For centuries, insurance relied on an actuarial approach for calculating various parameters-but all of that has undergone rampant changes with time. Over the years, companies have been successful in collecting, storing, and analyzing massive data sets to price different types of risks, and issue insurance policies accordingly. Further, the unprecedented pace at which data is being generated in the digital era today–in terms of volume, variety, and velocity–requires you to change the way your organization looks at data analytics.

As new sources such as social media, mobile apps, sensors, and telematics impart a whole array of customer information, you need to leverage next-generation data analytics for effective risk management. It’s time for transactional data to be combined with unstructured data relating to customer life milestones, location, health, profession, and other parameters, for unearthing personalized insights.

5 Insurance Industry Segments Being Transformed by Data Analytics

Let’s look at five ways in which you can use advanced data analytics to fundamentally reimagine core aspects of your business, and create tangible value:

1. 1. Fraud prevention

   Insurers have traditionally relied on statistical models to detect frauds, that often correlate events to the previous instances of deceitful activity and use sampling techniques to assess them. While the process itself sounds highly technical, it can get extremely complicated when performed manually.
   However, now you can enhance the fraud prevention mechanism by applying data science platforms and software, which can spot dubious linkages and dodgy activity through predictive modeling.

   The resulting “smart” intelligence will enable you to identify potential policyholders who are likely to commit insurance fraud, such as filing wrong claims. By matching the variables in each claim against the profiles of previous fraudulent claims, data analytics-driven predictive modeling lets you proactively identify candidates for further scrutiny in case there is a match. Such data-backed engines are also vigilant enough to detect and flag pattern-based user behavior that could result in fraud.

2. 2. Policy customization

   Data analytics algorithms can build personalized plans for prospective and existing customers by gleaning actionable insights from historical and current data collected from different sources including call centers, e-mails, and social media. These data points, encompassing each customer’s preferences, habits, and behavior, can help you create a unique profile for every buyer, and anticipate future behavior.

   Many players are now using data captured by mobile apps and wearables to track customers, even as they proactively work with patients to improve the latter’s health parameters. So, a diabetic patient, for example, will be levied a higher premium at the time of policy renewal if the insurer finds out–via data analytics–that they have not adhered to the treatment regimen.

3. 3. Premium pricing

   Conventionally, insurance premium calculation has been significantly influenced by the risk ratings for different customer cohorts or segments, based on their lifestyle, occupation, age, etc. This has meant individuals with a lesser risk profile have had to pay higher premiums than they should have.

   However, such calculations and risk profiling was performed manually and were mostly more intuitive rather than data-driven. But currently, that scheme of things is changing thanks to data and data analytics. By leveraging predictive modeling, you can track the behavior of individual policyholders, and generate a 360-degree customer profile to determine personalized policy premiums. These are found to be far more effective and customer-friendly as they grant the perfect alignment between pricing and personalization.

   For example, a responsible car driver will be charged a lesser premium as compared to someone with a history of having driving-related accidents. Similarly, individuals who have fewer comorbidities and follow an active lifestyle may be charged lesser life insurance premiums than those struggling on the health front.

4. 4. Policy self-servicing

   Digitally empowered customers have now gotten used to on-demand service, as and when they want it. Self-servicing portals put the customer in charge and transfer granular control in their hands to manage the terms of their service. As the idea of self-service transforms telecom, banking, media, and many other industries, insurance should be no different. You should consider provisioning a dedicated customer portal where policyholders can log in and manage their policies by themselves.

   So, an individual can buy a top-up policy, raise a claim for reimbursement, or flag a query for the help desk through this fully-functional and highly capable portal. Doing so will not only help you realize significant operating costs but also make customers happier by empowering them. You could use data analytics to automatically track the policy tenure and premium collection history, and make intelligent recommendations regarding policy renewal or modifications to the concerned individual.

5. 5. Business transformation

   You could also apply data analytics to transform core business functions including underwriting, wherein algorithms facilitate better, accurate, and automated risk modeling. In fact, some insurers have already begun using several types of non-medical data points to redefine the typical medical underwriting process to fall in line with the growing focus on wellness management. Ensuring a streamlined underwriting process with analytics will also allow you to issue policies faster.

   

   Actuaries and treasury are two other major areas that could also be transformed, by deploying data analytics for swift mathematical rate analysis, investment modeling, and cash forecasting.

   Last but not the least, you could enhance your relationship with individual customers by delivering data analytics-based personalized policy recommendations at different stages of their customer lifecycle to improve customer experience and satisfaction levels. This will not only pave the way for increased brand loyalty but also boost up-selling and cross-selling opportunities.

Conclusion

Data is undoubtedly the new oil of the 21st century. And as with every fast-moving trend, time is of the essence lest you find yourself facing the non-renewable spectrum of this intelligence.

That being said, harnessing data taps is just the first step in the journey. The more pressing question is, are you ready to extract this crude oil, refine it and leverage the same for delivering better, relevant solutions to your customers? If yes, then you can achieve stellar results through insurance data analytics.

Your competitors are already at it, and your customers won’t wait for long. So, the time to act is now!