GenAI in Financial Services: a New Era of Opportunity and Risk

GenAI’s potential has dominated headlines, spawned countless ideas, and swamped financial services organizations with trial proofs of concept. Now, leaders need to stop experimenting and realize the benefits at scale to stay ahead of the competition

Our recent whitepaper, co-produced with Google Cloud and Deloitte, delves into how financial services firms are scaling GenAI for maximum impact. Here is an excerpt from the report, you can download the full report for free here.

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GenAI in financial services: a new era of opportunity and risk

A recent Google Cloud Banking survey found that 96% of banking executives have seen their C-suite and board of directors become more involved in technology-related decisions due to heightened interest in GenAI.

“This is a generational leap in technology. This will define how we approach and use technology for the next two decades, if not longer,” says Gopal Srinivasan, Global Alphabet Google Generative AI Leader at Deloitte US.

Srinivasan says that GenAI technology should be approached proactively, as financial services organizations have unique needs and can’t risk being left behind by the competition.

“The technology is such that it should be uniquely moulded and applied to a specific organization’s needs. This is a time when no one should sit back and ‘wait and see’ what others do,” Srinivasan concludes.

However, the path from potential to profit for GenAI is full of challenges and risks. Leaders in financial services should decide how to adopt the technology, where to deploy it, and how to scale it. Moreover, leaders want to understand the risks the technology itself may pose so they can understand the impact on their regulatory controls and risk appetite.

“It's the risk of getting it wrong. It's the risk of hallucination. It's the risk of the regulatory interpretations along with the potential ethical risks. These things do keep leaders awake at night. But the opportunity is significant as well,” says Andy Lees, Global Financial Services Google Lead at Deloitte UK.

Despite these challenges, GenAI’s ability to quickly analyze and synthesize vast amounts of data should make it an indispensable tool for the financial services industry.

GenAI in banking: creating efficiency and improving customer experiences

Nearly half of the executives surveyed in our Google Cloud Banking survey1 said that the top expected benefit from GenAI is increased operational efficiency and cost savings. 
Realizing the benefits of GenAI couldn’t be timelier for banks as they wrestle with unpredictable financial pressures.

“The cost of services, whether from the branches or in the back office, is increasing. The cost per account is increasing. But the net interest margin isn't – there’s a lot of compression there,” says John Froese, Head of Banking and Wealth Management at Google Cloud.

Some early use case examples for banks include generating content designed to enhance employee productivity, summarize complex financial information and enhance chatbots.

“Large consumer banks are looking at how to streamline their front and back offices,” says Froese. “They’re looking at ways to give key decision-makers and service staff more information. Today, the focus is on operational efficiency because it's less complex and involves less risk than some truly customer-facing applications.”

GenAI also has the potential to improve the guidance, advice, and support that consumer banks provide to their customers, and do it at scale.

“With the help of GenAI technology, we can create digital banking advisors who can perform similar functions to humans and scale to the long tail of customer needs,” says Srinivasan. “We can provide our customers with effective guidance for managing their investments and getting everyday questions answered, and that can be a huge force multiplier for the customer experience.”

GenAI in insurance: revolutionizing the front and middle offices

Elements of back-office operations were among the first to be automated in the first wave of AI and machine learning transformation. However, meaningful improvements in the middle and front offices have been more challenging to achieve.

Executives see GenAI as the next step in operational cost optimization and tech-powered productivity gains. Insurance companies, in particular, stand to benefit from operational automation, code generation, and tools that empower claims adjusters' and underwriters' decisions.

“Every time there is a policy application, there is an underwriter. That underwriter must pore through guidebooks and rulebooks that can run into hundreds of pages. It's a highly laborious task. It is time-consuming, prone to human error, and expensive,” says Srinivasan.

One of GenAI's most significant benefits over earlier technologies is its ability to seamlessly produce insights based on synthesized data from various structured and unstructured data sources.

“If we consider an auto accident or property damage claim, we typically have a statement, pictures, or a video. Imagine having GenAI break down the dashcam footage frame by frame. It could tell you if the cyclist ignored a red light or if a driver was distracted by their phone at the moment a traffic light turns green,” says Nigel Walsh, Managing Director and Head of Global Insurance at Google Cloud. “That’s why multi-modal capabilities are a game-changer. It would significantly simplify their investigative process. More importantly, it would insurance companies get more value out of the information, making their job easier, more efficient, and more effective, helping to drive better profitability and improved loss ratios.”  

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You can explore this topic and many others in more detail at CDAO Chicago 2024 taking place on August 7-8, 2024. Check out the full line-up of world leading speakers here.