Importance of NLP in Insurance and Criticality of Data Labeling
Jul 7, 2022
Compared to other industries, the implementation of digital processes has been a little slower in the insurance industry. Complex legacy systems and heavy regulations are some of the key reasons behind the slower adaptation of digitization. However, many insurance companies have understood the significance of implementing the right technologies and solutions, and they are now capable of providing better customer experiences.
Thanks to the increasing adaptation of natural language processing (NLP) in the insurance industry, many things including data analysis, claims, and underwriting have become more streamlined than ever before.
What Does NLP Do?
NLP, the subset of artificial intelligence (AI) automates the process of understanding and finding meaning from text in various ways. The best thing about this technology is that it can be trained to work for different purposes and in different areas. And, this technology has turned out to be one of the most crucial technologies in the medical as well as the insurance sector.
How NLP is Transforming the Insurance Industry?
Understanding the importance and effectiveness of the technology, the insurance companies have started investing in AI technology. Let’s have a look at where this technology proves to be the most beneficial for insurers.
1. Providing Customers with a Personalized Experience
In order to retain customers, the insurance companies need to offer excellent customer service. Insurers have a large number of customers who expect to get the right offers, messages, and recommendations while buying any kind of insurance- life insurance, medical insurance, or auto insurance. On the other hand, customers want to get all their questions answered with total accuracy, and without wasting their crucial time. A customer service executive may not be able to answer the questions as quickly as machines can. It has been observed that customer service can take a great deal of time doing research work to find the right answers. Here virtual assistants, powered by NLP technologies, come into focus. With NLP integrated systems or NLP chatbots, insurers can provide an automated personalized experience for customers while providing answers accurately and efficiently. The chatbots can analyze and process the text and speech faster than humans. In this way, insurers can speed up the customer-facing processes significantly with little to no extra manpower.
2. Claim Processing is the Core Activity
Obviously, a major pain area of the insurance industry is claim processing and management. It takes a great deal of time, it’s very costly in some cases, and human errors are more likely to happen when done manually. To process and approve an insurance claim form, a customer has to go through many steps. It may take days or weeks to complete these steps. And the manual process cannot provide such transparency to the process that customers want. Insurance companies are transforming the insurance claim filing process with NLP technology resulting in increased efficiency and productivity. By integrating NLP technology, the claim form processing has become simplified with a quick one-step procedure. Insurers can integrate NLP-powered systems during phone calls. It can recognize the customer’s speech and fill out the form automatically with ease. NLP technology can effectively and quickly analyze the text and speech, improving the pace of the process which will only need to be verified by the customer service agent.
3. Figuring Out Any Fraud
Insurance frauds can strike without any prior notice and they can prove to be very costly for any insurance business. Even the experts in the insurance industry have agreed that NLP is probably the best way to detect insurance fraud. Reading emails and text messages and checking the claim notes manually can be a very time consuming process. This unstructured data can be properly understood and analyzed with the help of NLP technology. NLP models can automate the whole process through Text Mining and Sentiment Analysis. It can analyze the past fraudulent claims, detect the recurring patterns and flag them. If a new claim comes with same pattern, it raises an alert.
4. Automating the Underwriting Process
Underwriters are decision makers and they have a long list of documents and policies to analyze before reaching any final decision. The actual result to be obtained from the decisions heavily depends on how well and accurately they have taken a look at the given information and data. With the help of NLP solutions, underwriters can analyze risk levels and reduce possible errors. NLP tools can analyze, understand and extract relevant information like name, date, location, etc. from documents and help underwriters make decisions faster with more accuracy.
Criticality of Data Labeling
Insurance companies generate massive amounts of data including property data, customer demographic data, property data, claims data and pricing data with regards to premiums. So it becomes difficult to understand good data from bad data. Also the nature of data is unstructured, so there is need to do some kind of transformation, which can be done with NLP models. But, NLP models also need to be trained before they can be used. Data labeling, also known as data annotation, is the key step that ensures the proper scaling of AI and machine learning projects with the right information. You can call it a setup for learning and training models to know what it needs to understand and how it can differentiate between various inputs, and finally come up with the desired output. When done manually, processing and analyzing insurance applications can be tedious and time-consuming. This is where high-quality annotated data can prove to be helpful and play a pivotal role in success across various areas.
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