How Financial Services Can Say Yes to AI & Stay Compliant

Here are some common questions from risk management teams and compliance officers — and the answers they’re looking for.

Shane Closser.

By Shane Closser

Sep 18, 2023

4 min

Attitudes in the financial services industry are changing. Risk management and compliance teams are becoming more progressive. They want to find a way to say "yes" to AI — as long as it will deliver revenue, achieve client satisfaction, and still adhere to industry regulations.

The first step is to identify a use case for AI. These use cases should generate impact by:

  • saving your team's time

  • saving money on other tools, and/or

  • increasing incremental revenue by streamlining or improving client interactions

Once you've identified your use case(s) for AI, it's time to take industry regulations into account. Here are some common questions to expect from your risk management teams and compliance officers — and how you can provide the answers they're looking for.

How will you prevent AI from making mistakes or having hallucinations?

Large language models (LLMs) are a type of artificial intelligence that mimic human communication. Common use cases for LLMs in financial services are content generation, chat, and natural language search.

But LLMs can also hallucinate, or make up answers — leaving your brand vulnerable. If information isn't available, the model will take a guess to provide an answer. Your risk management teams want reassurance these hallucinations won't happen — especially in front of clients and potential clients.

AI needs to be able to draw on all a business' facts and information in a centralized, organized place. By structuring your data in a platform (like your headless CMS, for example) you give the model a strict set of information to reference. This is an essential safeguard for financial professionals and organizations using conversational AI to personalize client experiences. This places safety rails around the use of AI — and is integral for financial services organizations interested in adopting conversational AI specifically.

Can you prevent AI from overstepping promissory language boundaries?

This is an important question, and your legal and compliance teams will be very interested in the answer. How will you prevent AI from overreaching the bounds of regulatory guidelines?

For risk management, your AI solution needs to provide controls that help you meet regulatory requirements. You'll want to configure your model to only respond with information that's in your pre-approved collection of knowledge, and always includes the proper disclosures. This allows you to use AI while making sure the information stays within the bounds of your controls.

Will confidential and proprietary information be protected?

You now know that you need an AI solution that is trained not to answer questions outside of your pre-approved dataset. But your compliance teams know that using a public LLM can be dangerous because user inputs can make it into the broader algorithm.

You should control the information that is allowed into the LLM, and you should also control which LLM models are used. When you choose your supporting AI technology, make sure that you have control over what information the technology can and can't reference.

By using an AI model that references your pre-approved, structured information, you can control its output — and put your compliance team's fears to rest.

In January 2023, a case named Anderson v. Stability AI, et al. was brought forward. Artists sued generative AI platforms, claiming these platforms are using their protected works to train the AI algorithms — without a license.

Your legal and compliance teams will undoubtedly want to protect your institution from any similar allegations of copyright infringement. By structuring your data and limiting the model to only reference and surface information in your headless CMS (like your business information, products, FAQs, and financial professionals), you ensure that your AI model is only using pre-approved knowledge and content. The scope of its responses will be limited to what is managed by you within your headless CMS. The headless CMS can also manage disclaimers for this type of content.

For another layer of protection, consider using an in-platform suggestions workflow for added supervision and control.

It all comes down to a single source of well-managed information for your AI model to reference.

By preparing a single source of truth before integrating AI into your organization, you give your clients and professionals a great experience as soon as you launch. This creates faster time-to-value for both your financial professionals and clients.

In short, don't let regulations stop you from using AI. Instead, use the regulations to deploy AI properly, enriching client experiences without putting your organization at risk.

Embrace that regulations exist to protect your clients and your business. Meeting these regulatory requirements is entirely possible. Then, you can use AI as a powerful tool all throughout the client journey — from no-touch client interactions (like self-service) to high-touch conversations (which produce high net value for teams of financial professionals).

Up Next: How to Use AI (and Manage Risk) to Personalize Experiences for Financial Clients

Shift away from theoretical AI use cases, and towards a more practical application. Learn how you can personalize client experiences today — all while embracing regulations that keep your organization safe.

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