Financial Services Solutions
Generative AI in financial services IT modernisation
As a leading partner to businesses, including in the financial services sector, TEKenable has produced this guide to the fast-changing AI landscape, exploring what it means for financial enterprises, particularly in light of ongoing and much-needed IT modernisation processes.
Chief information officers (CIOs) and chief technology officers (CTOs) today find themselves tasked with reporting to the C-suite on the opportunities and risk of the AI boom.
New artificial intelligence (AI) technologies, such as large language models (LLMs), offer a lot of promise to banking, insurance and financial services, but need to be implemented with care, and as part of a broader IT modernisation strategy.
Although in its early days, generative AI is expected to have a significant impact on the banking and wider financial services sector.
Key initial applications for implementation of generative AI are in automation and efficiency, and workforce augmentation, including:
Augmenting human decision-making in areas such as financing or underwriting
Coding and development as part of IT modernisation
Extracting information from data; including chat logs and transactions, for a variety of purposes from marketing to fraud prevention
The key challenge facing financial institutions with generative AI is the fact that it must be integrated into existing technology stacks that are already widely considered in need of modernisation. In this context, AI can form a pillar in CIOs and CTOs making the case for upgrading enterprise technology architecture.
AI can be implemented in 3 key ways:
1. Using existing, publicly-available tools
2. Building out open-source AI tools on enterprise hardware
3. Deployment via private and hybrid cloud
Generative AI creates three key opportunities:
1. Increased revenue through greater efficiency and, eventually, development of new products and services
2. Retirement of technical debt
3. Bridging the gap between the business and underlying IT
The natural path for generative AI is deployment via private and hybrid cloud. This allows for flexibility and scalability, as well as leveraging the expertise of the likes of Microsoft and service provision partners, by applying AI to underlying core data.
Crucially, it does this in private instances, thereby ensuring no data leakage. To generate value, AI applications must be able to work both together as well as with existing systems.
The quickest win is to apply generative AI to already – modernised applications. This will not only create business value in its own right, but, crucially, serve as a showcase of the potential of AI and, as a result, help to overcome any inertia around IT modernisation.
Generative AI is not a tech fad and the AI boom that it has unleashed will have an enormous impact both across society and inside individual businesses. As a result, it cannot and should not be ignored. However, the risk of ignoring a technological revolution must be measured against other risks, including both the haphazard application of the technology and the novel risks that are inherent to the technology itself.
