NLP use cases in the insurance industry
Nov 23, 2022
Introduction
Because of claims, insurance policies, and customer relationships, the insurance industry generates a large amount of unstructured text, making it difficult for insurers to leverage their datasets using traditional methods.
Merging the insurance companies’ work process with artificial intelligence (AI) technology such as natural language processing can provide them with effective tools as they deal with rapidly changing consumer demands, habits, and purchasing behavior.
In this article, we will share the most common NLP use cases in the insurance industry, including underwriting, fraud detection, and claims processing.
1- Claim processing
Claim processing is a critical process in the insurance industry and the source of many troubles.
Typically, this operation is time-consuming, expensive, and prone to human error. However, because NLP technology can analyze both speech and text faster than humans, claim processing can be totally redefined by NLP-driven chatbots and optical character recognition (OCR) models.
- Insurance companies can use OCR to structure and rapidly access large amounts of customer information connected with previous claims, credit standing, bankruptcies, and criminal records and therefore minimize errors and avoid risk.
- They can adjust premiums based on risk analysis and devote additional resources to preventing errors and fraud.
- Customers can be guided by NLP-powered chatbots to take videos and photos of the damage, which can then be instantly converted into the first notice of loss.
2 – Underwriters
