A survey conducted in 2019 showcased that 84% of insurers were rooting for AI-based systems to work on. NLP can help insurers with cases and management, but what's more it can help improve customer experience, too. A 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, often in the form of lower costs or higher efficiency. NLP solutions have the power to help insurers level up by providing customers with the experience they’re now expecting.
Natural language processing (NLP) is an offshoot of artificial intelligence which exhibits the ability to automatically read, understand, and derive meaning from text. NLP bridges the communication gap between humans and computers, and it can be incredibly helpful in the insurance world. Since NLP models require specific datasets to train, they can become exceptionally knowledgeable in particular areas, which is an essential requirement for medical or insurance use cases. NLP has the power to give insurers the tools to speed up decision-making, reduce costs, detect fraud, and avoid human errors.
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 NLP 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.
Claim processing is an essential operation within the insurance sector, and 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. Insurers can integrate NLP during phone calls, for example, to recognize a client’s speech and automatically fill out a claims form.
In the past, the insurance industry has implemented 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. Insurance companies deal with a colossal amount of data and manually look at 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 never-ending amount of unstructured data (messages, social media posts, claims, etc.). These solutions can then flag cases of suspected fraud for human review.
Underwriters are responsible for analyzing many policies and documents to draw out key conclusions. This is a highly high-pressure task as the accuracy level shown while analyzing the data directly affects the company’s decision making. Unfortunately, since insurers deal with such high amounts of data, it is impossible for accuracy to be consistently high and, therefore, it is error prone. NLP solutions help 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 that otherwise would have been spent parsing through documentation.
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 since Covid-19. The insurance sector regularly faces financial and logistical challenges, and 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, and as NLP is evolving, so are the ways that it can be used for insurers. If you’d like to talk more about NLP use cases or how NLP labeling could help you, feel free to reach out to firstname.lastname@example.org. We'd love to chat!
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