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Pros and Cons of Generative AI: A Quantitative Analysis

March 1, 2024 | 1 minutes reading time | By Mike van de Graaf

Artificial intelligence is changing the risk modeling landscape. Financial institutions are evaluating whether they can gain an edge through the deployment of large language models, like ChatGPT, in particular – but are LLMs truly more effective than traditional modeling approaches?

Today, if you are a tech-savvy financial institution, you need to assess the risks and opportunities of artificial intelligence – and of large language models (LLMs) like ChatGPT in particular.

Can LLMs yield greater insights and improved productivity? To find out, I performed quantitative risk analyses for the Australian Commonwealth government bond series. I used publicly available data and ran ChatGPT4 – the latest version of OpenAI’s generative AI technology – on my personal Mac.

 Mike van de Graaf

The idea was to see how ChatGPT4 would identify clusters of yield curves via an unsupervised machine-learning approach. Would the language interface of ChatGPT4 help isolate outlier curves from the data set? How would it compare with the traditional approach of coding a program and generating solutions manually? And which methodology would prove more efficient, overall?

The Application: ChatGPT4 and Machine Learning

As a first step, ChatGPT4 was asked to collect the data from the Reserve Bank of Australia’s website in a file for me. Interestingly, ChatGPT4 created a file with simulated rates. It noted that the reason for this...

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Topics: Tools & Techniques

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