The promise of AI for banks is huge, but concerns abound

Kanv Pandit January 11, 2024 10:23

It has been a year since OpenAI launched its text-generating artificial intelligence (AI) chatbot, ChatGPT. There are a number of risks associated with ChatGPT, and as companies try to navigate these risks and capitalize on the rewards to stay competitive, it’s essential business leaders understand the possibilities of this emerging tech.

In the world of work, generative AI changes the game by encroaching - in theory at least - on a wider range of roles. But is this all just hype? And where do these latest advancements leave the financial institutions (FIs) that, until now, have been happy to harness the power of AI?

How AI has transformed finance

Pre-ChatGPT, AI tools played a significant part in the increased automation of critical processes. Machine-learning (ML) tools in particular have taken digital transformation to the next level for financial institutions by making far better and faster use of data than other technologies or human beings.

In addition to turbocharging banking processes and saving human employees time, AI tools can help financial institutions build a fuller, more in-depth and more dynamic picture of their customers and prospects. FIS’ Global Innovation Report found that over 40% of financial services firms in Hong Kong are currently using Generative AI and 45% are using other AI technologies.

There are several use cases to explore. For commercial lenders, for example, that could mean seeing where firms fit into the macroeconomic environment and their industry sector. In the financial cycle, they typically need access to funding or insight on how lending affects environmental, social and governance (ESG) scores.

By drawing on more data and digging into the details, AI could also help financial institutions identify lending opportunities that lesser technology might reject out of hand. And as credit risk rises once more, the faster, deeper insights it can provide are ever more critical to informing decisions, outthinking the market and overtaking the competition.

Above all, AI gives financial institutions the ability to draw on a wider, richer set of data: not only historic financials, but also covenant, transaction and market data, news feeds and social media. It's these more current data sources that can show how things are for business customers - and predict where they could be heading.

Generative AI: The Game-Changer

So far, then, so good: AI tools are already proving their ability to not only make day-to-day working lives easier but also forecast future challenges. But in just a few months, generative AI has rapidly gone several steps further. Rather than simply replicating manual processes and human decisions, tools like ChatGPT dig even deeper into available data to create their own textual or visual content.

Generative AI has been making headlines globally. In addition to consumer usage of ChatGPT, businesses are now experimenting with the technology. For instance, Chinese tech firms Baidu and SenseTime have launched their ChatGPT-style AI bots Ernie Bot and SenseChat.

Despite the benefits, Generative AI is both astonishingly clever and a potentially dangerous way to cheat the system. First, it introduces major, more sophisticated opportunities for fraud. Suddenly it's possible to automatically produce an entire, persuasively written dissertation or a disturbingly lifelike deepfake image. What's to stop generative AI from faking documents to show a credit applicant is more profitable and creditworthy than they are?

Within financial institutions, the emergence of generative AI tools is sparking other fears. For example, a number of large banks on Wall Street and beyond have banned the internal use of ChatGPT while they assess concerns about data privacy, cybersecurity and access to systems.

Finally, of course, generative AI only heightens the anxiety that robotic tools will replace more and more human jobs. According to this argument, it doesn't just automate repetitive, mindless tasks - it puts higher-powered roles at risk, too.

Collaboration in the age of AI

But once the financial services industry has addressed its valid concerns about generative AI, there are many positive use cases for financial institutions to explore. These could include regulatory requirements, customer analysis, fraud and risk mitigation and product generation.

We are witnessing a collaborative effort between regulators and industry players to propel the adoption of AI and related technologies. Hong Kong has been actively taking progressive steps in the fintech space, with initiatives such as HKMA's "Fintech 2025" strategy and the recently introduced Fintech Promotion Roadmap. Major financial services players are investing in practical AI applications, directly or indirectly, through inhouse technology upgrades, offering support to promising startups or even university research and student projects, to build up PoC (proof of concept) cases. All these endeavours showcase the collective belief in the positive potential of AI and the commitment to harness its capabilities in the most effective manner.

There's much that AI can add to a business, however ultimately what all AI tools lack is the human ability to read between the lines, to understand, if not empathize with, the nuances and subtleties of human behavior. AI can make a massive, redefining contribution to human decision making, but it shouldn’t replace it. Side by side with AI tools, emotional intelligence, human experience and human judgment still have a major part to play in business.

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Kanv Pandit

Group Managing Director, APAC, Banking Solutions at FIS