The financial services sector continues to make notable progress with gen AI. In just the past couple of months, we’ve seen the launch of several gen-AI powered solutions, including Morgan Stanley’s tool that summarizes video meetings and generates follow-up emails, as well as JPMorganChase’s AI assistant LLM Suite. Others, like BNP Paribas and TD Bank, have announced important partnerships with gen AI model builders.
The industry’s AI spend is projected to rise from $35 billion in 2023 to $97 billion by 2027, which represents a compound annual growth rate of 29%. The largest players are aggressively investing in developing their AI infrastructure and scaling use cases to capture more value. Daniel Pinto, JPMC’s President and COO, recently estimated that gen AI use cases at the bank could deliver up to $2 billion in value.
The question now is what will financial services do next and how soon will they apply AI across the entirety of their organizations and more broadly with customers.
Based on my conversations with senior leaders and through Accenture’s work running the FinTech Innovation Lab, I predict we’ll see gen AI evolve over two time horizons: one that’s happening now — which will see the rapid adoption of AI assisted tools and the availability of technologies for handling unstructured data and data gathering — and one further into the future, with more sophisticated applications as the infrastructure, modeling and regulatory considerations advance.
The more immediate time horizon is seeing FS firms focus on four areas:
1. AI co-pilots – Co-pilots that work alongside employees will streamline workflows and provide new insights, leading to significant productivity improvements. Citizens Bank for example, expects to see up to 20% efficiency gains through gen AI as it automates activities like coding, customer service and fraud detection. In the future, these co-pilots could tailor investment strategies in real-time or predict market trends, helping to fortify FS firms’ competitive edge and deliver differentiated client outcomes.
2. AIways-on AI web crawlers – These web crawlers continuously gather and analyze data from various web sources and public records. They can track real time financial news and market movements while detecting subtle changes in consumer sentiment on social media platforms, alerting banks to the potential risks and opportunities while enabling proactive management.
3. Automating unstructured data tasks – Gen AI systems will process and analyze unstructured data—emails, documents, and multimedia content—transforming it into structured, actionable insights and reducing the time traditionally required for data management. This shift will allow employees to concentrate on more high value tasks, like strategic decision-making and creative problem-solving. We’ve already seen financial services clients deploy a large number of highly-focused generative AI agents that can automate processes with little or no ongoing human input.
4. Hyper-personalization – Banks and others are leveraging AI and non-financial data to better create and target highly personalized offerings. This is shifting the paradigm in FS from a reactive service to one that is truly intuitive and responsive. Take Klarna’s AI assistant as an example. It now handles two-thirds of customer service interactions and has led to a decrease in marketing spend by 25%. Rather than reactively engaging when customers have a request or issue, it could eventually anticipate and proactively reach out to customers before they even know something is wrong.
Further into the future – risk management and new propositions via synthetic data
As the technology and infrastructure advance to allow for more sophisticated models with larger data sets at a lower cost than today—and as regulatory policy takes shape—we expect to see financial services delve deeper into using gen AI to tackle risk management and to deliver a richer customer experience.
Gen AI could play a critical role in risk management through the creation and use of synthetic data, which will become essential in enhancing the accuracy of predictive models, especially for fraud detection, and help financial institutions proactively safeguard against threats and make more informed decisions. One European neobank, bunq, is already using generative AI to help improve the training speed of its automated transaction monitoring system that detects fraud and money laundering.
More broadly, gen AI could transform compliance and security measures, enabling firms to meet regulatory requirements more efficiently while reducing the cost and effort involved in combating financial fraud and managing risk.
Synthetic data could also lead to a better customer experience through the designing and testing of new propositions, such as loans or investments. Banks can use the data to simulate how customers might respond to these new products or to other scenarios, like a financial recession. Some FS firms are already trialing tools in this space, but it may take some time before they are truly enterprise ready.
Fintechs will help democratize gen AI
Fintechs remain at the forefront of harnessing gen AI and many of their use cases and solutions are impacting financial services. For example, Synthesia utilizes an AI platform to create high-quality video and voiceover content tailored for financial services, while Deriskly provides AI software aimed at optimizing compliance in financial promotions and communications. Others include Reality Defender, whose deepfake detection platform helps banks, insurers and governments detect AI-generated content at scale, and Hyperplane, a data intelligence platform that lets financial institutions develop personalized experiences and predictive models through proprietary large language models (it was recently acquired by Nubank).
Many fintechs will play an enabling role by helping to democratize gen AI’s capabilities for mid-market and smaller financial institutions, allowing these firms to leverage gen AI in a way that currently is only available to the largest FS players in the world.
Financial services have made considerable progress adopting gen AI in the last two years. While there’s been a sizable focus on efficiency and cost optimization thus far, many FS CIOs are eager to deliver top line growth. To do so, they’ll need to work closely with the business to consider how gen AI can lead to new ways of working, new products and new capabilities that can help accelerate revenues. The future of AI in financial services looks bright and it will be interesting to see where firms go next.
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