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NLP Use Cases in the Insurance Industry

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Ananya Avasthi
December 3, 2021
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Natural Language Processing (NLP) is an off-shoot of artificial intelligence, which exhibits the ability to automatically read, understand and derive meaning from text in various contexts. Since NLP models require specific datasets to train, they can become exceptionally knowledgeable in particular areas, an essential requirement for medical or insurance use cases. Technology Experts equip insurers with tools to speed up decision-making, reduce costs and avoid human errors using NLP techniques.

By 2025, the NLP market is expected to be worth $35 billion. A survey conducted in 2019 showcased, 84% of insurers were rooting for AI-based systems to work on. Furthermore, another survey conducted by Accenture’s Living Business concluded that 64% of the time, when a customer changes their insurer, it’s due to better quality services and products. Therefore, embracing AI solutions is also relevant to providing customers with the experience they’re now expecting.

Use Cases in Insurance Industry

Improving Customer Experience

According to a recent study, more than 80% of insurance customers desire personalized offers, messages, pricing, and recommendations from their insurance providers. NLP has several uses, one of them comprehending natural language. Virtual assistants integrated with NLP can create a personalized experience for insurance customers. With NP integrated systems, one can achieve an automated personalized experience for each user and assist in providing answers efficiently and accurately. AI-virtual assistants tend to be more efficient in these tasks than an average agent.

Claims management

Claim processing is an essential operation within the insurance sector at the center of many pain points. Usually, this operation is time-consuming, costly, and subject to human errors. Overall, NLP technology dissects both speech and text faster than humans can. They integrate NLP during phone calls, for example, to recognize a client’s speech and automatically fill out a claims form. Employees are then required to verify the results manually.

Fraud detection

In the past, one has witnessed the insurance industry implementing both traditional and experimental technologies to combat insurance fraud. According to the experts, NLP technologies are one of the most efficient options for fraud detection. For sure, these companies deal with a colossal amount of data in the insurance industry. Generally, one must manually look at those claims notes and data like emails or text messages which is incredibly time-consuming and tedious. NLP solutions are integrated into the system to analyze and understand the neverending amount of unstructured data (messages, social media posts, claims, etc.). These solutions can then flag cases of suspected fraud for human review.

Underwriting automation

Underwriters are responsible for analyzing many policies and documents to draw out key conclusions: This is a highly high-pressure task as depending on the accuracy level they have showcased while analyzing the data bares direct outcome on the company’s decision making. Unfortunately, since one is dealing with such high amounts of data, it is impossible for accuracy to be high and, therefore, is an error-prone title. NLP solutions equip underwriters to extract relevant information to access the level of risk. NLP quickly extracts information like dates, locations, names, diagnoses, lab results, etc., which saves a tremendous amount of time for underwriters that otherwise would have been spent parsing through documentation themselves.

Conclusion

According to a Celent report from April 2020, cost reduction and improving efficiency have become a vital part of the expectations for 67% of consumers due to Covid-19. The insurance sector regularly faces financial and logistical challenges, even more so after the pandemic. NLP technologies have been proven to provide solutions that will help. A ] study by LexisNexis in 2019 showcased that 88% of insurers surveyed were already seeing benefits from the onboarding of AI-based solutions for claims settlements. The results were promising all around.

Insurance is a constantly evolving industry under extreme pressure due to the pandemic. Integrating insurance systems with AI tech such as cloud computing, identity resolution, predictive analytics, natural language processing equip insurers with powerful tools as they grapple with dramatically altered consumer needs, behaviors, and spending patterns.  Overall, it seems logical that the insurance industry is walking towards a technology-filled future. However, the NLP systems lack quality data available to train datasets cannot fully automate the process. 




Want to learn about NLP in other industries?

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