Challenge and Opportunity
A client and large insurance company had hundreds of thousands of forms that underwriters had to manually read to approve applications. The name of the game in insurance is getting an accurate quote first. The insurance company needed a solution for more accurate and timely application processing.
- Use Python to parse reports and extract required data.
- Save all data to an Exasol database.
- Maintain detailed entries on the several types of errors encountered.
The system was able to parse 215,273 reports and found 57,529 valid issues within the reports in which the client was not aware. Aside from the automated QA, we are able to enhance/clean the data through NLP methods. This improved speed and accuracy of the previous method to underwrite these policies.
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