Claims, Risk, or Underwriting – which commercial fleet insurance department benefits the most from telematics data?

Commercial fleet insurers have faced profitability challenges through a period of poor results, caused by increases in both loss frequency and severity.

This has led to a need for better approaches to assessing risk potential, and the growing acceptance that using telematics data can help address these issues, which in turn will help restore profit margins.

Within the commercial fleet insurance operation, the impact of using telematics data is definitely helping but in different ways.

Overall, it can be said to be helping underwriters over the longer term, whereas the gains for the claims and risk departments have been found in the short term.

Busy road junction with telematics data flows overlaid to illustrate how data can help commercial fleet insurers

How telematics data helps commercial fleet insurers.

Commercial fleet insurers spend millions processing claims and writing policies.

Simply reducing the number of claims will lower their cost of business. Not to mention the costs of underwriting and risk management.

To achieve this, the use of telematics data is taking commercial fleet 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 windscreen.

As an example, risk management is currently a reactive model based on the client’s claims frequency and severity.

With the correct use of data, this will become more targeted and tailored to each client’s fleet, enabling the commercial fleet insurer to create and offer a portfolio of supporting risk management services, all based on and triggered by the client’s telematics and related data.

These in turn will help the client address their risk factors to lower their claims frequency and severity. Leading to cost savings for the commercial fleet insurer, lower premiums for the client, and a stronger partnership-based business relationship for the two organisations, which will aid customer retention for the commercial fleet insurer.

General foundations for better use of telematics data

When embracing the use of telematics data, commercial fleet insurers should aim to achieve these three core foundations:

  1. Create a low touch, data sharing process for the telematics data for their customers.
  2. Standardise all telematics and other related data sources into one central data structure.
  3. Enable claims data and other data sources to be easily uploaded and incorporated with the telematics data.

If these are delivered then the specific benefits for claims, risk and underwriting can be realised.

Claims – Improving FNOL management, and liability decision making

For Claims, there are key short or near term benefits of using telematics data. Enabling the transition of the claims process from being reactive and hindsight based to being proactive and insight-driven.

With this, the whole process moves from being initiated by, and reliant on, the client notifying the claims function of an incident, to the subsequent manual collection of the incident information. To a state of automated reporting direct to the claims team, enabling their fast and proactive FNOL management and their resulting improvements in liability decision making due to a greater depth and easier access to the pertinent information.

Steps toward this evolution include:

  • Giving clients the ability to report incidents digitally, with multiple pieces and types/formats of media.
  • Combining manually reported incident data with automatically gathered data
  • Automatically generating more and more data points related to an FNOL incident.
  • Providing automated and precise crash detection direct to the client and their insurer
  • Giving clear information about the vehicle location at the time of the incident claim, for fraud prevention.
  • Providing a depth of context around the claim including the driver, their behaviour, the road state, and the environmental conditions at the time of the incident.
  • Reconstructing the incident from the relevant telematics hardware.

Over the medium term, the claim process will be further optimised by getting to a 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.

Into the long term, claims will 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.

Risk – more targeted and able to spot those clients going in the wrong direction

The delivery here is to be more effective in helping clients reduce their risk, and ultimately, their claims frequency.

This is enabled by both the commercial fleet insurer and their client being better able to share an understanding of the client’s risk profile, and from this, take the right preventative steps.

Currently, risk management is defined by the reactive approach to claims frequency, severity and/or by client spend

With telematics data, the short term goal is to move to the proactive insight-driven stage, which will make risk management more targeted.  It will also enable risk managers to identify, flag and support those clients going backwards in their risk management.

That is, support clients with data derived risk management improvement programs

From that point, the commercial fleet insurer can set client risk goals, and deliver cross book benchmarking best practice programs, to their clients.

Finally, the long term goal will be a move to the predictive model, and the creation of an ecosystem of risk management services that are all informed and triggered by the client’s data.

For example, there is a slight upswing in speeding across the fleet, this is picked up and those drivers responsible automatically receive messaging about speeding and an invitation to attend a CBT course on the dangers and issues of speeding.

Actions to take towards this evolution include, for better risk targeting:

  • Give risk management teams clear insights into which clients they should be focusing on each week and why.
  • Give clients the visibility to see how their risk profile is viewed and what they can do to improve.
  • Give both clients and risk management teams the ability to drill down to the root cause of why a risk is increasing.

And for identifying clients going in the wrong direction

  • Track the normalised risk trend of each book of business as well as individual client risk trends.
  • Benchmark the client’s risk against each other, normalised for mileage and driver volume variance
  • Alert the risk management team to behaviours that show risk is beginning to deteriorate for a client.

Underwriting – establish client risk pre-quote and improve visibility of the risk

As with claims and risk management, underwriting is currently, and predominantly in the hindsight driven mode, with traditional underwriting models being based on prior experience and broad insight.

With the use of telematics data, the short term goal of underwriting is to use a prospective client’s existing telematics data history (typically the last 90 days), to establish a more personalised understanding of their risk, pre-quote.

Doing this moves the client 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 not only means the client gets a more accurate quote, but the commercial fleet insurer is also getting improved visibility of the detail behind the risk they are underwriting before committing to taking it on.

Ideally, as the relationship with the client moves forward, with this flow of data, the underwriting team gets a more granular understanding of each of their clients, without disproportionately more work, at all stages of the client lifecycle.

Actions that need to be taken to establish client risk, pre-quote, are:

  • Give underwriters the ability to upload up to 90 days’ worth of telematics data and benchmark that against existing clients.
  • 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.

And those for improving the visibility of the risk

  • Get clear information about how many vehicles a client has active on the road and alerts if that volume goes up.
  • Access a completely standardised raw data set about the client to review and begin building into the modelling.
  • Track claims exposure risk alongside on road risk and other risk views.

With these in place, over the medium term underwriters will be able to build new pricing models based on this new data and work quarterly restrikes of premiums based on the risk trend

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 the new vehicles and 5G will deliver.

Summary and conclusions

The use of telematics and other data across the three commercial fleet insurance activities – claims, risk, and underwriting – is leading to huge improvements in customer service, as everything becomes more tailored, more efficient, and more transparent to everyone involved, compared to the past.

Plus, there is a related reduction in the admin costs of commercial fleet insurance as everything becomes more streamlined and less time-consuming.

Data is the lynchpin in this transformation.

By combining data, more informed decisions on risk, claims and underwriting can be made. Meaning there will be the more optimal use of risk capital, more risk reflective pricing and the ability to give risk management advice, programs, and alerts.

Over time, this will move the insurance model from being an annual process to one that will or can reflect the true situation at any point in time.

Clara can do all this for Commercial Fleet Insurers

While all of the above is possible, the aggregating, standardising, and normalising of the myriad data sets is a major task in itself.

That is why we built Clara, as it does all the heavy lifting for commercial fleet insurers.

Giving users the information they need, filtered from the data noise, to both act rapidly when incidents happen and to develop risk management programs to reduce future incident frequency

Clara is an incredibly powerful piece of software that can be used within a commercial fleet insurers risk management, claims and underwriting functions, to support all of the use cases detailed above.

Ultimately, Clara enables a commercial fleet insurer to enhance its digital proposition whilst reducing risk, lowering claim costs, improving risk selection, and increasing profitability.

To see how Clara does this, please request your free Clara demo here.