6 Major Use Cases for Advanced Data Analytics in Insurance Industry

The insurance industry is known to be a high-risk industry. Having to constantly navigate through claims procedures, pricing and promotion while minimizing compliance risks at all times can be quite a challenge.   

Insurance companies rely heavily on data and statistics for quicker decision-making. This data will give them all the crucial insights they need to make more effective decisions.  

The introduction of advanced analytics has caused a paradigm shift in the industry. Insurance agencies have started using big data to get actionable insights, which have then been used for several business use cases. Insurers have started using advanced analytics, not only to mitigate business risks but also to identify new growth opportunities. This blog talks about some of the major use cases of advanced analytics in insurance. 

Timely Detection Of Fraudulent Claims  

Insurance companies incur heavy annual losses due to fraudulent claims, which can cost them up to $40 billion each year. Insurers have estimated that 10 – 20 percent of the claims are proven to be fraudulent and less than 20% of these claims are actually detected.  

Enhanced data science technology has made it easier to detect fraudulent activities, suspicious claims and behavioral patterns using predictive analytics. When the system detects that these claims have been made by a person with a history of fraud, it immediately flags the claim and recommends that the case be investigated. Predictive modelling can help analyze patterns in fraud and screen false claims.  

Real-Time Risk Detection and Mitigation 

Advanced analytics conduct real-time risk analysis that enables organizations to think on their feet. When it comes to motor insurance, for instance, making an accurate assessment of the risk posed by a particular car driver makes it easier for companies to formulate the appropriate premium.  

Cars connected to the internet automatically transmit a lot of data. This allows insurance firms to obtain the minutest details, including the speed of the car and braking patterns. Insurers can rely on advanced analytical modelling to assess the chances of a driver being involved in an accident. They can also compare the driver’s behavioral data by referring to an expansive database on the behavior of other drivers. This can help them significantly reduce the number of accidents and reduce the insurance payout. 

Personalized Marketing Strategies  

Customers want to avail services best suited to their needs and lifestyle and look forward to personalized offers, loyalty programs, and other special recommendations. In this era of extensive digital communication, insurance companies find it tough to constantly engage their customers and communicate with them.  

Advanced analytics help in extracting insights from an extensive database. This database has detailed information on customers including their demographic data, preferences, lifestyle choices and belief systems. This makes it easier for insurance companies to give them personalized experiences. Personalizing offers, policies, prices and recommendations can help them find the most appropriate customers and also help increase insurance company rates.  

Influencing Customer Behavior  

Advanced analytics has been used by insurance companies to analyze data and influence customer behavior. For instance, health insurance companies can capture data generated from IoT devices using technology wearables such as fitness trackers, and track variables to assess a person's potential health risks.  

This lets them get a comprehensive assessment of their customers’ health and can urge them to take better care of their health, mitigating potential risks. Insurance firms can also offer additional services and discounts to motivate customers to use fitness monitoring devices.  

Lifetime Value Prediction  

Behavior-based predictive models are being used to process customer data, making it easier to forecast customer buying and retention.  

The Customer Lifetime Value (CLV) can be predicted using customer behavior data. This prediction makes it easier to determine how profitable the customer will be. Insights gained through CLV can also predict the customer’s behavior with regards to policy maintenance or surrender.  

Prediction of Claims 

Predicting the future turn of events is going to be very important for the insurance industry. When they are able to make accurate claim predictions, they can also minimize risks and eventually reduce their financial losses.  

Advanced analytics can enhance some of the most complex processes involved in building financial models by using a large number of variables. When insurance firms are able to forecast prospective claims, they can come up with competitive premiums and improve their pricing models.  

The Exdion Advantage 

Advanced analytics has become indispensable to stay ahead in the insurance industry. Early adopters have already saved claim costs to the tune of millions of dollars.  

Exdion, with over 15 years of expertise in the insurance space has helped many Fortune 500, medium-sized and small insurance companies successfully implement advanced analytics solutions. Looking to implement advanced analytics for your insurance company? Reach out to us. 

Scroll to Top