3 No Code NLP USE Cases for Insurance
Currently, insurance companies around the world are discovering potential use cases of AI/ML to optimize operations, save costs or increase revenues. After all, the insurance industry is a gigantic one, with premiums recently reaching more than €1 trillion in the US alone.
Among all the possibilities, which we will present in more detail later in our blog journey, NLP technology is the most widely used technology, along with computer vision. Many companies are looking to take advantage of powerful data processing capabilities to optimize the customer experience as well.
What are NLP Use Cases?
As part of AI and ML, insurance companies are using natural language processing (NLP) to extract meaning and actionable insights from text data. NLP is a rapidly growing sub-segment of AI that is gaining traction in the financial services sector, enabling a smooth and natural interface between humans and machines.
There are countless ways to apply NLP to a company's business processes. For example, NLP can decode and classify voice-based documents, interact with customers via chatbots or voice assistants, interpret and search news articles for relevant information, or even translate text data from one language to another.
In particular, the use cases of NLP in the insurance industry continue to evolve, offering these companies a way to make their solutions more efficient and accurate. Enhancing a company's risk management capabilities, detecting fraud, and improving customer service are key ways insurance companies are using NLP. However, there are many other applications for NLP in these companies.
AI's Impact on the Insurance Industry
For an insurance company, the amount of information that needs to be processed on a daily basis to assess the risk profiles of new customers, set premiums, and process claims is becoming too much for human employees alone. Since AI is capable of analyzing large amounts of data accurately and efficiently on a daily basis, this tool is revolutionizing the industry and helping these companies save valuable time and money at every stage.
In addition, more data about consumers is available than ever before, but it comes from a variety of sources and is not always standardized or available in centralized locations. While it would be tedious and time-consuming for employees to compile all relevant documents and data points about a customer from every source, AI can streamline this process. By filtering and sorting various databases and sources to extract information, AI can work around the clock to keep insights up to date.
3 Use Cases for NLP in Insurance
As the applications of NLP in the insurance industry continue to expand and transform the field, companies can find many practical applications of this technology in their operations. By providing valuable insights into customer behavior, facilitating better predictive capabilities, and helping companies increase their efficiency, NLP is changing the landscape of the insurance industry and the way these companies operate and deploy resources.
1. Obtaining Individual and Corporate KYC Information
In 2012, FINRA introduced Rule 2090, which requires financial institutions to make reasonable efforts to know and retain key customer information. This regulation, better known as Know Your Customer (KYC) data, was put in place to protect these firms from fraud and corruption and to prevent them from unknowingly facilitating illegal activity through their organization.
Each new regulation in the industry adds to the already heavy workload of these companies. Therefore, the use of NLP in the insurance industry can significantly help these companies perform the due diligence that this regulation requires. NLP models can automatically sort, archive, and extract the meaning of thousands of documents and records for each individual customer or company and compare them to historical records to determine if their activities and funds are legitimate.
NLP helps insurance companies extract meaningful data from their own and third-party sources to quickly learn the basic information about a customer, which would be very time-consuming for employees to do manually. In addition, NLP helps with compliance by ensuring that all relevant files and documents are sorted, filed, and easily accessible.
2. Customer Credit Risk Alerts
With NLP, insurance companies can receive real-time alerts on the credit risk of their customers based on the most up-to-date data. This has significant implications for insurance companies, as risk management is the foundation of their business model. In addition, the mountains of data that would need to be processed by staff cannot be processed in real time.
The more efficiently insurance companies can access their customers' risk profiles, the less risk they face as a company and the faster they can make informed decisions about their insurance coverage. NLP models can not only assess risk using the company's own data, but also leverage third-party sources to better understand the customer's full risk profile.
Overall, the use of NLP in insurance helps companies become more agile and responsive to data in real time. Consider an auto insurance company whose customer was involved in an accident. The company could adjust its monthly premium to compensate for the increased risk. So improved risk management is one of the most important use cases of NLP in insurance.
3. Claim Identification
High costs are incurred by insurance companies due to fraud, especially false or fraudulent claims. Recently, it was estimated that these activities cost insurance companies in the United States $80 billion each year, so it makes sense for these companies to find better and more accurate ways to detect suspicious claims.
Applying NLP to the insurance industry can help companies evaluate historical data and compare it to current claims, identify patterns in claims, and flag outliers for investigation. Claims processing has traditionally been a tedious and time-consuming task, and any employee may not be able to identify unique patterns or questionable claims when manually processing them every day.
With powerful predictive analytics capabilities, NLP models can identify potentially fraudulent claims early and determine which customers are more likely to submit false claims in the future. By integrating NLP into a company's workflows, insurance companies can save billions of dollars each year by saving claims representatives time that can be spent on other activities, preventing losses, and reducing human error that is prone to this type of task.
The Impact of NLP on Insurance
As the number of NLP use cases in the insurance industry continues to grow, companies that have traditionally stayed away from the new technology are beginning to recognize the opportunities these models offer for their operations and improving their efficiency. Through better risk management, improved fraud detection and faster data processing, the use of NLP in the insurance industry is helping companies meet their obligations and better respond to new information that could save them billions.
How No-Code NLP Helps Insurance Firms
As the insurance industry begins to leverage recent technological advances in ways it hasn't before, the use of AI and ML will continue to grow in these companies. The NLP sub-segment helps companies save valuable time and money by helping them extract meaningful insights from text data and work around the clock to provide real-time updates.
How zerocodeai No-Code NLP Helps Insurance Firms
While the insurance industry begins to embrace recent technological advancements in a way it hasn't done before, the use of AI and ML in these firms will continue to rise. Saving firms valuable time and money, the sub-segment of NLP is helping firms extract meaningful insights from text data and operate at all hours of the day to provide real-time updates.
With the use of a no-code NLP solution, like the one offered by zerocodeai.com any insurance firm can easily access these ready-made models that can quickly integrate into their current workflows and technology stacks. Financial professionals who understand these complex technologies can quickly implement them into their daily processes without coding. Try out with no registration to see how your firm can reap the benefits of these powerful AI applications.