Using your fleet data to prove you’re a better insurance risk
News and industry articles highlight how the insurance industry is changing with the use of big data and technology.
This started in the consumer market and is slowly becoming a reality in commercial markets, as those same consumers seek the agility and flexibility for their business operations that they get in their private lives.
But how about flipping this?
How can commercial fleet operators better use their own fleet data to show their risk profile situation and improvement to their insurers? And, by so doing secure better insurance rates.
That is, how can you use your fleet data to prove you’re a better risk?
The problem of doing it the same old way
While change is happening, the traditional insurance method still dominates commercial fleet insurance.
That is your fleet’s insurance is calculated on the basis of what sector you are in, how big your fleet is, your prior claims history, your operating times and where you operate.
While this has been the accepted way of doing things, is it necessarily a good indicator of your specific risk, considering it’s essentially based on years of averages from loads of commercial fleets, and not your fleet’s specific situation?
Or, put another way, are you still paying for issues that happened three years ago, which you’ve addressed, and you are now leading the way on, in fleet safety in your business sector?
How to use your fleet data to prove you’re a better risk.
How can you change the model and use your fleet data to have a better discussion with your insurer about your premiums?
At CMS we’ve seen several of our customers do just that, using the following topics and their supporting data sets, from their use of Clara, our fleet risk management system.
Your fleet’s risk profile
Using the data from your fleet risk management system, you can show how your risk profile has changed (normalised for changes in mileage, driver, and vehicle numbers) over time.
With this, you can detail how your five biggest or most common risk factors have changed. Plus, highlight what steps you’ve taken to achieve this.
This can be taken down to depot and driver level, to demonstrate both how much data you have, but also how you can use it to target problem areas at a very precise level.
You can use driver engagement as a metric to demonstrate how you’ve supported your analysis and insight with action.
For example, with your fleet risk management data, you can show how many touch points – be they management interventions or training sessions – you have had with your drivers to help them become more aware of their risk factors.
With such data, you can then show how drivers, both new and established, have improved over a three month period, for example, due to this management and training investment.
FNOL – first notification of loss – is a key driver in claims cost management, as we highlighted in this article – the faster an incident is reported the faster management resources can be deployed and the better the resulting control of related costs.
Over time, this improved claim cost management will lead to better premiums.
However, this goes further. The more information that you can provide to your insurer for individual incident claims, the better they can work to reduce claim severity, from their point of view. Savings for your insurer, derived from your actions, should then benefit you.
Of course, doing this a few time is not enough. To have an impact the data needs to be of good quality and in the quantity of 70%+ of your claims supported by it, to be of value in demonstrating your control of the situations to your insurer.
Challenge your insurer
While all of this might be alien to your insurer or broker, why not challenge them?
Turn the discussion around by using your fleet data to back up your situation and answer your case for better insurance.
Ultimately, you’ve got nothing to lose by doing it.
Also, you’ll be demonstrating additional value for the time and energy you’ve invested into fleet risk management and the collection and use of the associated data.