This repository is a companion to the ActuaryGPT paper and consists of multiple case studies that utilize the OpenAI API and other libraries to perform various tasks.
In this case study, GPT 4 is used to parse engagements with policyholders during the claims process to determine the sentiment of the engagement, emotional state of the claimant, and identify any inconsistencies with the claim information.
It is important to note that the LLM serves as an automation tool in this respect, and is not intended to replace human claims handlers, or to be the final decider on fraud or further engagements. Rather, it is intended to provide a tool to assist claims handlers in their work by parsing the information provided by the claimant and providing a summary of the engagement and a set of indicators to guide further work.
In this case study GPT 4 summarises a collection of news snippets to identify emerging cyber risks. The script conducts an automated custom Google Search for recent articles using a list of search terms. It extracts the metadata of the search results and uses GPT-4 to generate a detailed summary of the notable emerging cyber risks, themes, and trends identified.
Following this, GPT-4 is asked to produce a list of action points from the summary. Each action point is then fed into GPT-4 again to produce project plans to fulfil the action points.
As described in the paper, LLMs are not able to digest large volumes of regulatory documents so in this case study a knowledgebase of regulatory documents is constructed using vector embeddings of their contents.
Leveraging the ”OP” stack, so named because it uses the OpenAI API and the Pinecone database, the knowledgebase is constructed as follows.
- Regulatory documents are fed to the OpenAI embedding endpoint to generate vector embeddings of the documents.
- These embeddings are then stored in the Pinecone database. The Pinecone database is a vector database that allows for fast vector searches.
- On top of this, a website is built that allows the user to input a text query which is then used to search the vector database. The vector embedding of the text query is then compared to the vector embeddings of the regulatory documents and the most similar documents are returned to the user.
- These documents are then used as context for the LLM to generate a response to the user’s query. This allows the LLM to answer questions within the context of the regulatory documents provided, allowing for more accurate and relevant responses.
Reinsurance contracts are critical documents that are often lengthy and complex, making them time-consuming to analyze and understand, not to mention, exposing the insurer to significant risk if all contracts are not well understood.
In this case study, an LLM is used to automate the process of extracting structured data from reinsurance contracts, such as the type of reinsurance, the reinsurer, the reinsured, the coverage period, and the premium amount. This structured data can then be used to populate a database or spreadsheet for further analysis.
- Python 3.11 (not tested on earlier versions)
- OpenAI API Key
- Google Custom Search API Key
- Clone the repository:
git clone https://github.com/cbalona/actuarygpt-code.git
cd actuarygpt-codeSetup the virtual environment:
make setupInstall the dependencies:
makeYou will need to set the following environment variables:
| Variable | Description | Applicable Case Study |
|---|---|---|
| OPENAI_API_KEY | Your OpenAI API key | Needed for all case studies. |
| GOOGLE_CUSTOMSEARCH_API_KEY | Your Google Custom Search API key | Needed for Case Study 2. |
| GOOGLE_CUSTOMSEARCH_CX_KEY | Your Google Custom Search CX key | Needed for Case Study 2. |
Navigate to the appropriate case study directory (case-study-1, case-study-2, or case-study-4).
Run the main.py script:
python main.pyThis project is licensed under the terms of the CC-BY 4.0 license. See LICENSE for details.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent