By now, anyone familiar with the internet, which is probably practically the whole of humanity, is likely aware of ChatGPT, a Generative AI introduced only in November 2022 but which is threatening to revolutionize how life is to be lived in the Age of AI. And why? These cloud-based generative engines have been developed to a level where they are now surpassing human capabilities in various aspects to such an extent that there is growing concern that unless properly harnessed and controlled, our very existence as we know it is at risk.

For the majority among us, including yours truly, who are not net savvy, let me share some info generated from, where else, but Generative AI, of course! This magical fountain of information that never runs dry is also known as generative adversarial networks, or GANs, a subset of artificial intelligence that can create content, not just text but also videos and images, based on patterns and examples generated and learned from existing data of patterns and examples.

The rapid development of GAN applications in so short a time and in such a pervasive manner covering all forms of enterprises, the banking industry — one of the most sacrosanct of commercial endeavors which relies heavily on the utmost trust, integrity, and privacy of all parties involved in a financial transaction — is where it is expected to affect the most changes on how a business will be run in the future.

Accenture, the leading global business advisory firm, recently published a research report on Banking Trends for 2024, particularly in the context of GANs. They analyzed 19,265 jobs across 900 work categories in 19 industries, culling from US Bureau of Labor Statistics data. The time and motion study included a breakdown of each task and an evaluation of the potential for automation and augmentation by Generative AI.

The conclusion is that banking will be more extensively impacted than any other industry.

Approximately 39 percent of a bank’s tasks have a high potential for automation, with another 34 percent having a high potential for augmentation.

The study showed that banks have the highest improvement in productivity or number of hours spent on a task by up to 30 percent, measured in the dollar value of the US annual occupation headcount and wages for 2022.

These functions cover activities such as due diligence, risk management, HR, compliance, legal documentation, and code writing that, if automated, will significantly reduce operating expenses.

But the activities that have the most financial impact are customer-centric and generate financial revenues such as personalized wealth advisory investment services based on risk appetite profiling, i.e., deposits, fixed income, and equity securities mix; and customized as well as pre-formatted credit-related transactions, i.e., loan application evaluation, credit scoring, and financing structures.

Through avatars, for example, customer contact interactions can be standardized, enhanced, readily updated, and disseminated efficiently across a wide range of geographical locations, which otherwise would have to be performed by humans in a brick-and-mortar environment.

Customer satisfaction has improved, with four out of five customers rating banks with mobile apps and enhanced digital services high in customer fulfillment.

Accenture’s study further revealed that banks that have embarked on a successful generative AI strategy recorded an increase in revenues of a minimum of about 6 percent within three years.

On the other hand, there have been unwelcome consequences with the push for digitization. The study noted a marked decrease in emotional connectedness due to limited personal interaction of bank personnel with customers, resulting in reduced relationship loyalties.

Accenture refers to this as the need for a “life-centricity” instead of a “product-centricity” customer approach using generative AI to cull and curate from the abundance of customer data available to banks.

Properly harnessed, such data could enable banks to tailor fit advice and propositions, cementing a more personalized, mutually productive, and long-lasting relationship between the bank and its customers.

However, the successful deployment of generative AI also requires technically skilled human resources that currently need sufficient supply relative to the growing demand from the banking industry as more banks plunge into the digitization frontier.

There are also significant financial, legal, and reputational risks that go hand-in-hand with generative AI, such as the prevalence of data hacking that could result in financial losses arising from breaches of data privacy regulations, customer dissatisfaction, and litigation.

Until next week… OBF!

(To be continued)

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