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Insuring the Insurance sector with Data and Analytics

Insurance February 11, 2021

Insuring the Insurance sector with Data and Analytics


Importance of data in insurance 

Customer expectations are changing. Sources for data collection have increased. Insurance companies are continually evolving processes and models. 

Insurance companies are investing heavily in infrastructure and tools to get a better grasp of their customers. Data helps insurance companies understand what their customers’ most in-depth expectations and needs are. 

By understanding the clear expectations and needs, companies can develop new coverage options with policies that cover contingencies that people want, rather than what they think their customers want. The aging population’s needs are continually changing. Data helps companies respond to the aging population’s needs in real-time. 

When it comes to the younger audience, they expect instant processing and the mobile-first approach. Data can help companies offer those cutting-edge facilities to the younger audience. 

AI, blockchain, IoT, and machine learning improve efficiencies and create seamless connections with customers. 

Importance of analytics in insurance 

According to the Coalition of Insurance Fraud, the US loses about $80 billion every year in insurance fraud. Predictive analytics is designed to identify and prevent potential fraud before it happens. Insurance companies can also take corrective measures by going back in time. 

Big data analytics helps insurance companies check for any suspicious events right before the hefty payouts are made. Analytics uses police crime records and social media information to help companies understand the risk level. 

Impact of Data Analytics

How data and analytics give insurers an edge 

Accenture recently conducted a survey of over 47,000 consumers globally for its ‘Global Insurance Consumer Study’. 

According to the report, seven out of ten consumers said they would no problems sharing their data concerning their health and lifestyle with insurance companies to help reduce insurance premiums. Insurers rely on big data to accurately assign premiums to each policyholder by comparing driving behavior with a larger pool of data. The driving behavior is a sign of assessing risk factors.  

Companies like IBM and LexisNexis Risk Solutions allow insurers to have such intricate information through AI and machine learning. Companies like DataRobot and MarkLogic analyze vast amounts of data using machine learning-based variables to find out answers. DataRobot is designed to select the most accurate statistical model by cleaning up the data automatically. Using information provided by these models, insurers can understand if they should pay a claim.  

Oscar, a technology-driven health insurance company, aggregates medical records with patient histories to make permission-based predictions and recommendations to consumers. This level of intervention allows the insurer to intervene when patients are at risk. 

Hippo, a property insurance company in America, uses IoT and real-time data collected by devices to assess household risk and allows the insurer to set higher premiums for policyholders with high-risk behavior, such as forgetting to lock doors and set alarms.  

As policy claims for COVID-19 surged over USD 450 million, insurers rely heavily on digital technology, data, and analytics to reduce the application process, onboarding costs, and consumer attrition rates. 

Data and analytics help insurers settle claims effectively and drastically improving customer experience.  

An improved customer experience translates to stronger brand loyalty.  

Insurers who are prepared to be adaptive and are committed to handling tomorrow’s customers’ needs will find a way to survive any oncoming disruptions.  

Use Cases


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