Are you looking to add Insurance Analytics to your arsenal of tools? Maybe for your business or personal use only, whatever it is – it’s always a good idea to know more about the most important Insurance Analytics statistics of 2024.
My team and I scanned the entire web and collected all the most useful Insurance Analytics stats on this page. You don’t need to check any other resource on the web for any Insurance Analytics statistics. All are here only 🙂
How much of an impact will Insurance Analytics have on your day-to-day? or the day-to-day of your business? Should you invest in Insurance Analytics? We will answer all your Insurance Analytics related questions here.
Please read the page carefully and don’t miss any word. 🙂
On this page, you’ll learn about the following:
Best Insurance Analytics Statistics
☰ Use “CTRL+F” to quickly find statistics. There are total 97 Insurance Analytics Statistics on this page 🙂Insurance Analytics Latest Statistics
- The average homeowners insurance premium rose by 3.1 percent in 2018, following a 1.6 percent increase in 2017, according to a January 2021 study by the National Association of Insurance Commissioners, the latest data available. [0]
- The average renters insurance premium fell 0.6 percent in 2018 marking the fourth consecutive annual decline. [0]
- Renters insurance premiums fell 2.7 percent in 2017. [0]
- Fortythree percent of homeowners said they had an inventory in the 2020 Triple I Consumer Poll. [0]
- The survey showed that homeowners in the South and West were more likely to have a home inventory , followed by homeowners in the Northeast and Midwest. [0]
- In 2019, 5.1 percent of insured homes had a claim, according to ISO. [0]
- Property damage, including theft, accounted for 97.2 percent of homeowners insurance claims in 2019. [0]
- In 2019, 5.1 percent of insured homes experienced a claim, compared with 6.2 percent in 2018. [0]
- Homeowners insurance losses, net of reinsurance, rose to $63.8 billion in 2020 from $54.2 billion in 2019, according to S&P Global Market Intelligence. [0]
- Property damage 96.1% 96.% 97.6% 97.7% 97.% Wind and hail .5 33.4 41.0 36.3 34.3 Water damage and freezing 4.4 9.0 18.7 3.7 9.4 Fire and lightning 3.7 6. 3. 30. 5.1 Theft 1.9 1.9 1.1 1.0 1.0. [0]
- . . 7. Liability 3.9% 3.8% 2.% 2.3% 2.8% Bodily injury and property damage 3.7 3. 2.2 2.1 2. Medical payments and other 0.2 0.2 0.2 0.2 0. Credit card and other. [0]
- Total 00.0% 00.0% 00.0% 00.0% 00.0%. [0]
- In the fiveyear period, 2015 2019, 5.7 percent of insured homes had a claim. [0]
- Wind and hail accounted for the largest share of claims, with 2.5 percent of insured homes having such a loss, followed by water damage and freezing with 1.9 percent of homes having a loss. [0]
- The cost of living rose 4.7 percent in 2021. [0]
- The cost of motor vehicle insurance increased 3.8 percent after a 4.6 percent decline in 2020, when drivers reduced their driving due to the COVID. [0]
- The cost of tenants and household insurance declined slightly, down 0.3 percent. [0]
- Used cars and trucks increased a significant 26.6 percent and the median price of a single family home increased 17.7 percent and nearly 100 percent since 2012. [0]
- 150.3 0.9% 131.3 3.1% 198.7 NA 303.5 2.0% 177 6.5%. [0]
- 4.8% NA 23.4% 99.4% December 996=00. [0]
- The U.S. homeownership rate was 65.5 percent in the fourth quarter of 2021, according to the U.S. Census Bureau. [0]
- The 2010 Census showed that in some of the largest cities renters outnumbered owners, including New York, where 69.0 percent of households were occupied by renters, followed by Los Angeles , Chicago and Houston. [0]
- Year Homeowners Percent change Renters. [0]
- Rank Group/company Direct premiums written Market share State Farm Mutual Automobile Insurance $,046,55 8.4%. [0]
- Homeowners Insurance Industry Underwriting Expenses, 2020 Expense Percent of premiums Losses and related expenses. [0]
- Loss and loss adjustment expense ratio 77.0% Incurred losses 67.5. [0]
- Defense and cost containment expenses incurred 1.8 Adjusting and other expenses incurred 7.7 Operating expenses Expense ratio 28.%. [0]
- Combined ratio after dividends 105.9%. [0]
- As a percent of net premiums earned. [0]
- As a percent of net premiums written. [0]
- There were 113,500 deaths from unintentional home injuries in 2020, up 21.1 percent from 2019. [0]
- The number of unintentional home injury deaths has increased by 272 percent since 1999, largely due to increases in unintentional poisonings and falls. [0]
