Top Use Cases for Generative AI in Banking, FSI, & Insurance

Use Cases & Projects Lynn Heidmann

Here's the scoop on Generative AI, according to a June 2023 Dataiku + Databricks survey of 400 senior AI professionals. A whopping 64% of organizations across all industries will “Likely” or “Very Likely” use it over the next year. Plus, 45% of respondents said they are already experimenting with Generative AI. This includes large language models (LLMs), the tech behind ChatGPT.

→ Read Now: Beyond the Chatbot — The Path to Enterprise-Grade Generative AI

How do financial institutions stack up? Generative AI aside, respondents in financial services, banking, or insurance show signs of higher maturity compared to other industries, with:

  • More mature levels of adoption of artificial intelligence (AI) — 54% are at “expanding” or “embedding” stages.
  • More mature organizational structures, with lines of business more likely to have embedded AI capabilities.
  • The highest estimated returns on investment for AI initiatives among other industry peers.
  • More robust systems in place for measuring AI performance and value.

Let's look at respondents from the financial services, insurance, and banking industries when it comes to Generative AI. A whopping 61% say they will “Likely” or “Very Likely” use the technology over the next year, with 42% already experimenting. By all accounts, it's safe to say the Generative AI race is on.

Dataiku built a full Generative AI Use Case Collection using experience gained from working with our more than 500 customers. From risk management to customer service, this collection of use cases goes beyond the boundaries of traditional AI-powered chatbot functions. And here are our top picks for real-life Generative AI use cases in banking.

LLM-Enhanced Next Best Offer

In today’s competitive landscape, you’re probably constantly seeking ways to optimize customer engagement, increase customer satisfaction, and maximize revenue. Next Best Offer (NBO) solutions have emerged as a powerful tool in achieving these goals. 

In fact, Dataiku has its own plug-and-play NBO solution. This solution enhances the efficiency of marketing campaigns and advisory activities through:

  • Intelligent targeting
  • Product-offer personalization
  • In-depth customer insights

However, LLMs can take this even one step further. LLM-enhanced NBO takes these product offers and other recommendations from a classic machine learning system. It then enables banking sales professionals to generate customized yet automated follow-up messages with the power of Generative AI.

Bonus: The Generative AI model provides the same high level of performance across many languages. Perfect for banking professionals that speak or manage customers in more than one language.

Plus, this application is the right balance of personalization and control. Banking professionals oversee the message content before sending it to the customer. They can even add a (non LLM-generated) disclaimer that has been approved by the legal department.

Exploration of Insurance Contracts and ESG Documents

One of the big advantages of Generative AI — more specifically large language models (LLMs) — is the ability to read and summarize large amounts of text.

Imagine leveraging this technology to speed up customer support. Readily available contracts and legal information fuel instant answers for detailed policyholder questions like:

  • Will I receive reimbursement if I miss my train?
  • Is my contract covering me for damages linked to flooding?
  • Can I get access to a specific network of partners to repair my car?

 

Similarly, imagine leveraging the power of Generative AI to create environmental, social, governance (ESG) insights from a large and complex corpus of documents in seconds.

For example, credit or equity analysts could simply ask questions like these in natural language to generate readable insights from a large and complex corpus of documents, all with source citations:

  • Between Company 1 and Company 2, which is the company most exposed to environmental risks?
  • What is Company 3 policy to encourage gender equality and mitigate discrimination?
  • Was Company 4 involved in human rights controversies in the last five years?

Dataiku has built out both of these use cases. Generative AI can ultimately be used to increase efficiency and focus analysts or support agents on more high-value tasks.

See this use case in action below. Or learn more about the Dataiku Insurance Contract Explorer and LLM-Enhanced ESG Document Intelligence.

 

The Regulatory Challenge for Generative AI & Banks

To be sure, financial institutions, banks, and insurance companies have an additional hurdle to jump when it comes to Generative AI. That is, of course, more stringent requirements for AI governance and explainability.  

That’s why the right processes and oversight for safety are critical. This was true before Generative AI, and it's even more true for those leveraging it.

At Dataiku, we’ve built the RAFT framework for Responsible AI. It builds upon this baseline set of values for safe AI, whether Generative AI or not. As with any framework, we encourage governance, ethics, and compliance teams to adapt it. Teams might make changes for specific industry requirements, local regulations or additional business values.

→ Get the Ebook: Building Responsible Generative AI Applications

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