The insurance industry is on the verge of disruption, and data science is playing a massive part in this. Data science is transforming the insurance industry.
Data analysis is one of the strong pillars of insurance. Mathematical models have been used to predict property loss and damage for centuries. While selling policies, insurers collect large data-sets regarding customers that are updated when those customers make a claim.
Nowadays, insurers are trying to become more relevant efficient; they have realized the true importance of their data investments. They want to use the power of data analytics to improve customer experience significantly while cutting claims handling time and costs and eliminating fraud. Making use of advanced analytics, and integrating the results into their business processes should be an integral part of every insurer’s strategy.
Insurance industry professionals are also leveraging external data sources and adding more information about a claimant or injured party. Using machine learning in collecting the data will help considerably in how insurers become more data-led and driven businesses.
There are a few excellent examples showing how insurers are using smarter predictive analytics to faster claims and process them with almost no human intervention.The other data analytics issue that insurers are facing is that actuarial science has limits when used to predict new categories of 21st-century risks like cyber, food safety, or complex supply chain disruption.
Helping businesses and individuals in managing these risks result in huge potential rewards for the industry. However, until recently, insurance professionals did not have the tools required to understand or estimate these risks accurately. There has just not been adequate data and loss experience for conventional modeling of the emerging categories of risk. While there are more than a hundred years of data about extreme weather, this is not the case for cyberattacks, for example.
These new types of risks have quite different patterns and connections, from threats to vehicles and property. For example, cyber risks are coalesced not by being in the same building or on the same flood plain, but also by patterns of software usage, network connectivity, and human error.
Even for well-understood risks, old assumptions may not apply. Indeed, the future might be very different from the past. For example, changes in technology such as a rise in semi-autonomous vehicles, and human behavior such as distracted driving have already affected losses.
Insurance professionals will work with data in newer ways is by using new models of technology, such as Internet-scale “data listening” that, cleanses, and updates petabytes of data to build risk models in cyber.
After all, the industry is moving towards applying machine learning, NLP, and other modeling techniques along with third-party data in support of both operational and risk analytics. Industry is embracing underwriting tools for evaluating and estimating risk through the scores used to make better operational decisions regarding service and claims after those risks become insured.
Data science has proven to be a boon so far and it will help the insurance industry immensely. For more information log on to Global Tech Council.