- However, the number of policies in FAIR plans peaked in 2011 and had been falling steadily through 2018, down 49.7 percent from 2011 to 2018, while exposure dropped by 54.6 percent. [0]
- In 2019 the downward trend ended and from 2018 to 2020, total policies grew 10.1 percent while exposure grew 30.8 percent. [0]
- In 2020, 66.6 percent of housing units were owner occupied and. [0]
- 33.4 percent were renter occupied, according to the latest U.S. Census figures. [0]
- In 2019, 32.1 percent of owner occupied units housed people age 65 and over. [0]
- The same year, 16.2 percent of rental units housed people over age 65. [0]
- The nation’s homeowners paid a median of $1,510 monthly housing costs in 2019, compared with $1,301 for renters, according to the latest American Housing Survey from the Census. [0]
- However, renters usually paid a higher percentage of their household income on these costs than did owners, 45.1 percent compared with 26.5 percent of homeowners who spent 30 percent or more of their income on housing costs in 2019. [0]
- State Percent Rank State Percent Rank Alabama 74.8% 4 Montana 68.4% 33 Alaska 64. 44 Nebraska 69.8. [0]
- 74.2 8 Wyoming 73.9 9 Missouri 71.1 19 United States 66.6%. [0]
- States with the same percentages receive the same rank. [0]
- In 2020 West Virginia, Delaware, Maine, Alabama and Minnesota had the highest percentage of owner. [0]
- State Percent Rank State Percent. [0]
- Rank Alabama .6% 35 Montana 8.9% 0 Alaska 7. 6. [0]
- States with the same percentages receive the same rank. [0]
- In 2019, Hawaii, California, New Jersey and Florida had the highest homeownership costs, based on the percentage of homes in which owners spent 30 percent or more of their income on homeowner. [0]
- North Dakota, Indiana, Iowa and Kansas had the lowest costs, based on the percentage of homes in which owners spent 30 percent of more of their income on homeowner. [0]
- Rank Alabama 40.9% 35 Montana 4.% 33 Alaska 4.0 34 Nebraska 38.4 47 Arizona 43.4 Nevada 48. 5. [0]
- Percent of renter occupied units spending 30 percent or more on rent and utilities such as electric, gas, water and sewer, and fuel. [0]
- Nationwide, 45.1 percent of renters spent at least 30 percent of their household income on rent and utilities in 2019. [0]
- In 2019 North Dakota, South Dakota, West Virginia and Kentucky had the lowest percentage of rental units in which occupants spent 30 percent or more of their income on rent. [0]
- VA released Percent Change in Veteran Population by State from 2000 to 2020The Department of Veterans Affairs provides official estimates and projections of the Veteran population using the Veteran Population Projection Model. [1]
- The “Percent Change in Veteran Population” data table shows the change in the Veteran population from 2000 to 2020 by state. [1]
- During this period, the average decrease in the Veteran population is 25% at the state level. [1]
- For example, a survey conducted by Willis Towers Watson found that life insurers who use predictive analytics reported a 67% reduction in expenses and a 60% increase in sales. [2]
- Four factors were rated highly important by life insurance companies in the drive to adopt predictive analytics Competitive pressures in product development and pricing (cited by 78% of respondents). [2]
- Customer relationship management (67%) Earnings and profitability pressures (64%). [2]
- 17% reported a strong positive impact, and 50% reported a somewhat positive impact. [2]
- 17% reported a strong positive impact, and 43% reported a somewhat positive impact. [2]
- 13% reported a strong positive impact, and 47% reported a somewhat positive impact. [2]
- The expanded use of predictive analytics by life insurers can be applied to four specific functions Pricing and rate setting use is forecast to increase from 31% to 56% in two years for group life, and from 18% to 55% for individual life. [2]
- Underwriting use may increase from 52% to 92% in two years for individual life. [2]
- Mortality and morbidity risk use may increase from 19% to 56% in two years for group life, and from 23% to 75% for individual life. [2]
- Claim management use may increase from 37% to 87% in two years for group life, and from 10% to 40% for individual life. [2]
- Current use of predictive analytics to calculate individual life policies 70% of large carriers (increasing to 90% in two years)50% of midsize carriers (75%)54% of small carriers (89%). [2]
- 70% of large carriers (increasing to 90% in two years). [2]
- 71% of large carriers (increasing to 100% in two years). [2]
