Artificial Intelligence in Financial Services 2025

Seeing early value from AI implementation2 After achieving success from efficiency opportunities, businesses are developing future strategies backed by lessons learned and recurring themes. It is clear from the above that there are countless potential applications of AI within the operating models of financial services firms; however, most surveys indicate that the primary objective today is efficiency. This is understandable, as cost-reduction programmes tend to deliver quicker and more directly measurable results than growth or risk management programmes. Dramatic outcomes go a long way towards building support for continued investment. Nonetheless, 70% of financial services executives believe that AI will directly tie to revenue growth in upcoming years5 by enhancing customer experiences, making products and services more personalized and relevant, enabling innovative new offerings, empowering cross- and up-selling, and improving security against potential threats. There are, of course, many ways in which AI can enable these improvements. As financial services leaders continue to identify and prioritize new use cases and develop their investment strategies, however, there are common themes that frequently recur: –Personalized customer experiences: AI-powered virtual assistants can provide thorough, tailored, round-the-clock support for customers’ more basic issues (e.g. product recommendations or applications); for complex enquiries, they can augment the capabilities of human agents, allowing for quicker, more relevant responses (e.g. questions around investment decisions and portfolio allocation adjustments). –Product innovation and revenue growth: genAI enables financial services firms to address market niches, such as mass-affluent advisory services, more effectively than was previously feasible. It is also enabling innovation and the creation of new revenue streams (e.g. by combining synthetic customer data with more efficient A/B testing for new deposit and lending products). Through its ability to process and synthesize data, it can also identify and help realize opportunities for up- and cross-selling. –Data-driven decisions: The power of AI to convert large amounts of data into useful, tailored insights allows the technology to enhance investment decisions, improve risk assessments and generally help business leaders make better day-to-day decisions – allowing leaders to spot key trends (e.g. in transaction volumes, customer feedback or market trends). –Improved risk management, compliance and security: AI can scrutinize data, transactions and other events more thoroughly, quickly and tirelessly than humans can. It is being used to constantly monitor for cybersecurity threats and to identify suspicious activity in real time. It can also be hugely helpful in relieving data collection and reporting burdens – especially during the mandatory identification process of customers, known as KYC (know your customer). of financial services executives believe that AI will directly tie to revenue growth in upcoming years.70% 11 Artificial Intelligence in Financial Services