Banking is a highly regulated industry to ensure stability of the financial system. This understandably makes bankers and their technology partners risk-averse. Compliance is critical and strictly enforced. However, use of AI in all technology, including banking, is inevitable. Hence, the banking industry needs to prepare itself.
Artificial intelligence (AI) enabler professionals
Enterprise-grade, plug-and-play AI banking products are still some distance away, but that doesn’t mean that there isn’t enough technological computing capability available to make a difference starting now!
Grok by xAI (an X, formerly Twitter company), Claude by Anthropic, Gemini by Google, Co-pilot by Microsoft, Perplexity assistant by Perplexity or chatGPT by openAI can all add immense value in their present form.
At this very moment, every bank in the world can benefit immensely from the nuanced hand of an experienced AI operator. AI isn’t another typical enterprise software, but a transformation of the very nature of existing work as well as the creation of new ways of working.
Why AI?
The technological landscape is one of the fastest changing landscapes in today’s world. With advancements in Artificial Intelligence (AI) becoming accessible, we are now able to harness their limitless capacity to introduce solutions and positive improvements to all industries, including banking.
Adoption challenges
- Risk-averse nature of banking
- Fundamental transformation of the meaning of work
- Extreme reliance on unaudited, unintelligible code clusters
- Redundancy in Information Technology clusters
1. Risk-averse nature of banking
A highly regulated and risk averse environment poses unique obstacles in adoption of new technology and technology solutions that include variability or in-house testing. Banks aren’t early adopters of new technology. By their very definition, they are the last movers of a proven and thoroughly tested technology and AI has barely scratched the surface of what’s possible.
2. Fundamental transformation of the meaning of work
Use of any kind of AI technology fundamentally transforms the very nature of work expected of the bankers and their technology partners. Building AI and working with AI is a change that’s as fundamental as the introduction of calculators, typewriters or computers themselves. Instead of shrugging this change, the banks should openly embrace the future.
3. Extreme reliance on unaudited, unintelligible code clusters
Most modern banks still rely on technology black boxes that were sourced and deployed several decades ago. With bank mergers, and typical several generations of turnover of IT personnel, not one person working today has complete understanding and knowledge of any of the banking systems, which makes even simple upgrades extremely difficult and challenging.
Without a starting point or even a fixed point, all attempts at improvements eventually end up being insignificant add-ons to the existing incomprehensible cluster of the black box. In the absence of clear visibility, the bankers maintain a position of ‘let things be as they are’ in order to avoid any possible risk arising from lack of knowledge of systems.
4. Redundancy in Information Technology resources
Introduction of AI based operators and AI solutions will streamline large clusters of technology and Information Systems. At present, all banks are deploying at least 5 times more IT resources than what is necessary simply because it’s impossible to do an audit of the entire banking technology landscape, largely for reasons defined in the previous section.
What next?
Upgrades and improvements happen iteratively
Unlike new enterprise software, AI solutions are not a complete overhaul. Instead, they start with a small footprint, adding value in small areas of existing work. The minor, iterative and rapid improvements collectively scale up to add a significant amount of value over time.
Leadership and management now need to know the subject matter
In the previous decade, technology was treated and purchased as a fixed built unit that operated in a particular way which required trained resources to operate it. With AI, this has changed completely where instead of buying the fixed unit technology, the banks instead buy technology components.
These components are then used in-house to build requirement specific solutions via internal teams or vendor teams. The management and leadership need to embrace the culture of inquisitiveness and exploration, and they need to learn to be comfortable with uncertainty and not-knowing as those are the first steps to shed light on a dark segment.
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Pulkit Saraf 