It’s no secret that auto insurance companies extensively apply predictive analytics in insurance to estimate the level of risk of an individual insurance seeker. The higher the risk, the more expensive the insurance coverage, or an insurer may even refuse to provide one.
In this article, we will delve into the reasons that forced insurance companies to resort to predictive analytics in insurance.
Without any further ado, let’s dig into it.
What Role Does Math Play in the Insurance Industry?
Mathematical concepts and statistics help insurers assess risks and determine the pricing model insurance. Actuaries are professionals who use predictive modeling insurance to analyze data related to mortality rates, future events, and financial outcomes.
Actuaries help insurance companies manage their risks by determining probabilities for different scenarios. For example, they can calculate the likelihood of a car accident or natural disaster occurring within a certain time frame in a state.
Predictive analytics in insurance allows insurers to set premiums that accurately reflect the level of risk associated with each policyholder. If all drivers were charged the same price regardless of individual risk factors (age, gender, driving experience, year of manufacture, etc.), it would be unfair to those with lower risk levels. By utilizing the principles of predictive analytics in insurance, companies can offer fair prices, protecting themselves from losses.
Predictive analytics for insurance allows insurers to not only manage existing policies but also develop new products to meet changing consumer needs and demand trends.
As you can see, the importance of math in the insurance industry cannot be overemphasized. It acts as a fundamental aspect of risk management. Thanks to math insurance, insurers can ensure fair coverage and protect themselves from unforeseen costs or economic downturns.
Diverse Risk Factors
Insurance risk is the primary parameter when it comes to compensation for damages. It is a hypothetical event that a person insures against, and in the event of its occurrence, the affected person receives monetary compensation according to the policy terms.
The cost of an insurance policy directly depends on the degree of insurance risk, which is determined by considering a number of criteria:
- Driving history: If there are recorded traffic violations, you will be charged a higher insurance premium. However, experienced drivers with a clean record are eligible for discounts that significantly reduce the cost of coverage.
- Age: The insurance premiums will be higher for drivers aged 16 to 20. This is because this age group has the highest level of involvement in fatal accidents – 43 per 100,000 licensed drivers. For individuals over 65, insurers increase quotes due to the increased probability of being involved in accidents.
|Drivers in fatal crashes
|Percent of total drivers
|Involvement rate (1)
|16 to 20
|21 to 24
|25 to 34
|35 to 44
|45 to 54
|55 to 64
|65 to 74
- Gender. Insurance policies for women are significantly cheaper than those for men. Why is male insurance higher than female? This is because men are much more likely to die or sustain injuries in car accidents. According to statistics, in 2020, a total of 28,033 men and 10,690 women died as a result of collisions.
- Place of residence. The region in which you reside affects the cost of your insurance. This is related to both the legislative requirements of individual states and the overall assessment of fatal accidents on the roads in different regions of the United States. For example, in 2021, the mortality rate per 100,000 people ranged from 5.7 in Rhode Island to 26.2 in Mississippi. Accordingly, the insurance premium for residents of Mississippi will be much higher.
- Credit rating. The insurance company may request information about your credit rating. If you pay your bills on time and have no outstanding debts, this can lower your premium.
- Insurance history of your vehicle. Many insurance companies pay attention to your experience with your previous insurance company. If you have had late payments or a lapse in coverage, you are less likely to be offered a discount. If you have always paid your previous premiums on time, the agency may be willing to work with you and reduce the cost of the policy.
- Model and cost of the car. The higher the price of your vehicle, the higher the rate you will be offered. High-speed sports cars will also cost more, as they are more likely to be involved in accidents.
Insurance companies use these factors, often through insurance predictive modeling, to estimate the likelihood of a policyholder making a claim. The goal is to set premiums that accurately reflect the risk associated with insuring a particular person and vehicle.
Be sure to research the offerings of several insurers, as each evaluates these factors and uses insurance predictive analytics for insurance claims differently, resulting in different costs for the same coverage.
Predictive Modeling in Insurance: Uncertainty in Human Behavior
Uncertainty in the driver’s behavior means unpredictability and variability of their choices and actions. It is very difficult to predict how he or she will behave in a given situation.
This is due to behavioral factors that influence decision-making. Therefore, when conducting predictive analysis in insurance, consider the following:
- Cognitive factors. Individual perceptions, beliefs, and cognitive processes play a crucial role in decision-making while driving. Each person interprets information and assesses risks differently, resulting in unpredictable behavior.
- Emotional influence. Emotional state has a significant impact on driving behavior. Emotions change quickly, making it difficult to accurately predict how a person will react to a particular situation on the road.
- Social and cultural components. Drivers’ behavior is shaped by social norms, cultural values, and the influence of others. These factors influence how people behind the wheel will act in specific circumstances.
- External stimuli. Environmental factors, unforeseen incidents, and external stimuli can cause different reactions. Drivers on the road react differently to the same situations, further increasing the uncertainty in predicting behavior.
Understanding and managing uncertainty in driver behavior on the road is very important to insurance predictive analytics. General advances in behavioral science and data analytics allow insurance companies to gain insight into human driving behavior patterns and correctly identify risks.
Claims Frequency and Severity – Key Indicators in Assessing and Managing Risks
Claim frequency, also known as loss frequency, is the number of claims received by an insurance company during a given period. It is a measure of how often policyholders make claims for monetary compensation for insured events or losses.
The value is calculated according to the following formula:
- Frequency of insured events = Number of insured events for a certain period / Total number of policies or total insured value
A high frequency rate of insured events indicates a higher probability of losses for policyholders, i.e., a higher risk.
The severity of insured events, or loss severity, is the average cost of individual insured events. The indicator reflects the financial impact of each insured event on the insurance company.
The value is calculated according to the following formula:
- Severity of insured events = Total amount paid out on insured events / Number of insured events for a certain period
The high severity of insured events indicates a large number of expensive payouts, which affects the financial stability of the insurer and may lead to an adjustment of the insurance pricing model.
Data Heterogeneity in Auto Insurance
Heterogeneity refers to the diversity and variability of data. This means that they differ in type, structure, format, and other characteristics. For example, there may be different time periods, units of measurement, or sampling.
Understanding and managing data heterogeneity is crucial in predictive modelling in insurance and decision-making. To effectively address heterogeneous data sets, a comprehensive approach to predictive analytics in insurance industry is required.
Numerical Experiments and Sensitivity Analysis
Numerical experiments involve the use of insurance predictive models and simulation modeling. Sensitivity analysis assesses how uncertainty or variation in model inputs affects the variability of outputs.
Together, these techniques provide valuable insights into the behavior and stability of mathematical models, helping insurance companies make informed decisions and improve forecast accuracy.
The complexity of the mathematical models that insurance companies use results from the diverse risk factors, uncertainty in driver behavior, and a wide range of policies that require predictive modeling insurance.
Insurance math helps companies to operate efficiently despite market dynamics and new challenges. Therefore, it is important to pay sufficient attention to the use of predictive analytics in insurance.
Frequently Asked Questions
What is predictive modeling in insurance?
Predictive modeling in insurance is the process of using statistical algorithms and mathematical models to predict future events or outcomes related to insurance.
Victoria Berezhetska is a Content Lead at Phonexa.com and an expert contributor to American REIA. She has a Bachelor of Science degree in Business Administration, with extensive working experience as a PR specialist and content writer. At American REIA, she helps customers find the right educational material through easily digestible blog posts and buying guides backing their insurance coverage choice. Victoria covers diverse topics around digital and insurance marketing, including auto, home, health, and life insurance.