Data forms the bedrock of the U.S. insurance industry, which accounts for almost 2.7% of the domestic gross domestic product, according to the Insurance Information Institute. Over the years, companies like yours have collected, stored and analyzed massive data sets to price different types of risks, and accordingly issue insurance policies. However, the unprecedented pace at which data is being generated in the digital era today–in terms of volume, variety and velocity–requires you to transform the way your organization looks at data analytics.
As new sources such as social media, mobile apps, sensors and telematics throw up 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.
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. Fraud prevention
You have traditionally relied on statistical models to detect frauds, applying them on the previous instances of deceitful activity and using sampling techniques to assess them. 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 perpetrate 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.
2. Policy customization
Data analytics algorithms can build personalized plans for prospective and existing customers by gleaning actionable insights from historical and current data sourced 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 payers 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 he has not adhered to the treatment regimen.
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 ,that scheme of things is now changing.
By leveraging predictive modelling, you can track the behavior of individual policyholders, and generate a 360-degree customer profile to determine personalized policy premiums. For example, a responsible car driver will be charged a lesser premium as compared to someone with a history of having driving-related accidents.
4. Policy self-servicing
Digitally empowered customers have now got used to on-demand service, as and when they want it. 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 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 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. 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, 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 policy recommendations at different stages of their experience lifecycle. This will not only pave the way for increased brand loyalty, but also higher up-selling and cross-selling.
Data is the new oil, everyone agrees in the 21st century. The question is, are you ready to extract this crude, refine it and leverage the same for delivering better, relevant solutions to your customers? Your competitors are already at it, and your customers won’t wait for long. So, the time to act is now!