Uses of Data Analytics in the Insurance Industry

Uses of Data Analytics in the Insurance Industry

Insurance underwriting, or the analysis of risk, was at one point designed to operate on scarcity of data. Now insurance companies are focusing on the abundance of information at their disposal. They, like many other industries, are finding new ways to collect data and inform their decision making in several key areas. Below are a several examples of how data analytics have advanced in the insurance industry and how advanced analytical systems that employ artificial intelligence and machine learning can help them get the most from their collected data.

Personalized Risk Pricing

Today, insurance companies are able to gather more and more data on their insured in order to truly understand the risks involved in selling policies. Data scientists in the insurance industry are combining a wide variety of information to better understand risk and price policies accordingly. They are gathering information from customer behavioral models that create customer profiles and real time data from satellites, weather forecasts, vehicle sensors, and in home monitors to create personalized risk assessments for potential and existing customers. To achieve measurable results from this information in a timely fashion, insurance companies must turn to advanced analytics with artificial intelligence and machine learning. These platforms aggregate, organize, analyze, and visualize results autonomously to reduce the time consuming effort of legacy analytics and to return actionable insights at a pace that keeps up with demand. This process helps insurance companies identify those risks they are willing to take, and those they are not in easy to understand metrics.

Auto Insurance

Insurance providers are pushing to include a system of telematics that will monitor driving habits and allow them to price policies based on customer behavior. Introduced in 1998 by Progressive insurance, advances in automotive technology have augmented telematics through standard options on vehicles such as examples like GM’s OnStar. Insurers want drivers to install monitoring devices that track their speed, cornering, mileage, fuel consumption, braking, and acceleration. These metrics would allow insurers to price policies based directly on behavior. Without advanced analytics that can automatically assess the incoming data in real time, the value of this data decreases. By implementing AI powered data analytics, insurance companies can better understand the inclinations of their customers, avoid risks, and provide personalized coverage that suits the customer requirements.

Property Insurance

Telematics are also appearing in property insurance, with insurers wanting the ability to monitor properties 24/7 to assess them for risk and potential issues. They can include data sources such as moisture sensors, appliance monitors, utility use measurements, security cameras, and occupancy sensors. Combined with outside sources of information such as neighborhood statistics, crime tracking, and traffic reports can give insurers a comprehensive assessment of potential risk for property insurance. In addition to risk assessment, these monitors can be used to protect the customer by potentially predicting events and allowing customers to actively avoid potential occurrences. Merging this data through machine learning capable analytics platforms can create real time analysis of the available information and allow both insurers and the insured to proactively understand the risks involved and react accordingly.

Life and Health Insurance

Companies that provide life and health insurance are also looking to implement real time data tracking and create well being scores for their customers. These profiles will keep track of health data through transactional information about what food customers buy, body sensors that track heart rate and blood pressure, external information from workout machines, and social media monitoring that will track relevant posts about health or exercise. Health insurance providers want to combine this information with hospital data to create a complete view of their customer’s health and price their policies accordingly. With so much data collected, advanced analytics must unify these varied inputs and provide complete analysis on a large data set. Machine learning will allow analysts to easily and automatically produce detailed results that will inform decision making and could even help customers prevent potential health issues before they occur.

360 Degree Customer Profiles

Insurance, like every industry, wants to improve customer experience and increase customer retention. How insurers are approaching this is by attempting to learn everything they can about their customers and creating in depth customer profiles. By understanding data relating to customer habits, needs, preferences, and interactions companies can create a better experience for their customers, keep them happier, and keep them paying premiums. Insurers attempting to create customer profiles monitor emails, call center notes, adjuster reports, social media, customer reviews, website traffic, and other data. These customer profiles need to merge a wide variety of information into a singular view of each insured. In order to create customer profiles in a timely manner, powerful analytics tools must be employed to merge this information into a cohesive assessment of individual customers.

Contact Center Optimization

The vast majority of customer contact comes through various insurance industry contact centers. Massive amounts of information is collected here through chat logs, call notes, and recordings. The challenge is to take this information and inform customer service improvements while recognizing opportunities for training of contact center employees. AI powered analytics that employ natural language processing are able to take call notes or chat data and create detailed sentiment analysis that allows companies to recognize the context behind the textual information and act on the results. Insurers are also able to track structured metrics such as call volume and call time to organize staffing to handle customer demand. By combining contact center information with customer profiles, insurance companies have the opportunity to upsell new products to customers and better meet their needs.

Fraud Detection

Fraud is a major concern for insurance companies worldwide. It costs billions of dollars anually in losses and investigation costs. Insurers are organizing collected data from across all sectors to create a multi-channel approach to fraud detection and investigation. Both traditional structured data such as claims and policy information, and unstructured data such as adjustor notes, police reports, and social media combine to produce a very close observation of customer behavior and potential fraud activity. Insurers can implement text analytics, predictive analytics, and behavioral analytics through advanced AI powered platforms to recognize trends and patterns across a host of available information. These analysis activities can help create new models to identify patterns of normal and suspect behavior that can be used to more easily recognize potential fraud.

Tim Roberson