The insurance industry is mainly based on prediction and it’s characterized by a high-risk activity profile.
The highly competitive character of the industry and the ever-changing consumer behavior makes it mandatory for small and large insurance companies to invest in accurate and effective ways to .
Nonetheless, predictive modeling and big data, as well as data analysis, fundamentally mutate the way in which such enterprises carry their activity and create new insurance products. This contributes enormously to making this business model a more scalable one, which allows insurance companies to leverage their activity. But this happens only if the data is properly used.
Analytics and big data capabilities allow insurance companies – when used properly – to assess and minimize their risks, but also tailor more insurance products that suit each consumer’s needs and expectations. More about the ways in which insurance companies can benefit from the power of data in the following paragraphs.
Insurers nowadays can use predictive measurements and use those to assess their company’s . It is impressive how auto insurers can use the data delivered from one’s automobile or smartwatch’s socio-geographic coordinates and predict if a policyholder is likely or not to be involved in a car crash, have their vehicle stolen, or be subjected to other events.
Besides the data that is used in the process comes from in-vehicle telecommunication devices, health trackers, and exogenous data such as the road conditions or the safety levels of the neighborhood. This is one of the most impressive ways in which insurers can maximize the data that is usually stored on various mediums: to tailor and design policy premiums. But not only auto insurers use big data and data analysis to minimize their risk and correctly predict it.
The health insurance industry does the same. Because wearables and health trackers have increased in popularity tremendously and are nowadays accessible to almost anybody, health insurance companies use the data made available by those to assess health risks and vulnerabilities in their consumers. Many insurers even offer promotional packages for those clients that use wearables and offer access to their data.
Ethical and privacy concerns have arisen already, in this case, and many debates have started. But the potential in terms of risk assessment of data collection and analysis is an enormous one. Bid data is used for , but the banking and insurance industry seem to benefit the most from it. Insurers use predictive modeling and big data to identify criminal intents and fraud.
Where humans fail, big data and predictive modeling can identify mismatches between the insured party, third-parties involved in the claim (e.g. repair shops) and even the insured party’s social media accounts and online activity. Fraudulent claims can be identified quite easily.
The insurance company will gain a more secure place on the market and decrease the chances of being subjected to fraudulent claims. In the past, insurance companies were betting on loyalty programs to tailor a better customer experience.
