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How a Fortune 500 insurance company saved 30% manual labor by automating its quoting workflow
Technology Category
- Analytics & Modeling - Natural Language Processing (NLP)
Applicable Functions
- Sales & Marketing
Use Cases
- Fraud Detection
Services
- Data Science Services
The Challenge
The Fortune 500 insurance company was struggling with the manual extraction of key information from prior insurance plans issued by the competition. The process was tedious, error-prone, and expensive due to labor costs. The company had to review tens of thousands of policies per year, each with their own jargon and format. The complexity of the plans, with multiple entries per row and multiple employee classes of coverage, made the task even more challenging. Manual work often overlooked important provisions, leading to inaccurate quote calculations and profit losses for the company. Conventional tools failed to extract accurate information, forcing the company to rely on expensive manual labor.
About The Customer
The customer is a Fortune 500 insurance company. The company is one of the largest insurance providers in the United States, offering a wide range of insurance products to its customers. The company has a large workforce and deals with a high volume of insurance plans each year. The company's goal was to increase the efficiency and quality of its quoting workflow by automating the extraction and classification of key information from prior carrier plans. The company was facing challenges with the manual extraction of information, which was a tedious, error-prone, and expensive process.
The Solution
The company implemented Cortical.io Contract Intelligence to automate the extraction and classification of key information from prior carrier plans. The solution was trained on five different products: Long-Term Disability (LTD), Short-Term Disability (STD), Vision, Dental, and Life Insurance. The solution gave full control to Subject Matter Experts (SMEs) over the whole extraction process, allowing them to fine-tune the system without the intervention of any AI expert. The solution was able to accurately extract key information from complex tables with multiple entries per row, detect employee classes and associate extractions to class description, classify clauses, even when the wording differs, detect and OCR scanned documents without manual intervention, and export the document extractions in an Excel format.
Operational Impact
Quantitative Benefit
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