- 20% of midsize carriers (40%) 15% of small carriers (38%). [2]
- Internal customer data 55% used as of September 2018 (82% planned to use in two years). [2]
- Customer interactions and surveys 55% (73%). [2]
- Clickstream data 18% (45%) Social media 13% (35%) Web scraping 11% (29%) Wearables 6% (38%). [2]
- The Willis Towers Watson survey found that 82% of large life insurers and 50% of midsize and small carriers were using or exploring the option of using cloud based environments for their big data needs as of September 2018. [2]
- Similarly, 45% of large life insurance carriers, 50% of midsize carriers, and 29% of small carriers were either using or exploring the option of using the Apache Hadoop framework for managing big data. [2]
- Only 13% of the insurers surveyed believed the models were well understood or very well understood by people outside of data science and actuarial areas, and 40% stated that widespread understanding is very limited or nonexistent. [2]
- According to the National Academy of Medicine, 5% of all patients account for nearly 50% of all healthcare spending. [2]
- Research by Cisco Systems found that companies that have an enterprisewide analytics policy in place have average annual revenue growth greater than 7%. [2]
- ACS includes a 1% sample of the US population and allows for precise state. [3]
- Estimates with relative standard errors greater than 30% are not provided. [3]
- For example, even the leading insurers can see loss ratios improve three to five points, new business premiums increase 10 to 15 percent, and retention in profitable segments jump 5 to 10 percent, thanks to digitized underwriting. [4]
- In our experience, up to 95 percent of policies may undergo straight through processing with no underwriter involvement. [4]
- As a result of these efforts, the insurer can now provide customers with initial quotes in less than two minutes and the standard time for issuance and binding has been cut by 50 percent. [4]
- Armed with the new platform, the insurer expects to increase new business premiums by 50 percent. [4]
- By decreasing manual inputs by up to 90 percent, the insurer aims to dramatically simplify and improve the agent experience. [4]
- It has set a goal to improve its target loss ratio by 5 to 7 percent over three years. [4]
- For example, 22% of uninsured Oregonians were uninsured because they lost OHP coverage. [5]
- Each of these modules generates a risk score for each claimant that is aggregated and normalized to a scale of 0 to 100, with low scores meaning it is less likely to be an overpayment and higher scores meaning it is more likely to be an overpayment. [6]
- For example, a model that identifies 25% of overpayments after examining only 5% of certifications would be extremely useful – such a model would identify overpayments at five times the baseline rate. [6]
- The charts below reflect a sample of results including one that focuses on the highest scoring 5% of claimants. [6]
- The top 5% to 10% of claimants is typically of greatest interest because they represent claims with the highest risk – and are those most worth examining given limited resources. [6]
- Scott’s assistant notifies him that his mobility insurance premium will increase by 4 to 8 percent based on the route he selects and the volume and distribution of other cars on the road. [7]
- It also alerts him that his life insurance policy, which is now priced on a “payasyou live” basis, will increase by 2 percent for this quarter. [7]
- According to the National Insurance Crime Bureau , catalytic converter thefts have seen a significant increase across the country since March of 2020, the start of the global pandemic. [8]
- According to NICB’s Operations, Intelligence and Analytics study of reported thefts, there were 108 catalytic converter thefts per month on average in 2018, 282 average monthly thefts in 2019, and 1,203 average thefts per month in 2020. [8]
- NICB member companies wrote over $530 billion in insurance premiums in 2020, or more than 82% of the nation’s property. [8]
- That includes more than 95% of the nation’s personal auto insurance. [8]
I know you want to use Insurance Analytics Software, thus we made this list of best Insurance Analytics Software. We also wrote about how to learn Insurance Analytics Software and how to install Insurance Analytics Software. Recently we wrote how to uninstall Insurance Analytics Software for newbie users. Don’t forgot to check latest Insurance Analytics statistics of 2024.
