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Insurance Data Analytics
Faheem Shakeel
Faheem Shakeel Updated on May 21, 2024  |  5 Min Read

In today’s time, it is imperative for every sector to focus not only on better data handling and governance solutions but also steadfast the use of data that is long untamed. The findings of a study indicate that data-driven organizations are 23 times more likely to acquire more customers than their peers.

Insurance is a data-driven industry. Every day there are new players in the competition and each one of them has a mine of data, but only the ones converting that data into useful insights and using them in their decision-making can make it a gold mine. According to the findings of a recent study, 86% of insurance companies are working on Insurance data analytics mechanisms for optimum predictions of big data reports.

Data Analytics in Insurance Industry

According to a predictive analytics report, current investment in predictive analytics of the Individual Life and Individual Health ecosystem is 70 percent and 40 percent respectively, which is assumed to grow up to 90 percent and 80 percent in just the next two years.

This is the power of data that is being used as a source of strategic decision-making today. But this source of data needs to be unleashed to its full power by procuring insights that will help the insurance companies achieve their long-term goals.

If advanced analytics in insurance is leveraged appropriately, it can revolutionize the insurance business and make their operations resilient and future-ready.

Use of Business Analytics in Insurance

Use of Business Analytics in Insurance

How does Data Analytics Benefit Insurers?

Insurance companies using data analytics solutions have witnessed significant improvements in decision-making underpinned by business intelligence to improve customer conversion. The key benefits offered by data analytics are:

1. Customer Acquisition and Personalization

Customer acquisition through a lead generation of high quality has become progressively tougher at a time when information is available at our fingertips and attention are fleeting. Even if you manage to capture the prospect’s attention, tracking different lead information from different sources can get tricky through paper and spreadsheet-based processes in place. Such a scenario calls for modernizing legacy systems to centralize information and databases, especially when it relates to core business processes. Insurance data analytics of such unstructured data helps you deep dive into the customer behavior patterns and common demographics and characteristics, and target the right customer segments to create market opportunities to up-sell and cross-sell.

Further, data analytics-enabled tools like CRM and agency management systems enable businesses to extract valuable insights from reports that reveal the customer journey, right from search to conversion. It helps them understand customer behavior and enables the marketing department to target the right personalized messages for warming up leads.

2. Meaningful and Deeper Customer Engagement

Satisfied customers drive revenue and brand equity of businesses. Enhanced customer satisfaction is the result of initiatives on advocacy, referral marketing, and brand identity creation. If a business is successful in fulfilling customer expectations, it will automatically register accelerated and unprecedented growth. According to a McKinsey report, satisfied policyholders are 80% more likely to opt for policy renewals.

An insurance company that can correctly predict the needs of prospective customers by looking through data trends and the complete view of a customer’s previous interactions with the brand, has much more potential to make the sale than an insurance company just using conventional methods of selling. Analytics in insurance provides the capability to go through IoT-enabled data to understand the needs, desires, and advice of their customer. Similarly, analysis of the existing customer data can also offer prescriptive insights into improving customer satisfaction. Customer Relationship Management (CRM) solutions can prove to be invaluable in such situations as they can offer detailed and granular insights into the customer’s current and future requirements.

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3. Mitigating Claims Fraud

Claims fraud continues to be a major challenge in the insurance sector. The Coalition of Insurance Fraud estimates that $80 billion is lost annually from fraudulent claims in the USA alone. Additionally, fraud makes up 5-10% of claims costs for insurers in the North American region. However, insurance companies using data analytics have seen considerable improvements in their fraud detection process. With the application of data analytics, insurance claims fraud detection becomes speedier and more accurate. For example, the history of fraudulent cases is stored in the data trends of an insurance company and while processing any claim, the insurers can carefully check if the trend is repeated. This, in turn, helps reduce the act of fraud.

Apart from fraud detection, analytics can also be applied for fraud prevention and mitigation. Advanced analytics and claims predictive modeling leverage both business data and information from external third-party sources for identifying potential claims fraud. Even before the submission of the claims, the predictive analytics model can detect individuals who have more odds of submitting fraudulent claim reports.

4. Predicting Accurate Risk for Underwriting

Underwriting is a complex task for insurers and it can be simplified through insurance underwriting analytics. For example, the data trend would predict a higher auto insurance premium for a customer who has been engaged in rough driving than that for a customer whose data trend predicts a lesser risk profile. Such data can also come in handy during insurance claims automation as insurers can accelerate the policyholder’s journey from FNOL to recovery.

The application of advanced analytics in the insurance underwriting process encourages underwriters to concentrate on subjective tasks that call for judgment and intuitive decision-making while enabling systems to handle back-office work. Data analytics models can also be used for developing better underwriting rules. This, in turn, contributes to a uniform application of underwriting practices and lesser risks.

5. Streamlined Claims Processing

Data analytics in insurance can have a significant impact on improving the efficiency and accuracy of claims processing. By analyzing historical claims data and utilizing predictive modeling, insurers can automate and streamline the claims assessment process. This leads to faster claims resolution, reduced administrative costs, and improved customer satisfaction. Also, as mentioned earlier, applying data analytics in insurance claims processing can help identify fraudulent claims more effectively. This ensures that legitimate claims are processed promptly while mitigating losses due to fraudulent activities.

6. Enabling Business Growth

One of the important elements of the Insurance domain is quantifying the levels of risk so that it can accelerate business growth. Until recent times, the calculation of this business-critical risk was purely intuitive and largely guesswork. However, with hordes of data now readily available, it is possible to base such assessments on pure data rather than conjectures, and even predict eventualities that can disrupt operations. Accordingly, insurance businesses can analyze this data and plug revenue leakages that could be eating into the business’ profits. In this way, Insurance data analytics acts as an engine for the growth of Insurance companies with its capability in predictive analysis of big data.

7. Improving Regulatory Compliance

The insurance industry is subject to a myriad of regulations and compliance requirements. Data analytics in the insurance industry helps insurers navigate this complex regulatory landscape by providing tools to monitor and ensure adherence to compliance standards. Analyzing data in real-time enables insurers to detect and address potential compliance issues promptly. Furthermore, data analytics for insurance companies can assist in generating comprehensive data and analytics reports and audits, thereby simplifying the compliance reporting process. By leveraging analytics to stay abreast of regulatory changes and proactively address compliance concerns, insurers can avoid penalties, reputational damage, and legal challenges associated with non-compliance.


To retain that competitive edge, the emerging leaders of the insurance sector are leveraging insurance data analytics while making decisions concerning pricing strategies and risk selection. McKinsey research shows that accelerating investments in digital and analytics will be the “difference between slowly declining and flourishing” businesses in the insurance sector. Given the unpredictability of the marketplace insurers operate in, an accelerated analytics journey can help them drive strategic decision-making at scale across underwriting, claims management, customer satisfaction, and policy administration functions. Therefore, the insurers should not postpone their analytics agendas.

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