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

Reflecting on the lessons from 2023, it's clear that innovation is crucial for financial organisations, extending beyond specific applications to redefine their operational foundations. Trends such as embedded finance, tokenisation, and cross-border payments have gained a strong foothold, paving the way for enhanced digital transformation, increased automation and the integration of GenAI. This transformative journey promises improved efficiency and reduced costs for financial service providers.

Innovation, despite its promise for higher productivity, comes at the expense of an increase in upfront investment costs. To offset this rise in investments (and often to fund such investments), financial institutions are now focusing on efficiency, leading to the emergence of back-office efficiency as an essential part of the financial narrative of 2024. The arrival of Large Language Models (LLMs) and GenAI platforms provides a great opportunity to both improve efficiency and reduce operational costs.

Intelligent automation, fueled by the prowess of GenAI, is being widely explored and piloted within banks' operations and technology departments. This transformation will reshape the industry, leading to increased efficiency and bring significant cost reductions. However, the widespread adoption of Gen AI powered intelligent automation necessitates implementation of robust safeguards to mitigate risks like fraud and money laundering.  For instance, if the system is not trained on diverse and representative datasets, it might overlook nuanced patterns in financial transactions. This oversight could result in false negatives, enabling fraudulent activities to slip through undetected. Additionally, if the model lacks adaptability to evolving fraud techniques, it may become obsolete in identifying emerging risks, compromising the bank's security measures.

I believe AI's role in enhancing consumer experience is pivotal, shifting from being a trend to be the primary vehicle of experience delivery. With ever emerging and evolving technologies, the competition for customer satisfaction intensifies. However, the reach of AI will extend far beyond user interaction. In the current economic climate of high inflation and living costs, the appeal of financial products like unsecured loans and BNPL is elevated. Emerging AI has the ability to positively contribute to managing risks across both of these product portfolios. For instance, in the realm of unsecured loans and Buy Now, Pay Later (BNPL) offerings, AI algorithms can analyse vast datasets in real-time, identifying subtle patterns indicative of potential defaults. By assessing individual creditworthiness more accurately and dynamically, AI-powered risk management mitigates financial risks, ensuring responsible lending practices and bolstering the overall stability of these product portfolios. The ability to analyse lots of data quickly will help razor-sharp decision-making by the credit engine. I anticipate visible AI progress in 2024 in such areas, building on initial post-COVID euphoria.

The other critical factor to consider is the positioning of AI within a financial organisation. There is a consensus that a ‘Digital Worker’, a.k.a Bot, can best be used as a co-pilot, but not the pilot-in-command. Natural human intelligence, often enriched with intuition, is not being completely replaced in the real-world. Employees in tech and fintech are urged to see AI as a facilitator, embracing its widespread adoption across diverse operations like customer service, risk management, fraud detection, and back-office functions among others. This will lead to increased and unhindered adoption, promoting further innovation.

As 2024 unfolds, the financial sector grapples with transformative shifts driven by digitalisation, AI, and evolving economic dynamics. Innovation, notably through GenAI and Large Language Models, propels operational enhancements. However, the surge in upfront investment costs underscores the need for efficiency. The integration of GenAI introduces potent tools for intelligent automation, revolutionising banking operations. Yet, caution is paramount, demanding robust safeguards to counter potential risks like fraud. Amidst this, AI's pivotal role in elevating customer experience and managing financial risks becomes evident, heralding a promising and progressive year for AI in finance.