Reference
- iii – https://www.iii.org/fact-statistic/facts-statistics-homeowners-and-renters-insurance.
- va – https://www.va.gov/vetdata/.
- maryville – https://online.maryville.edu/blog/predictive-analytics-in-insurance/.
- kff – https://www.kff.org/other/state-indicator/total-population/.
- mckinsey – https://www.mckinsey.com/industries/financial-services/our-insights/how-data-and-analytics-are-redefining-excellence-in-p-and-c-underwriting.
- oregon – https://www.oregon.gov/oha/HPA/ANALYTICS/Pages/Insurance-Data.aspx.
- statistics – https://www.statistics.com/unemployment-insurance-fraud-catching-the-crooks/.
- mckinsey – https://www.mckinsey.com/industries/financial-services/our-insights/insurance-2030-the-impact-of-ai-on-the-future-of-insurance.
- nicb – https://www.nicb.org/news/news-releases/catalytic-converter-theft-skyrocketing-nationwide.
How Useful is Insurance Analytics
One of the key applications of insurance analytics is in the area of risk management. Traditionally, insurance companies have relied on actuarial tables and historical data to assess risk and set premiums. However, these methods are limited in their ability to accurately predict future risks and trends. With the advent of analytics, insurance companies can now draw upon a wide range of data sources, including social media, weather patterns, and demographic information, to develop more sophisticated risk models. By analyzing this data, insurance companies can identify patterns and trends that help them better understand the underlying risks associated with their policies and adjust their pricing and underwriting strategies accordingly.
Insurance analytics is also transforming the way insurance companies interact with their customers. By analyzing customer data, insurance companies can gain a deeper understanding of their clients’ needs, preferences, and behaviors, allowing them to tailor their products and services to better meet their customers’ expectations. For example, by leveraging predictive analytics, insurance companies can anticipate when customers are likely to experience a life event that may prompt them to purchase a new insurance policy or modify an existing one. By reaching out to customers at the right time with the right offer, insurance companies can improve customer satisfaction and retention rates.
Moreover, insurance analytics is helping insurance companies streamline their operations and enhance overall efficiency. By analyzing internal processes and workflows, insurance companies can identify bottlenecks, eliminate redundant tasks, and optimize resource utilization. This not only improves operational efficiency but also reduces costs and allows insurance companies to pass on the savings to their customers in the form of lower premiums or better coverage options.
Despite these significant benefits, the adoption of insurance analytics is not without its challenges. One of the major challenges facing insurance companies is the sheer volume and complexity of data that they need to process and analyze. Many insurance companies struggle to manage and leverage the vast amounts of data at their disposal, hampering their ability to extract meaningful insights and derive actionable intelligence from this data. Additionally, there are concerns surrounding data privacy and security, as insurance companies are entrusted with sensitive personal information that must be safeguarded against potential threats and breaches.
In conclusion, insurance analytics is a powerful tool that is reshaping the insurance industry in profound ways. By harnessing the power of data and analytics, insurance companies can unlock new opportunities for growth, drive better decision-making, and enhance customer experiences. However, as insurance companies continue to navigate the complexities and challenges associated with insurance analytics, it is imperative that they develop robust data strategies, invest in cutting-edge technologies, and prioritize data security and privacy to fully realize the potential of analytics in the insurance industry.
In Conclusion
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