Insurance Analytics Statistics 2023
– Everything You Need to Know

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

LLCBuddy editorial team did hours of research, collected all important statistics on Insurance Analytics, and shared those on this page. Our editorial team proofread these to make the data as accurate as possible. We believe you don’t need to check any other resources on the web for the same. You should get everything here only 🙂

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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 🙂

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]

Also Read

Reference


  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

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