How NLP is Transforming Analytics in Insurance Industry
A typical insurance company provides services to a huge customer base, has multiple product types, various distribution channels, and a market spread across geographies. Every transaction in the insurance world produces valuable data that can be leveraged for business value.
With the huge amount of data flowing in from multiple channels, insurance companies are undergoing a transformation in the way they function. In recent years, data and analytics have become essential tools for insurers that help them improve and simplify existing business processes such as product planning, pricing, marketing, claim processing, and customer self-service.
Many insurance carriers developed in-house analytic capabilities to streamline the flow of data to the correct agents and departments. But now when analytics becomes the foundation for conducting successful business the data systems become more sophisticated, with adoption of the new technologies and greater involvement of third parties. According to a report from Everest Company, adoption of third-party analytics tools in the insurance industry is expected to quadruple its current size by 2020.
For such a data-intensive industry, utilizing advanced analytics can become a real remedy. Let us delve a little deeper into how Natural Language Processing, a field of artificial intelligence that we are so excited about at FriendlyData, can enhance big data analytics and help insurers close the loop between data, insights, and action, reducing their costs, mitigating risks, enhancing customer loyalty, and maintaining long-term profitability.
NLP and text analysis in the insurance industry
NLP and text analysis open up new opportunities in the insurance industry. Using chatbots and virtual assistants to create personalized customer experience and improve query response time, are the most common examples of NLP applications in insurance.
Another big challenge that public and private insurers can address using NLP technology is the complexity of enterprise data access. Instant analysis of internal company data is frequently inaccessible to non-technical employees within the enterprise. Key decision makers in health insurance, for example, are often unable to investigate specific procedures, diagnoses, and patient profiles that contribute to macro-trends. Brokers and agents have no data to make smart recommendations to customers at the moment they are buying a new policy. These result in poor decision making and lost money.
Insurers need a fundamentally new approach to data analytics. Conversational analytics platforms powered by NLP technology have the potential to change the way people interact with enterprise data. Conversational analytics allows users to get answers to their data questions in seconds simply by typing their requests in natural language in a search bar. Such Google-like interface makes data easily accessible to everyone within a company fostering better decision making.
Natural language interface applications across insurance value chain
Insurance value chain consists of the following building blocks:
- customer relationship management
- channel management
- underwriting and policy management
- claims management
- finance management
- human resources management
- corporate management
To succeed in the market, all blocks of the insurance value chain should be data-driven. Making corporate data easily accessible to all decision makers can help insurance companies achieve this goal.
Let’s have a look at how self-service analytics tools that provide a natural language interface to databases can democratize enterprise data and improve analytics in the insurance industry.
Customer relationship management
There is a trend in the insurance industry towards being more customer-centric. Despite face-to-face interaction is decreasing due to extensive use of online channels yet customers expect a more personal experience. In order to effectively interact with the customers, insurance companies have to fully utilize the potential of new technologies and their CRM data.
Search-based analytics allows to speed up data access for all decision makers in a company and meet the ever-changing needs of the demanding customers. By asking more questions and being able to quickly discover actionable insights from CRM data insurers can retain high-value customers, launch effective marketing campaigns, design better products tailored to a customer’s behaviors and preferences.Examples of questions you can ask about your customers:
- Show policyholders who generated the most revenue last year
- Show customer retention rate for dental insurance
- Customers acquired through Facebook campaign in May segmented by age
- The number of customers who churned in Q2 by state.
A typical insurance company distributes its products through various channels: agents, brokers, direct sales, and digital. To improve overall distribution strategy insurers should care about channel effectiveness, workforce allocation, agent development and training, growing online presence, etc.
Search-based analytics can be very handy for channel management because it allows asking many questions and drilling down to the level of a specific product or an individual salesperson.Examples of questions you can ask about your distribution channels:
- The number of new insurance policies sold last month by channel.
- The number of agents by region.
- Top 10 agents who sold the most universal life insurance policies last month.
- Policy renewal rate by brunch.
Fast and effective claims handling is crucial for customer satisfaction. While sophisticated machine learning models can be used for automated fraud detection, search-based analytics can help insurers spot important trends in claims data, and improve claims estimations for new products.Examples of questions you can ask about claim processing:
- The number of new claims per month in 2018
- Claims by customer segment
- Open claims by cause of loss
- Average number of days to close a claim by brunch.
Underwriting and policy management
An underwriter evaluates the risk of insuring a home, car, driver or individual and decides if the risk is acceptable for the insurance company or not, after the risk assessment, the underwriter sets the right amount of premium to be charged. Search-based analytics can help underwriters leverage claims, policies, and loss data stored in corporate databases to improve decision making.Examples of questions you can ask about your claims, policies, and loss data:
- Cost per claim vs premium earned last month for Motorcycle Insurance
- Premium earned by insurance type last quarter
- Incurred claims ratio for Motorcycle Insurance
- Loss ratio for Collision Coverage month by month
Benefits of search-based analytics for insurance companies
Search-based analytics tools can help insurance providers in many different ways:
- Equip brokers, insurers, claim processors, underwriters, and C-suite with the information they need to inform all strategic and operational decisions.
- Blend different data sources so that internal teams can have a complete view of corporate data from a single location.
- Increase adoption rate of Business Intelligence tools without additional training.
- Eliminate the need for expensive data scientists & database professionals and thus reduce labor costs significantly.
All that’s needed to democratize data in an insurance company is implementing a reliable search-based analytics platform that can aggregate, analyze and visualize all enterprise data, and most importantly that can understand natural human language and insurance industry jargon.
Democratize your data
FriendlyData helps to respond to one of the key challenges in the insurance industry - building the power of data and analytics into day-to-day decision-making.
The solution we offer is data democratization. FriendlyData makes data accessible to everyone in a company by providing a user-friendly natural language search interface to databases.
Foster data-driven culture in your organization with us!