Introduction

We will use ChatGPT to show how it can help with data extraction for a "First Notice of Loss" (FNOL). FNOL intake is a crucial step in the insurance industry, recording initial claim information. Automating this process can make it faster, reduce errors, and improve customer satisfaction.

The Role and Benefits of ChatGPT in FNOL Intake

ChatGPT, powered by OpenAI, can automatically extract important information from claim documents. This offers several benefits over the manual process:

• Efficiency: Speeds up the FNOL intake process.

• Accuracy: Reduces errors from manual data entry.

• Consistency: Ensures uniform data extraction.

• Scalability: Handles large volumes of claims efficiently.

Structured Template Prompt for Extracting Information

To use ChatGPT for FNOL intake, you can use a structured template prompt. This prompt helps extract the necessary information from a claim document systematically. Here’s an example of such a template; just upload it with the claims document to start extracting the data:

You are an AI assistant. I need your help to extract specific information from a claim document which I will upload. Please provide the following details:

1. **Claimant Information**:

   - Name:

   - Address:

   - Contact Number:

   - Email:

2. **Incident Details**:

   - Date of Incident:

   - Location of Incident:

   - Description of Incident:

   - Type of Loss (e.g., property damage, injury):

3. **Policy Information**:

   - Policy Number:

   - Type of Policy:

   - Coverage Details:

4. **Witness Information** (if any):

   - Name:

   - Contact Number:

5. **Additional Notes**:

   - Any additional relevant information:

Limitations of Using ChatGPT for FNOL Intake

While ChatGPT offers numerous advantages for FNOL intake, there are MANY limitations to be aware of:

• Manual File Handling: Users must manually download and upload claim documents, which is time-consuming and can cause delays.

• Exporting Data: Extracted data needs manual export, reducing efficiency due to the upload and download process.

• Validation Challenges: Verifying extracted information is difficult as users must cross-reference text responses with the original documents manually.

• Large or Complex Documents: ChatGPT struggles with large or poorly formatted documents, leading to incomplete or incorrect data extraction.

• Security Risks: Manual handling of sensitive claim information can pose security and privacy risks.

• Adjusting Extraction: Modifying prompts for better extraction requires manual testing with old documents, making it impractical and inefficient.

Conclusion

Planflux was designed to address these shortfalls affiliated with ChatGPT by integrating advanced automation and workflow optimization, enhancing the efficiency, accuracy, and consistency of FNOL intake in the insurance industry.