Data analytics is a complex field that many companies use to maximize profits and improve business practices. But what exactly is it, and how does it relate to insurance?
Here are a few key facts about using data analytics in insurance and what it means for you.
What Is Data Analytics in Insurance?
The first question is: what is data analytics? This branch of study is rooted in statistics and data and has skyrocketed in scope and usefulness with the advancement of computers.
If that sounds confusing, don't worry! You don't need to understand all the complex math to understand the basics of how it works.
Many types of companies use data analysis. It's used in the clothing industry, entertainment, manufacturing, and everything in between. This improves their savings, allowing them to cut costs and expand their businesses.
Analysts can break down a lot of data to find ways to predict the likelihood of certain costs, insurance claims, or injuries. This can make it cheaper for you to buy insurance because they won't charge you for things you aren't likely to incur.
For example, let's say that you have a bad health condition. Normally, your insurance costs would go up, and that would be it. But with data analytics, the insurance company can look for things like exercising or not smoking that will bring your costs down.
How Is Data and Predictive Analytics Used in Insurance?
A key aspect of data analytics is the idea of predictive modelling or creating math equations to try to gauge the chance of something happening.
But what is predictive analytics? There are plenty of predictive analytics examples around you every day. You can see predictive modelling or analytics used in things like weather reports, where they tell you that there is a "60% chance of rain." Their predictive models gave a percentage of around 60%, based on a wide range of factors and data.
Insurance companies use statistical analysis and predictive analytics in insurance to help determine how much to charge. They also look at the likelihood of certain accidents or medical conditions. Although the math behind this data science is rather complex, the idea is rather simple.
Basically, each data point contributes to a person's risk of needing insurance. This can include injuries, hospitalizations, medicines, or anything else of that nature.
If you have certain heart health conditions, for example, you might qualify for a pacemaker. A data science predictive model will take the health factors and a pacemaker into account when looking at your insurance. This allows you to get a more personalized plan that will cover the heart issues that you need.
Predictive modelling isn't perfect, but it can help insurance companies save money and save their customers money. Do certain groups of people have a higher likelihood of getting hospitalized? The information gained from big data can help!
A key aspect of using predictive analytics tools and data science is that they give a statistical chance for a group, but do not guarantee any outcomes. But when applied to large groups of people, a company's predictions are greatly improved, which improves their savings.
What Are Some of The Tools Used?
There are plenty of life insurance predictive analytics examples, including the way companies use health risk assessments.
A Health Risk Assessment (HRA) is a way for a physician to examine at-risk patients and assess their chances of having difficult medical incidents or other health risks. Smoking, drinking, poor diet, and other things can negatively affect a person's HRA.
Data analytics can bolster the HRA, giving a better idea of how health factors can affect things. A healthcare provider may not know how much smoking increases the chance of lung cancer, but data analysis can find that out.
Sometimes companies use Artificial Intelligence (AI) and Machine Learning as well. This helps them perform data analysis on health data. With this, they can use the collected data and search for key findings, trends, and concerns.
Insurance companies can use the data from the HRA and from their insurance analytics to target people and offer incentives. This lowers the chances of health issues, which can save both the insurance company and the client money. It's a win-win!
For example, an insurance company might offer an incentive to walk a certain number of steps a day, attend health classes, or reduce smoking. If the client takes these incentives, they will hopefully be healthier, and can also get a discount on their insurance.
Examples of Data and Predictive Analytics
With the advent of data and data analysis, customers now have better choices for choosing health plans. Plans can be customized to fit what they need, but customers can also choose a plan that's right for them based on the data they provide.
By looking at collected data, health insurance agents can help customers find a plan that works for them and cover what they need. If a customer's plan doesn't cover their frequent doctor visits, the data will reflect that. An agent can then help them select a different plan with the variables that fit their needs.
The use of data analysis improves both the customers' options for plans and the insurance company's ability to customize those options. By using detailed analysis, companies can cut excess plans and spending where it isn't necessary, and put money towards the treatment and plans people actually need.
Additionally, insurance companies can use data to perform predictive marketing. This allows them to target people who may fit certain profiles or needs that the company can cover, which bolsters the company's finances, which in turn helps them cut costs for customers.
Data analysts can improve pricing and risk selection as well. By analyzing the given data, they can see where prices need to change, and what kind of risks are incurred. This can be applied both to individuals as well as health conditions or procedures.
More Personalization
Nobody wants to be simply another cog in the system. While it seems counterintuitive, big data analysis can actually help health insurance companies personalize your plans and care.
If you have a certain occupation with one type of hazard, but live in a city with another type of hazard, and don't have health problems related to either, that's something you want your insurance company to know! You don't want them to simply lump everyone in one city together on the assumption of health problems.
Basically, insurance analytics offers health insurance companies the chance to only focus on the issues that affect or could affect you, and not charge you as much for things that seem less likely. This also lets them improve your coverage for the things that matter to you, giving you a better outcome.
You'll still want to compare health insurance plans to see what they have to offer. Just remember, a plan that can be personalized for your needs is a huge benefit! You want your health insurance company to take care of you and your actual needs!
Fraud Prevention
Medical insurance fraud is a serious problem facing insurance companies and governments. Anywhere from hundreds of millions to billions of dollars are stolen every year with fraudulent claims. This drives prices up and slows down legitimate claims.
So how are companies using claim analytics to prevent this, and how is this helping consumers?
There are a lot of parts to it, but basically, the data analysts can look for patterns, things that don't fit, and other insurance anomalies. Where an individual on the ground level might not notice things going wrong, the big picture view can sometimes spot things that would be otherwise missed.
Although it sounds backwards, sometimes a more removed, big picture outlook can help spot these issues. Insurance fraud often involves fooling people in the system, so it takes someone from the outside to spot the problem.
Data analysts can help find unusually high payouts or people who are filing weird claims. Some companies have entire teams of data analysts just for fighting fraud.
The less money insurance companies have to pay in fraud, the more money they save. This saved money helps them pass on savings to their customers. It's a win win all around!
Learn More on Data Analytics and Insurance
While this is a complex subject that mostly goes on behind the scenes, it can be quite helpful for insurance companies and customers. Data analytics in insurance has helped companies improve greatly, and that improvement is likely to continue.
If you have any questions or concerns about data analysis or any other insurance topics, we'd love to hear from you! Feel free to contact Insurdinary with any questions you have, and we'll do our best to help in any way we can.
