Commercial auto insurers spend millions processing claims and writing policies. And with the rise in crashes and costly claims year after year, their expenses are only projected to grow. But by implementing proactive strategies, carriers can lower their cost of business, not to mention the costs of underwriting and risk management. To achieve this, carriers must leverage their policyholders’ telematics data.
The Benefits of Operationalizing Telematics Data
Telematics data is taking commercial auto insurers on a journey from being reactive – as they use hindsight to predict the future – through to a predictive model based on a cognitive data-driven understanding of what’s likely to happen.
That is, the metaphor moves from trying to predict the future by looking in the rearview mirror to predicting the future by looking out of the windshield.
Traditional risk management is a reactive model based on the policyholder’s claims frequency and severity. But with the correct use of data, this can become more targeted and tailored to each policyholder’s fleet, enabling the commercial auto insurer to create and offer a portfolio of supporting risk management services – all based on and triggered by the policyholder’s telematics and related data.
These in turn will help the policyholder address their risk factors to lower their claims frequency and severity – leading to cost savings for the commercial auto insurer, lower premiums for the policyholder and a stronger partnership-based business relationship for the two organizations, which will aid customer retention for the commercial auto insurer.
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General Foundations for Better Use of Telematics Data
When embracing the use of telematics data, commercial auto insurers should aim to achieve these three core foundations:
- Create a low-touch, data-sharing process for the telematics data for their customers
- Standardize all telematics and other related data sources into one central data structure
- Enable claims data and other data sources to be easily uploaded and incorporated with the telematics data
Underwriting Uses and Benefits
Traditional underwriting models are based on broad insight. But underwriters can leverage a prospective policyholder’s existing telematics data history (typically the last 90 days), to establish a more personalized understanding of their risk, pre-quote.
Doing this moves the policyholder away from a traditional quote based on their location, sector, size, fleet mileage etc to one based on their fleet’s actual performance over the recent past. This means the prospective policyholder gets a more accurate quote, while the commercial auto insurer gets improved visibility of the detail behind the risk they are underwriting – before committing to taking it on.
Ideally, as the relationship with the policyholder moves forward, with this flow of data, the underwriting team gets a more granular understanding of each of their customers at all stages of the client lifecycle.
Pro Tips for Establishing Client Risk, Pre-Quote
- Give underwriters the ability to upload up to 90 days’ worth of telematics data and benchmark that against existing policyholders
- Give underwriters the ability to see what the risk looks like compared to when they first priced it at and how it has changed over time
Pro Tips for Improving the Visibility of Risk
- Get clear information about how many vehicles a policyholder has active on the road and alerts if that volume goes up
- Access a completely standardized raw data set about the policyholder to review and begin building into the modeling
- Track exposure alongside on-road risk and other risk views
With these in place, underwriters will be able to build new pricing models based on this new data.
In the long term, the commercial insurer will be able to deliver pay how you drive (PHYD) and/or pay as you drive (PAYD)-based insurance products and be ready for the growing amount of connected data that new vehicles and 5G will deliver.
Managing Risk Throughout the Policy Lifecycle
To be more effective in helping policyholders reduce their risk and claims frequency, commercial auto insurers and their policyholders must have the ability to share an understanding of the policyholder’s risk profile and take the right preventative steps.
Currently, risk management is defined by the reactive approach to claims frequency, severity and/or by policyholder spend. With telematics data, risk management becomes more targeted. It enables risk managers to identify, flag and support policyholders that are reactively managing their risk. From that point, the commercial auto insurer can set policyholder risk goals and deliver cross-book benchmarking best practice programs to their policyholders.
This creates an ecosystem of risk management services that are all informed and triggered by the policyholder’s data. For example, if there is a slight upswing in speeding across the fleet, the drivers responsible can automatically receive messaging about speeding and an invitation to complete fleet driver training on the dangers and issues of this driving behavior.
Pro Tips for Better Risk Targeting
- Give risk management teams clear insights into which policyholders they should be focusing on each week and why
- Give policyholders the visibility to see how their risk profile is viewed and what they can do to improve
- Give both policyholders and risk management teams the ability to drill down to the root cause of why certain risk is increasing
Pro Tips for Identifying Policyholders’ Downward Trends
- Track the normalized risk trend of each book of business as well as individual policyholder risk trends
- Benchmark the policyholders’ risk against each other, normalized for mileage and driver volume variance
- Alert the risk management team of behaviors that show risk is beginning to deteriorate for a policyholder
Improving FNOL Management and Liability Decision-Making
For claims, there are many benefits of using telematics data, as it enables the transition of the claims process from being reactive and hindsight-based to proactive and insight-driven.
With this, the whole process moves away from being initiated by and reliant on the policyholder notifying the claims function of an incident. Instead, reporting is automated and sent directly to the claims team, enabling fast and proactive FNOL management and improvements in liability decision-making due thanks to a greater depth of and easier access to pertinent information.
Pro Tips for Improving the Claims Process
- Give policyholders the ability to report incidents digitally, with multiple pieces and types/formats of media
- Combine manually reported incident data with automatically gathered data
- Automatically generate more data points related to an FNOL incident
- Provide automated and precise crash detection direct to the policyholder and their insurer
- Give clear information about the vehicle location at the time of the incident claim, for fraud prevention
- Provide a depth of context around the claim including the driver, their behavior, the road state and the environmental conditions at the time of the incident
- Reconstruct the incident from the relevant telematics hardware
As a result, the claim process will be further optimized via this new position of foresight-driven claims management. This includes using big data and insight to improve in areas such as repairs, professional indemnity and fraud assessment capability.
Claims will also reach a cognitive state, enabling predictive claims management that delivers a straight-through process where everything is essentially mapped out ahead of time as a series of interlinked and interdependent scenarios.
Maximize the Value of Telematics Data
Beyond a decrease in claims, the use of telematics and other data across the three commercial auto insurance activities – underwriting, risk and claims – is leading to huge improvements in customer service, as everything becomes more tailored, more efficient and more transparent for everyone involved. Plus, there is a related reduction in the admin costs of commercial auto insurance, as everything becomes more streamlined and less time-consuming.
Data is the essential element of this transformation. By combining data, more informed decisions on underwriting, risk and claims can be made. It gives carriers a more optimal use of risk capital, more risk-reflective pricing and the ability to give risk management advice, programs and alerts.
This strategy moves the insurance model from being an annual process to one that will or can reflect the true situation at any point in time.
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