Success in the auto insurance industry largely depends upon two factors: loss ratio and gross written premium (GWP). And while GWP determines how much money is coming in, auto insurance loss ratio is the biggest indicator of how much is going out.
Improving your auto insurance loss ratio means improving your bottom line. But, for the longest time, insurers were largely at the mercy of how good or bad of a year they were having.
Insurtechs are changing the game, though. Big data and AI have empowered insurtechs to lower the auto insurance loss ratio for insurers. Instead of relying on external luck, insurers are now making their own luck.
Let's take a deeper look into the auto insurance loss ratio and how you can set yourself up for a more favorable outcome.
What is auto insurance loss ratio?
Auto insurance loss ratio is defined as the ratio of losses to premiums earned. "Losses" in loss ratios include paid insurance claims and adjustment expenses.
The auto insurance loss ratio formula is as follows: insurance claims paid plus adjustment expenses divided by total earned premiums. For example, if a company pays $80 in claims for every $160 in collected premiums, the loss ratio would be 50%.
- Insurance claims paid is the amount of money paid out by the insurance company for claim settlements.
- Loss adjustment expense is the money incurred by the insurance company to investigate and verify claims.
- Total premiums earned are the amount of money (premiums) paid from clients to the insurance company.
As you can see, there are two levers that can be pulled to improve (i.e. decrease) auto insurance loss ratio:
- Increasing the inward flow of money in the form of total premiums earned, and/or
- Decrease the outward flow of money (i.e. insurance claims paid and other expenses).
So how do you lose less while writing the same amount (or more) premiums?
You work with insurtechs that help find a more accurate price of risk, discover the root causes of fraud, and set yourself up for a better auto insurance loss ratio.
Ingredients to a better auto insurance loss ratio
Gone are the days when you had to wait to record the odometer readings or the travel history of a driver after an accident had taken place. This type of post-mortem analysis provided an ultra-slow feedback loop. It could take months, if not years.
Now, thanks to real-time data, you can think about loss ratio on a second-by-second basis. This drives dynamic data insights into the cause, effect, and nature of accidents.
An in-depth understanding of drivers' behaviour, road conditions, the surrounding environment, and external independent factors is critical in auto insurance. Bolstered by real-time analytics, you can help better predict future behaviour, and help the concerned parties mitigate risk and avoid accidents.
Using AI/ML for predictive analysis
Understanding and predicting the nature of any adverse events is now possible due to the availability of large amounts of data collected from various sources.
We are no longer tied to the static demographic and qualitative data like "26, Male, Red Car". Now, we can base our decisions on environmental factors, like the shape of the road, weather, external conditions, driver's history, traffic patterns, and more. AI and ML can crunch this historic and real-time data and get a clearer picture of risk at any given point in a driver's journey.
These factors can be taken into consideration, analyzed, and used to give insights, thanks to advanced ML/AI techniques used by insurtechs and the monumental processing power we have today.
Real-time alerts and updates for safety
Insurtech can send alerts on a real-time basis to the driver and the insurer in case of any abnormal driving pattern, traffic disasters, climate, weather, or any other dangerous things that could happen during the journey.
This helps lower the overall claims costs because there are simply fewer claims. This can be transformational for your auto insurance loss ratio. In addition, it equips divers to understand their personal risks.
AI Risk Engine to understand risk better
Insurtechs use AI risk engines that leverage sophisticated ML models to get a granular understanding of the risk data and detect risk patterns for any trip. Because of this, they can provide better and fairer pricing models that can generate superior insights and analyses.
Using AI/ML helps insurers create a more comprehensive risk profile and enhance the safety of drivers using alerts and updates. This prevents the accidents that lead to claims and improves the overall auto insurance loss ratio.
Detect fraudulent claims
Insurance fraud not only increases the insurer's expenses but it also makes insurance costlier for other fair players.
Insurtechs now have the resources to detect fraud and anomalies in claims using models trained on data. Using AI/ML for constant analysis, they can raise red flags and detect fraudulent activities.
While fraud can never be eliminated entirely, it can be minimized. This, too, affects claims paid and greatly reduces auto insurance loss ratio.
Improve your auto insurance loss ratio with smarter technology
Auto insurance loss ratio used to be largely out of insurers' hands. Now, they can use insurtech to increase their bottom line, by:
- Using AI/ML for predictive analytics, taking into an almost exhaustive list of factors like demographics, qualitative data, road conditions, weather, and other external factors
- Improving safety with real-time alerts based on predictions and historical behaviour
- Leveraging advanced AI risk engine to price the risk better
- Detecting fraudulent claims
If you would like to know more about how we can help improve your auto insurance loss ratio, let's get in touch.