The 7 top pitfalls of AI chatbots in the financial sector – and how to avoid them.
The recent rise in large language models (LLM) have ushered in a new era of content generation and communication. For online brokers juggling high levels of support inquiries, with robust platforms chockfull of advanced datasets and tools, this presents the incredible opportunity to surface personalized insights on demand.
The challenge? Ensuring the highly sensitive and nuanced nature of financial information is delivered correctly
Understanding the Risks in AI tools
What are some of the risks associated with generic, open LLM models, AI chatbots within the financial sector?
- Input Quality: “Garbage in, Garbage out” is a common expression referring to how low quality data input results in subpar output. LLMs like ChatGPT read “too much” content including from unreliable sources.
- Output Quality: LLMs are generalists in nature and not trained for finance, by financial experts. If not trained and refined properly, the output may not be as desirable and relevant to financial investment as one may hope for.
- Compliance: Most compliance officers won’t be comfortable displaying AI generated content which may include inaccurate information to their individual investors.
- Hallucination: Large language models can occasionally generate content that appears factual but is inaccurate or entirely fabricated —a phenomenon known as “hallucination” - because LLM models are based on probabilistic formulae which includes a margin of error.
- Reputation: Providing your customers inaccurate information could harm their chances of making profitable decisions, deteriorating their portfolio return and trust in you as a broker.
- Overtrust in AI: The human tendency to overestimate AI’s capabilities or authority can lead some users within an open environment to assume responses may possess foresight or investment advice. This risk would be exacerbated if the A.I. application is presented as an advisor instead of an assistant.
- Meaningless Engagement: Recent studies indicate that retail investors can easily get lost in prompts that bots can’t answer. Within these cases, their time spent researching doesn’t equate to more frequent trades or higher returns. This may also lead to the frustration of users.
Choosing the Right AI Financial Assistant Vendor
Are you evaluating vendors to enhance investor support?
Here at Trading Central, our specialized team of TC Labs data scientists and financial analysts have been building proprietary AI solutions specifically for the investment space since 2019. With their help, we’ve designed a comprehensive checklist to help you assess the risk and quality of various AI chatbots.
Here’s a list of questions to ask your AI providers to ensure they meet your customers needs, so you can properly compare them, weigh their risk and make an educated purchase.
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The 7 top pitfalls of AI chatbots in the financial sector – and how to avoid them.
