New challenges for generative AI in finance

The financial services sector has long served as a testing ground for the application of emerging technologies. The current period of turmoil is no exception to this history. Generative artificial intelligence (AI) is the latest in a series of innovative technologies that are reshaping finance and banking, with applications for everything from enhancing consumer interactions to refining risk assessment models. . Although its influence is already crucial in financial decision-making, generative AI poses significant challenges. These include the risks of propagating false financial information, compromising the security of sensitive banking data, and widening the digital divide between modern economies and developing countries.

Banks and financial institutions (FIs) are adopting innovative approaches to mitigating the risks associated with generative AI integration and actively developing strategies to navigate these complexities. Additionally, the establishment and expansion of regulatory guardrails is critical to addressing these challenges and ensuring that the deployment of generative AI in the financial sector is safe and secure. The focus is not only on recognizing and exploiting the potential of generative AI, but also on highlighting the importance of strategic and regulatory frameworks to make the most of its capabilities.

Generative AI accelerates the transition of financial services to BaaS.

With the help of generative AI, the financial industry is accelerating the adoption of banking as a service (BaaS) and embedded finance, marking a shift from planning to implementation. A recent report revealed a significant increase in BaaS adoption across global financial institutions, rising from 35% to 48% in 2022. Similarly, embedded finance has seen significant growth, increasing by 8% in the past 12 months.

Generative AI is rapidly gaining traction in the financial sector, primarily as a tool to meet the growing demand for personalized customer service. However, its applications go far beyond this, encompassing important areas such as environmental, social, and governance (ESG) and anti-money laundering (AML) initiatives. With increased global implementation this year, generative AI has become an instrumental technology for advancing key areas of focus within financial services.

The expansion of AI in the UK financial sector brings challenges.

The emerging role of generative AI in financial services is significant, with around 90% of UK financial institutions already adopting predictive AI for back-office functions. While predictive AI in finance is primarily used to predict future events based on past data, generative AI creates new synthetic data and insights that influence financial modeling and analysis beyond existing patterns. To do. More than 60% recognize the potential of generative AI to drive significant cost savings and operational improvements. Supporting this level of optimism will require a thorough reassessment of business models, workforce capabilities, and the considerable resources needed for AI technologies, especially in terms of supply chain sustainability.

There is widespread alarm in the highly regulated financial sector, with more than 70% of generative AI applications still in the experimental stage. Achieving return on investment will depend on the quality of the data and the seamless integration of the technology into existing frameworks, a process that is expected to take three to five years for the average solution. The confluence of predictive and generative AI holds transformative potential, but also new challenges such as the now-infamous illusions and complexities that plague external model sourcing. Despite these hurdles, 60% of UK institutions feel ready to support generative AI within their current risk management strategies.