Vasi Philomin is VP of Generative AI at AWS. He leads generative AI efforts, including Amazon Bedrock and Amazon Titan.
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The financial services industry has been an early and enthusiastic adopter of generative AI, which is estimated to create an additional $200 billion to $340 billion in value annually in the banking industry alone, according to McKinsey. Taking a look at this sector’s use of generative AI can provide valuable insights to any organization interested in putting this transformative technology to work to solve major business challenges.
I’ll start with a few examples of the wide-ranging generative AI activity in financial services. Intuit, the global financial technology platform, is building a generative AI-powered assistant that will be integrated throughout all its products—giving users personalized insights that help them make smart financial decisions. Global macro manager Bridgewater is creating a secure large language model-powered Investment Analyst Assistant that will be able to generate elaborate charts, compute financial indicators, and create summaries of the results, based on both minimal and complex instructions. Leading P&C insurer, Travelers, has identified numerous use cases where generative AI can enhance risk management and enable more efficient insurance processes in areas like knowledge management. Today the company has deployed AI in production for risk selection, risk control and claims handling. (Disclosure: All companies mentioned in this article collaborated with AWS on these projects and used AWS cloud services.)
Start With The Business Challenges
In my experience, the right first step when adopting generative AI is to look within the organization to identify key business problems that need to be solved versus getting lost in technical details. Financial services leaders have clearly taken this path, identifying specific issues—from detecting fraud to providing more customized investor recommendations—that need to be solved, then piloting initial generative AI use cases to begin to address them.
Given the diversity and scale of the financial services industry—which includes banking, capital markets, insurance and payments—there are countless opportunities to leverage generative AI. However, popular use cases are emerging that have relevance far beyond financial services.
Letting Knowledge Workers Be More Efficient And Creative
In an industry awash with data and documents, generative AI tools are helping financial analysts, financial advisors, loan officers and others by taking the heavy lifting out of research. Today, these knowledge workers spend a lot of time searching for, aggregating and summarizing text—work that’s ideal for generative AI.
Generative AI has the potential to reduce time-intensive manual tasks for knowledge workers by training a model and using its application to perform that work automatically. These models can then be connected with ever-evolving information, such as the latest regulations. By freeing up their time, generative AI lets knowledge workers use their talents to think creatively and explore new initiatives.
Improving The Customer Experience—At Scale
Financial services organizations are using generative AI to streamline and improve customer interactions, enhance the customer experience—and create a competitive differentiator. Consider the call center, that nexus of customer interactions. Generative AI can expedite onboarding/training for customer service agents and provide responses that align with a firm’s policies and guidelines.
It empowers representatives to answer complex customer queries more quickly, completely and personally. It puts detailed knowledge, recommendations and best actions at a representative’s fingertips during customer interactions, drawing on the individual customer’s financial goals and other contextual criteria.
Empowering customer service personnel is a good first step toward empowering actual customers with advanced capabilities, which promises to be a major use case. In fact, a 2023 KPMG survey of financial services executives found that more than 60% of respondents anticipated launching a first-generation AI solution for their customers in the near future.
On the back end of the process, data captured from customer interactions, such as call transcriptions and chatlogs, can be summarized and analyzed by generative AI to help understand the reasons behind positive or negative customer experiences. Organizations can use these insights to create a more positive, consistent experience.
In the past, much of this work would have been handled by time-consuming data entry and analysis, which gave financial organizations a massive amount of data, but not necessarily insights. Now, generative AI helps improve and personalize customer experiences at scale, a key concern for financial services organizations with hundreds of thousands (or millions) of customers.
Learning From The Early Innovators
What can organizations in other sectors learn from these early use cases in the financial industry? At a high level, it’s clear that generative AI is having a major impact on the employee and customer experience. Generative AI can bring major efficiency gains to any organization overwhelmed with data and documents (in short, all organizations). It reduces drudge work for knowledge workers—freeing more of their time for creativity and innovation. It can improve the customer experience by streamlining processes, providing faster/better responses and giving customers frictionless access to information, answers, recommendations and expertise.
There’s also much to be learned from how financial services teams are implementing generative AI. These organizations inherently deal with sensitive personal data, so they are focused on ensuring security and privacy when envisioning any possible use case. They’re building stronger, more streamlined data foundations to serve generative AI, establishing vigilant new governance processes and experimenting with multiple models to find the right match with their needs. These critical initiatives would serve any organization well, no matter what sector they are in.
Facing Similar Challenges And Goals
Financial services organizations are highly regulated and competitive—and uniquely motivated to explore generative AI since their competitive edge is at stake. However, they share many of the same goals as organizations beyond their sector—raising efficiency, improving the customer experience, and transforming processes via innovation. Executives in other industries would be well-advised to learn from the financial sector to inform their exploration and adoption of generative AI today—and to get early insights on what’s ahead.
The information provided here is not investment, tax or financial advice. You should consult with a licensed professional for advice concerning your specific situation.
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