(As published in ABA Risk & Compliance magazine)
We decided to ask ChatGPT to help us write an introduction to this article.
Q: We’re writing an article for a banking risk and compliance magazine on large language models. Tell us a fun fact about you that would encourage compliance officers to want to use you.
A: Fun fact: As a large language model, I can significantly streamline and enhance the compliance process for banking professionals. Compliance officers can leverage my capabilities…
Not what we hoped for, so, we tried again.
Q: Can you be a little more succinct and fun?
A: Certainly! Fun fact: I’m your compliance co-pilot, navigating the complex terrain of banking regulations with lightning speed and precision. I’m the Swiss Army knife for compliance officers, simplifying searches, drafting documents, and keeping you on the straight and narrow in the exciting world of banking compliance!
Better right? And we could not leave well enough alone – so –
Q: That’s good. How about a catchy title for the article?
A: “Compliance revolution: How large language models are transforming banking regulation”
Q: Too long and not fun. Do you have something shorter and pithier?
A: “Compliance meets AI: A banking love story”
Nailed it! We promise, from here on out, the writing is all human!
Generative AI: What you need to know
Rarely does a day go by without our news feeds reporting on the rapidly evolving world of Generative AI (GAI)—artificial intelligence that can generate text, images, or other media in response to prompts. GAI models, and more specifically large language models (LLMs), a type of GAI, are being deployed across industries and financial institutions are thinking of ways they can do so, too—a challenge made all the greater by being highly regulated entities. Larger financial institutions are developing their own solutions, and of course vendors are rapidly figuring out ways to use this technology in their solutions.
So, what exactly are LLMs and why should you become familiar with them? LLM’s, like ChatGPT and Bard, are models that are trained on vast amounts of textual data such as books, articles, white papers—anything you can find on the internet. The models recognize patterns and relationships in human language. They take that learning to generate text in response to your question (called a prompt in the AI world). Based on the vast data upon which they are trained; they answer your prompt based on predictive analytics—determining the most logical word or phrases in response to your question.
Risks associated with GAI technology
If you like to keep an eye on technology news, you have likely seen reports of some spectacular “fails” when it comes to the use of LLMs. Take for example, the attorney who submitted a brief to a court, citing cases, complete with quotes, citations, and judge’s names. The only problem was that ChatGPT made them up. This type of inaccuracy is known in the AI world as “hallucination.”
Hallucination is a tendency of a LLM to “make up facts,” drawn from the many terabytes of data they have been trained with, to produce very convincing answers to prompts. Hallucination can result because LLMs are trained to be predictive, based on word patterns—with the goal of being conversational vs strictly knowledgeable.
The good news is that while hallucinations cannot be eliminated, they can be minimized and mitigated. LLM developers have been quick to respond to reported epic failures in their models to provide more safeguards in the results produced. OpenAI, the company behind ChatGPT, has initiated a new methodology that it refers to as “process supervision” that is intended to evaluate each logic step in the model’s method for answering a prompt and, in doing so, to help detect and mitigate a model’s logical mistakes. Also, controlling the data sources the model is trained on by using domain-specific, trusted data sources can provide better results. Ultimately, there is no substitute for expert (human) verification of the results.