The opinions expressed here are those of the authors. They do not necessarily reflect the views or positions of UK Finance or its members.

Though some of the hype around Generative AI (Gen AI) is cooling down, the business opportunities are real. According to research conducted by World Economic Forum, AI will add 14 per cent increase to global GDP by 2030, equivalent to a growth of $15.7 trillion! Another research by one of the global tech companies suggests that 750m new applications will be built in the next 2 years, many will use AI and drive productivity in all sorts of ways.

Gen AI is already transforming multiple areas within financial services, with the top three use cases emerging in customer service, risk management, and software development. Large Language Models (LLMs) are proving particularly effective in tasks such as summarisation, content generation, classification, semantic search, code generation, and data extraction—leading to estimated productivity gains of up to 40 per cent.

Generative AI can have a big impact in fraud detection. By analysing massive amounts of transaction data, AI can identify unusual activity and flag potential fraud before it becomes a bigger problem. Language models (LLMs) are especially good at working with text data, which means they can help financial institutions analyse customer feedback, review documents quickly, and protect sensitive information.

Many banks and insurance companies continue to rely on decades-old core transaction processing systems, often written in legacy programming languages that few professionals are skilled in today. AI is now playing a key role in modernising these systems. By reading and analysing legacy code, AI can generate business logic, translate outdated code into modern programming languages, and assist in application testing. . IBM is successfully using Gen AI to reverse engineer and modernise these legacy platforms. This approach has been shown to cut modernisation costs and timelines by more than 50 per cent, significantly improving return on investment.

While AI has the potential to transform customer interactions, and business operations in financial services, adoption of this technology comes with its challenges. Given the industry is highly regulated the question of how to safely exploit AI is as important as where you are going to apply it. There are several considerations for building trusted AI, including data privacy, IP, transparency and explainability, compute and carbon cost, skills scarcity and, most important of all, governance.

Strong governance is central to building trusted AI, especially in financial services. It is crucially important to understand what AI models the organisation has, the data that the organisation tunes and applies those models to, the models’ intended uses, and their compliance with regulations. At least five countries have AI regulations, and two-thirds of the world's countries have privacy and data governance laws. We are supporting many banks and insurance companies in UK develop strong AI governance and compliance to regulations. 

Generative AI is all about people, so industry needs to invest in training people on AI. Putting this technology into the hands of users, across all functions and lines of business—not just technology users—allows everyone to understand how transformative it can be to their role and the workflows around them. IBM is driving AI-first skilling across its enterprise, with a goal to train 2 million learners in AI by the end of 2026. As AI continues to transform the financial services industry, the challenge lies in upskilling people many of whose jobs will dramatically change.