as purchasing orders. AI can do this better than any human, making it considerably more efficient.

Henry says: “AI has an incredible ability to synthesize all this information and make insightful predictions about future cash flow.”

However, the progress banks have made in developing and using the technology is unsurprisingly inversely correlated with the technical complexities involved.

Synechron’s Gupta says: “At the simpler end of the scale, AI can help in low-value, high-volume areas [such as] data entry, automated decision-making for reconciliations and identifying trading breaks. At the medium to complex end of the spectrum are regulatory reporting and predictive analysis capabilities.

“The reporting formats are standardized, but data calculations and manipulations underlying the reports are not. Ultimately, self-learning machines will be able to help with that.”

Cautious roll-out

Celent’s Grealish says banks have made less progress developing customer-facing AI applications.

Having studied a number of banks’ AI investments, she found that “most customer-facing applications are just heading to the launch pad, which is not surprising given the complexities involved in scaling on the commercial side compared to the retail side.”

BAML’s Henry agrees, saying: “We are more inclined to take risk when we can control the impact, which is easier with internal processes than with client facing functions. But we also want to lead and offer our clients services that help them.

“So we are cautious about what we put in front of our clients, we do not offer them anything that has any operational risk attached to it – it can only be about improving efficiency.”

As the bank’s confidence in its AI products increases, BAML will be able to offer them more, he adds.

Pankaj Gupta,

Perhaps the most obvious use-case for AI in transaction services, at the simpler end of Gupta’s spectrum, is automation of reconciliations.

Banks routinely deal with a variety of customer errors when initiating payments, and payment repairs and investigations typically account for 75% to 80% of the labour-intensive part of a payment-processing operation, says Grealish.

BAML’s global receivables team launched Intelligent Receivables in August 2017, with a view to enabling straight-through reconciliation (STR) for automated clearing house (ACH) payments for which the remittance information is either missing or received separately from the payment.

More use of ACH and cards has been increasing the processing challenge, with a growing number of clients turning away from banks to fintechs, while regulation is also pushing customers towards greater accounts receivable efficiency.

However, machine learning helped the bank reduce the processing burden by eking out the additional percentage points that traditional technologies could not, pushing STR rates from 10% to above 90%, with direct posting to SAP and Oracle enterprise-resource-planning systems.

BAML plans on enhancing Intelligent Receivables with foreign-currency processing and incorporating it into its virtual-accounts service, notes Grealish.

“Already, its clients are realizing numerous benefits, including improved cash forecasting and the ability to manage trade credit more efficiently,” she adds.

Future of payments

Grealish believes AI will become more important in payments processing during the next five years as the share of machine-to-machine payments and real-time payments steadily rises. And other banks have already developed, or are developing, similar applications.

Panama-based Glo