Insurance Analytics Statistics

Insurance Analytics Statistics 2023: Facts about Insurance Analytics outlines the context of what’s happening in the tech world.

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Top Insurance Analytics Statistics 2023

☰ Use “CTRL+F” to quickly find statistics. There are total 22 Insurance Analytics Statistics on this page 🙂

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Insurance Analytics “Latest” Statistics

• 5% of patients, according to the National Academy of Medicine, account for close to 50% of all healthcare expenditures.^[1]
• In 2 years, the usage of claim management may rise from 10% to 40% for individual life and from 37% to 87% for group life.^[1]
• The data for predictive analytics to calculate individual life policies are as follows: 70% large carriers, 50% midsize carriers, and 54% small carriers.^[1]
• In a study by Willis Towers Watson, it was discovered that life insurers using predictive analytics reported a 67% decrease in costs and a 60% rise in revenues.^[1]
• In two years, the usage of mortality and morbidity risk may rise from 19% to 56% for group life and from 23% to 75% for individual life.^[1]
• Companies with an enterprisewide analytics strategy in place see an average yearly revenue increase of more than 7%, according to research by Cisco Systems.^[1]
• The Apache Hadoop framework was either being used or was being considered for use by 45% of major life insurance companies, 50% of midsize carriers, and 29% of small carriers for handling big data.^[1]
• As of September 20, 2018, 82% of major life insurers and 50% of midrange and small carriers were utilizing or considering adopting cloud-based platforms for their big data requirements, according to a poll by Willis Towers Watson.^[1]
• Fraudulent activities account for 5-10% of insurance companies’ claim expenses in the US and Canada.^[2]
• About 60% of Americans believe that social media has made it simpler for customers to get answers and get issues resolved.^[2]
• A 2020 Triple-I Consumer poll found that a record high 27% of homeowners claimed they had flood insurance, which is higher than NFIP forecasts.^[3]
• According to the US Department of Interior, up to 90% of wildland fires in the US are started by humans.^[4]
• By the end of 2022, insurance companies plan to spend up to US $56.97 billion, according to the most recent study.^[5]
• According to research, data deployment improves access to insurance services by 30%.^[5]
• The insured bears a greater part of the damage if the insured fails to maintain the amount stipulated in the clause, typically at least 80%.[1]
• 90% of the anticipated monthly claims, for example, are self-funded by the employer, and the insurance covers the other 10%.[1]
• According to ACS data, from 6.94M in 2019 to 7.04m in 2020, the number of persons working in the finance & insurance industry subsector has increased at a pace of 1.42%.[2]
• Composition by sex, the finance & insurance industry subsector employs 56.8% of women, making them the most prevalent sex in the workforce.[2]
• 10.7% of employment in finance and insurance is occupied by financial managers, which are followed by insurance salespeople (7.1%).[2]
• The Finance and Insurance industry anticipated a 10-year production growth rate of 15.2% is less than the expected 24 .2% national output growth rate.[2]
• Less than the average rate of employment growth in the country, which is 7.66%, is predicted for this industry’s growth.[2]
• Compared to the 73% increase between 2000 and 2010, the average annual rise in medicare expenditure per beneficiary between 2010 and 2018 was merely 17%.[3]

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How Useful is Insurance Analytics

One of the key benefits of insurance analytics is its ability to help companies better understand their customers. By analyzing customer data, insurers can identify patterns, trends, and preferences that can inform product development, marketing strategies, and customer service improvements. By using predictive analytics, insurers can accurately predict customer behaviors, needs, and preferences, enabling them to offer more personalized and targeted products and services.

Insurance analytics also plays a crucial role in risk assessment and underwriting. By using sophisticated modeling techniques and algorithms, insurers can analyze a wide range of data points to evaluate risk factors more accurately and set appropriate pricing for policies. This not only helps insurance companies make more profitable underwriting decisions but also ensures that customers receive fair and competitive premiums based on their individual risk profile.

Furthermore, insurance analytics can help companies detect and prevent fraudulent activities. By analyzing claims data and behavioral patterns, insurers can identify potentially fraudulent claims and take appropriate actions to mitigate risks. This not only protects the company from financial losses but also helps safeguard the interests of honest policyholders by keeping premiums affordable.

In addition to improving customer satisfaction and reducing risks, insurance analytics can also optimize operational processes within insurance companies. By analyzing internal data and workflows, insurers can identify bottlenecks, inefficiencies, and areas for improvement. This can lead to streamlining operations, reducing costs, and enhancing overall productivity.

Moreover, insurance analytics can also provide valuable insights into market trends and competitive landscapes. By analyzing external data sources, such as industry reports, economic indicators, and social media trends, insurers can gain a better understanding of the market dynamics and make more strategic business decisions. This can help companies respond proactively to changing market conditions, capitalize on emerging opportunities, and stay ahead of the competition.

Overall, insurance analytics offers a wide range of benefits to insurance companies, including improved customer understanding, better risk assessment, fraud detection, operational optimization, and market intelligence. By harnessing the power of data and analytics, insurers can make more informed decisions, drive business growth, and deliver better value to their customers. As the insurance industry continues to evolve and adapt to new challenges, insurance analytics will play an increasingly important role in shaping the future of the industry.

Reference

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1. maryville – https://online.maryville.edu/blog/predictive-analytics-in-insurance/
2. duckcreek – https://www.duckcreek.com/blog/predictive-analyitics-reshaping-insurance-industry/
3. iii – https://www.iii.org/fact-statistic/facts-statistics-flood-insurance
4. iii – https://www.iii.org/fact-statistic/facts-statistics-wildfires
5. virtueanalytics – https://www.virtueanalytics.com/data-analytics-in-the-insurance-industry/
6. theinstitutes – https://web.theinstitutes.org/designations/associate-insurance-data-analytics
7. kff – https://www.kff.org/other/state-indicator/total-population/
8. mckinsey – https://www.mckinsey.com/industries/financial-services/our-insights/how-data-and-analytics-are-redefining-excellence-in-p-and-c-underwriting
9. oregon – https://www.oregon.gov/oha/hpa/analytics/pages/insurance-data.aspx