NLP use cases in the insurance industry

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

Underwriters are in charge of examining numerous policies and documents in order to reach key conclusions.

The accuracy level demonstrated during the data analysis heavily impacts the company’s decision-making, and since insurers deal with large amounts of data, the accuracy is error-prone and difficult to maintain consistently high.

NLP solutions can help them save a significant amount of time that would otherwise be spent analyzing data.

• Underwriters can use NLP to gather relevant data to determine the level of risk.
• NLP quickly extracts data such as dates, locations, names, diagnoses, lab results, and so on.

3 - Detecting Fraud

Insurance companies suffer financial losses as a result of fraud. In the past, they have used both traditional and experimental technologies to battle insurance fraud.

NLP technologies are one of the most effective methods to solve their issues. Insurance companies deal with massive amounts of data and manually review claims notes and data such as emails or text messages, which is extremely time-consuming and labor-intensive.

• Because they always have to deal with a large number of documents, NLP is a useful tool for investigative procedures. It can save them time and help them complete the investigation in less time by providing a classification of the documents.
• NLP models can be used to analyze previous fraudulent applications in order to identify and detect similar applications. They can be integrated into the system to comprehend unstructured data like messages, social media posts, claims, etc.

Conclusion

Insurance companies’ business can be risky and costly. The difference between profit and loss is highly dependent on the firm’s ability to manage and mitigate risk. This is why using natural language processing (NLP) tools like UBIAI to apply structure and understanding to complex language-based documents is the ideal solution for predicting risk while bringing growth opportunities to the surface.

If you’d like to learn more about how NLP can assist your business, please contact https://ubiai.tools

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