Machine Learning is a Revolution for Insurance
As an insurance company, retention is the name of the game. Holding onto one happy customer is a lot easier than converting one new lead into a customer.
The most tried and true method for holding onto insurance customers is offering better rates than your competition. But insurance pricing requires a deep and complex understanding of risk.
In the past, a lack of technology made this difficult. Insurance companies were disconnected from their most powerful sources of data. Unable to process the sheer volumes of data they have access to, they left valuable insights on the table.
Now, more than ever, this is the case. Real-time telematics data provides insights into driver speed, road conditions, movement data, and weather forecasts. An environmental risk score is complicated, yes, but thanks to machine learning, it's possible to draw powerful, game-changing insights.
So instead of lumping all drivers into generic buckets (age, gender, etc.) and pricing their premium as one size fits all, embrace technology. Let's take a look at the best insurance applications for machine learning.
Tailormade solutions for customers
Insurers can now offer a more personalized insurance premium, thanks to sophisticated machine learning algorithms. For example, one of the insurance applications for machine learning is that it can help insurers gain a better understanding of changing weather patterns. This is done by analyzing historical weather data and future climate predictions.
Another way is by analyzing personal data from vehicles. Insurance companies can see whether or not drivers practice safe driving behavior with the help of machine learning. From the data garnered, insurers can more accurately assess the driver's risk score. As a result, insurers can offer lower premiums to drivers who demonstrate safe driving behavior.
These are just a few of the environmental insurance applications for machine learning.
Predicting Human Behavior Patterns & Profiling
It's a popular notion that human behaviour can be spontaneous, but according to a recent study by Northeastern University network scientists, human behaviour is 93 percent predictable. This can be used to insurers' advantage.
Machine learning can help us not only establish patterns for individual driving profiles but also groups of drivers and fleets. From here, we can discover underlying factors which influence these patterns. When analyzing data about groups of drivers, machine learning can help insurers find trends in behaviour that are difficult to process manually because of the sheer amount of data available and computational abilities.
When it comes to individuals, the insurance applications for machine learning show that humans are more complex than just their age, gender, and the car they drive. Plenty of other unique factors can all contribute to the level of risk a driver has when on the road. These include past behavior, environmental data, and insurance data. This unique driver data can be processed through machine learning and generate a far more accurate score than what can be garnered from generic profiling.
Understanding Traffic Threats and Behavior
Traffic is complex. Many factors go into why different traffic patterns occur, such as location, time of day, the number of cars on the road, etc. This makes it difficult for humans to understand how traffic conditions influence driving risk scores fully.
For example, you may think that most traffic deaths occur in urban areas because there are far more cars in a condensed area. In reality, most traffic deaths occur in rural areas, even though significantly fewer people live there. Many people have this misconception because risk is not one-dimensional.
Having the capabilities to analyze the complexities of traffic patterns and areas of high threat where accidents typically happen is another prime insurance application for machine learning. So, if a driver typically commutes in an area of high threat for accidents combined with unfavorable environmental conditions (like rains, snow, and storms), their risk score would likely be higher and should be priced accordingly.
And don't forget, these insurance applications for machine learning not only help price premiums more accurately. They also help save lives.
Fraudulent claims are one of the most critical challenges in the insurance industry. While there is always a human element involved while assessing the damage and claims, fraud detection is one of the best insurance applications for machine learning.
Using state-of-the-art machine learning algorithms, you have the ability to spot patterns and dubious behaviors in customer claims data. These bad actors can then be detected without hampering the genuine claims. Armed with this knowledge, you can ultimately reduce premiums, positioning yourself more competitively in the market.
Understanding Event-Based Conditions
Every day doesn't have the same traffic pattern. Most people know that 5 P.M. on weekdays marks peak rush hour. But what about one-offs?
Maybe the local state championship game is finishing at 9 P.M.? Or what about the annual parade that closes down Main Street for the day? Thanksgiving traffic is unpredictable on an hour-by-hour basis. Ditto to when the roads actually open up on New Year’s Eve.
These are all independent, event-based factors that can greatly affect one’s risk score while driving. These threats all have data that can be analyzed to find trends and patterns that can assist drivers in avoiding risky situations, which is another fantastic insurance application for machine learning.
All in the Interest of Safety & Vigilance
These main insurance applications of machine learning are designed to lower the risk of driving and detect fraudulent behaviors. If they were to be used, we could significantly decrease our risk on the road. This would allow insurers to lower their rates and increase profits. Not only would drivers be happier with cheaper insurance, but insurers could see greater profits with lesser losses.
With Inaza, that's exactly what we do. Want to know more? Let's get in touch.