Five generative AI use cases for the financial services industry
Managing Director, Regulated Industries
Managing Director, Global Retail Banking Solutions
Generative AI has the potential to revolutionize the way we live, work, bank, and invest. Its impact could be as significant as the advent of the internet or the mobile device. Indeed, 82% of organizations considering or currently using gen AI believe it will either significantly change or transform their industry (source: Google Cloud Gen AI Benchmarking Study, July 2023).
First and foremost, gen AI represents a massive productivity and operational efficiency boost. Especially in financial services, where every service or product starts with a contract, terms of service, or other agreement. Gen AI is particularly good at discovering and summarizing complex information, such as mortgage-backed securities contracts or customer holdings across various asset classes.
But it doesn’t end there. Foundational models, such as Large Language Models (LLMs), are trained on text or language and have a contextual understanding of human language and conversations. These capabilities can be particularly helpful in speeding up, automating, scaling, and improving the customer service, marketing, sales, and compliance domains.
Gen AI can act as an assistant or a coach to employees by helping them do their job more efficiently and ultimately enabling them to focus on strategic, high-impact activities. For example, coding assistance and generation, such as Codey, which is a family of code models built on PaLM 2, can dramatically increase programming speed, quality, and comprehension. Using gen AI can help address some of the most acute talent issues in the industry, such as software developers, risk and compliance experts, and front-line branch and call center employees.
5 practical use cases for the financial services industry
Gen AI provides three main capabilities that can help businesses and institutions:
- Making online interactions conversational (e.g., conversational journeys, customer service automation, knowledge access, and others)
- Making complex data intuitively accessible (e.g., enterprise search, product discovery and recommendation, business process automation, and others)
- Generating content at the click of a button (e.g., creative, document generation, developer efficiency, and others)
Picking a single use case that solves a specific business problem is a great place to start. It should be impactful for your business and grounded in your organization’s strategy. This will enable you to measure the results easily.
Here are five use cases that can help you get started with gen AI.
1. Financial document search and synthesis
Banks spend a significant amount of time looking for and summarizing information and documents internally, which means that they spend less time with their clients.
Gen AI can help bank employees effectively find and understand information in contracts (e.g., policies, credit memos, underwriting, trading, lending, claims, and regulatory) and other unstructured PDF documents (e.g., ”summarize the regulatory filings of bank X”).
For example, gen AI can help bank analysts accelerate report generation by researching and summarizing thousands of economic data or other statistics from around the globe. It can also help corporate bankers prepare for customer meetings by creating comprehensive and intuitive pitch books and other presentation materials that drive engaging conversations.
Watch this demo to see how you can build an application for this use case.
2. Enhanced virtual assistants
Sometimes, customers need help finding answers to a specific problem that’s unique and isn’t pre-programmed in existing AI chatbots or available in the knowledge libraries that customer support agents can use. For example, assisting a customer resolve fraudulent transactions. That kind of information won’t be easily available in the usual AI chatbots or knowledge libraries.
That’s where gen AI comes in to help get customers the answers they need. It excels in finding answers in large corpuses of data, summarizing them, and assisting customer agents or supporting existing AI chatbots. Gen AI-powered chatbots can also be more conversational. These capabilities help provide improved customer service experiences. For example, in this video, we explore how gen AI can speed up credit card fraud resolution — a win-win for customers and customer service agents.
3. Capital markets research
To fully understand global markets and risk, investment firms must analyze diverse company filings, transcripts, reports, and complex data in multiple formats, and quickly and effectively query the data to fill their knowledge bases.
In capital markets, gen AI tools can serve as research assistants for investment analysts. Such assistants can help sift through millions of event transcripts (e.g., earnings calls), company filings (e.g., 10Ks/10Qs), consensus estimates, macroeconomic reports, regulatory filings, and other sources, and quickly and intelligently identify and summarize key information.
Watch this video to learn how you can extract and summarize valuable information from complex documents, such as 10-K forms, research papers, third-party news services, and financial reports — with the click of a button.
4. Regulatory code change consultant
In the financial services industry, new regulations emerge every year globally while existing rules change frequently, requiring a vast amount of manual or repetitive work to interpret new requirements and ensure compliance. Developers need to quickly understand the underlying regulatory or business change that will require them to change code, assist in automating and cross-checking coding changes against a code repository, and provide documentation.
Gen AI can give developers context about the underlying regulatory or business change that will require them to change code by providing summarized answers with links to a specific location that contains the answer. It can assist in automating coding changes, with humans in the loop, helping to cross-check code against a code repository, and providing documentation.
For example, today, developers need to make a wide range of coding changes to meet Basel III international banking regulation requirements that include thousands of pages of documents. Gen AI could summarize a relevant area of Basel III to help a developer understand the context, identify the parts of the framework that require changes in code, and cross check the code with a Basel III coding repository.
5. Personalized financial recommendations
While existing Machine Learning (ML) tools are well suited to predict the marketing or sales offers for specific customer segments based on available parameters, it’s not always easy to quickly operationalize those insights.
For example, creating marketing emails or in-app messages with specific financial recommendations can be time-consuming. Gen AI can help in the creative process of one-to-one personalized messaging at scale using conversational language. It can help improve customer experience, retention, and cross sales.
You can start implementing these use cases using Google Cloud’s Vertex AI Search and Conversation as their core component. With Vertex AI Search and Conversation, even early career developers can rapidly build and deploy chatbots and search applications in minutes.
From vision to practice
Financial services leaders are no longer just experimenting with gen AI, they are already way building and rolling out their most innovative ideas.
For example, Deutsche Bank is testing Google Cloud’s gen AI and LLMs at scale to provide new insights to financial analysts, driving operational efficiencies and execution velocity. There is an opportunity to significantly reduce the time it takes to perform banking operations and financial analysts’ tasks, empowering employees by increasing their productivity.
MSCI is also partnering with Google Cloud to accelerate gen AI-powered solutions for the investment management industry with a focus on climate analytics.
Dun & Bradstreet recently announced it is collaborating with Google Cloud on gen AI initiatives to drive innovation across multiple applications.
Gen AI isn’t just a new technology buzzword — it’s a new way for businesses to create value. While gen AI is still in its early stages of deployment, it has the potential to revolutionize the way financial services institutions operate.
For more details on jumpstarting your journey, download our eBook, The executive’s guide to gen AI.
About the Google Cloud Generative AI Benchmarking Study
The Google Cloud Customer Intelligence team conducted the Google Cloud Generative AI Benchmarking Study in mid-2023. Participants included IT decision-makers, business decision-makers, and CXOs from 1,000+ employee organizations considering or using AI. Participants did not know Google was the research sponsor and the identity of participants was not revealed to Google.