The primary question today is whether new AI models are fundamentally distinct from existing technology or if they will be used in such a different way that the current regulatory frameworks are not sufficient or do not apply.
...
My remarks today will focus on how financial policymakers are learning about the use of new AI tools by financial firms, and what kinds of risks these tools could introduce to the financial system. Adoption of new technologies in finance is not new. Financial firms are innovating continuously in order to increase efficiencies and offer new services. Policymakers have experience with changing technologies and have developed regulatory frameworks focused on building guardrails, regardless of the underlying technology used. In other words, we are not starting from scratch in thinking about how to address the risks of AI while also allowing for the opportunities from it to be realized. The primary question today is whether new AI models are fundamentally distinct from existing technology or if they will be used in such a different way that the current regulatory frameworks are not sufficient or do not apply.
With that framing in mind, I will start my remarks today with a characterization of the technology and how financial institutions use AI today. These current uses can help us think about how financial firms perceive their opportunities and how they may want to use AI in the future. I will then consider the potential risks and our financial regulatory framework for assessing and addressing these risks. I will end with some questions for this group to consider.
II. DEFINING AI
Artificial intelligence is a broad concept and resists a precise definition. Here, I will use AI to describe any system that generates outputs – which can be forecasts, content, predictions, or recommendations – for a given set of objectives. From this conceptual framing of what AI systems do, we can think about the underlying technology as falling into three categories: “early” artificial intelligence, machine learning, and newer generative AI models. These categories roughly track the order in which they were developed, but many AI models combine elements across these three categories.
First, early AI describes rule-based models. Many computer programming languages are basically rules-based AI and have been used since the 1970s. Generally, these systems solve problems using specific rules applied to a defined set of variables. We have all experienced customer service that uses a rule-based AI. We ask a question that leads to pre-defined follow-up questions, until we get a pre-packaged answer or press zero enough times that we can talk to a human. Internal loss forecasting models or early algorithmic trading, for example, also might be considered forms of early artificial intelligence. We are very familiar with these kinds of tools in finance.
Second, in contrast to rules-based systems, machine learning identifies relationships between variables without explicit instruction or programming. In machine learning, data are the key input and the system identifies patterns from the data. Learning can be reinforced, for example, by providing feedback to the system about whether the output is good or bad. From this feedback, the machine learning model can learn to perform better in the future. Machine learning is also embedded into many existing processes for financial institutions. For example, it has long been used to develop fraud detection tools. Machine learning also enables the mobile banking app on your phone to read handwritten checks.
The latest AI models can be characterized by their ability to generate new content – from text to images to videos. Instead of being limited to a defined set of potential responses in a defined format, GenAI can produce a range of responses in a range of formats. For example, “Give me recommendations for where to eat in Paris but composed as a poem in iambic pentameter.” These models are flexible and often dynamic, learning from experience in generating responses and through ingesting new information. More advanced AI systems, which are still being developed, aim to be highly autonomous with capabilities that match or exceed human abilities....
more at FSB
© FSB - Financial Stability Board
Key
Hover over the blue highlighted
text to view the acronym meaning
Hover
over these icons for more information
Comments:
No Comments for this Article