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The constant evolution of in-vehicle video technology is rapidly increasing the justification for video technology, especially in certain types of fleets

Early video—initial video solutions provided devices with relatively expensive and limited memory as well as high airtime transfer costs.  These were limiting factors around functionality as well as price.  Technology focused on identifying risky events and saving short clips that showed that behavior.  Value was received in terms of claims settlement (recording of crashes) and exoneration (from the recording of “truth”), and, to a lesser degree, in the identification and improvement of driver behavior and associated advances in operational considerations impacted by improved driving.  Challenges included the time required to review video clips or the added cost of paying someone else to review them for you.  Many insurers funded or co-funded video solutions for their insureds because of the clear claims settlement value.

Over the years, cameras have increased in capabilities and quality of recording and rich data transmission costs have decreased.  Continuous video recording is now more common.  Fleet management companies are aggressively adding video components to their offerings as fleets are demanding improved driver behavior and risk identification.

Modern video–There are two very significant relatively recent enhancements to commercially available video solutions that have altered the value proposition of video.  As well as receiving continued claims settlement value, insurers have increased justification for funding video solutions as these advanced capabilities create the expectation of a more effective loss prevention/driver behavior improvement solution for the fleet.  The two specific advancements referred to are as follows:

  • Instead of relying on human video review to determine the value of content in video recordings, there is an evolving focus on more sophistication applied to the automated interpretation of video.  With Mobileye-like capabilities to recognize objects and interpret them in conjunction with driving behavior, companies like Netradyne and NAuto are early leaders in this exciting field.  With more advanced AI, recognition of pedestrians, bicyclists, other vehicles, driver awareness, following distance, lane markers, stop signs, intersections, intersection lights, etc., video solutions are becoming more capable of automatically identifying the contextual risk of a driving situation, combine that with interpretation of actual driving behavior, and provide more relevant and actionable insight to fleets and drivers.
  • The second advancement in video technology relates to the capability of viewing the internal view of a vehicle at any time to see what is actually happening in the vehicle. This may be overkill for many types of vehicles where there is not a lot of “action” in the vehicle.  However for many application where passengers are being carried, this ability to view real-time video of the inside of the vehicle provides a very valuable view as to what the passenger’s experience is.  This is a critical component of customer attraction and retention. For passenger-carrying providers, the understanding of the customer’s experience is a valued competitive insight.


With recent advancements in video capabilities, fleets and insurers are demanding and receiving more sophistication in the driving behavior identification and correction component of their safety focus.  In addition, the ability to understand to, and react to, customer perceptions are becoming vital competitive advantages for the passenger-carrying fleet.

Accuscore Announces Specialized Separated Driver Risk Scoring for Insurers and Fleets

Accuscore, a provider of the unique driver scoring system that provides the most accurate and risk-predictive driver behavior analysis, now offers a separated scoring function, based on the same collected driving data, for insurers and fleets. While the fleet sees scoring that more purely reflects the drivers’ behavior, the insurer receives scoring that adds in the risk elements of the driven road for a more complete view of overall risk exposure.

San Diego, CA July 12, 2017 – Scoring can reflect not only factors that the driver controls, but also uncontrollable factors such as the nature of the driven route.  If risk is associated with changes in momentum, it is natural that the driver on a route with relatively more stopping, starting, and turning, will have the opportunity to have more negative driving events and; therefore, a worse score. This type of separated scoring is required to be able to utilize this type of insight as both an effective loss prevention tool for the fleet and a tool of significant underwriting insight to the commercial insurer.

While the commercial insurer would like to fully understand the risk of the total driving experience, including the driver’s behavior as well as the risk associated with the road, the fleet manager/safety manager wants good driving behavior to be reflected with a good score even on a more challenging road.  It is not fair for the driver to be penalized for factors beyond their control and yet the insurer benefits from the additional insight of the risk of the driven road.

Accuscore’s in-depth, second by second motion analysis, with multiple compensations applied to each second, achieves the sensitivity of scoring that allows us to isolate the “pure driving” score from the effects of varying road characteristics.

According to Alan Mann, President of Accuscore, “The actionable nature of driver risk scoring is associated with its value as a loss prevention tool as well as an indicator of risk exposure for underwriting purposes.  The fleet and the insurer have different needs in terms of scoring insight to fully monetize the value of driver scoring.  Accuscore uniquely delivers that optimized view of driving risk to both fleets and insurers.”

About Accuscore

Accuscore is a San Diego-based company specializing in more accurate driver behavior scoring.  Commercial insurers receive more risk predictive underwriting insight.  Fleets receive an effective loss prevention/driver behavior tool to identify and correct issues related to risky driving behaviors.

For more information, contact Alan Mann at amann@acculitx.com

Why Consumer UBI Approaches Just Don’t Cut it for Commercial Insurance

Plus, Key Questions to Ask Regarding Driver Scorecards

The use of UBI approaches to commercial insurance necessitate a very different approach than the typical consumer UBI that we are all familiar with. Commercial use of UBI technology needs to identify very different factors of driving risk to help achieve underwriting insight and provide the foundation for an effective loss prevention program. Commercial insurance applications have been slow on the uptake thus far because vendors have simply not delivered required value to commercial insurers. This value is clearly the predictive risk associated with driving behavior. This is the missing piece of underwriting insight for commercial insurers. Just how different are commercial and personal lines solutions?

Commercial vs. Personal Lines UBI

Unlike personal lines,

  • Commercial risk prediction is not aimed at delivering a “discount” to the opt-in (self-selected) participant. Value is delivered in terms of incremental underwriting insight for the insurer as well an effective loss prevention tool for the fleet. Potential financial participation of both the insurer and the fleet are enabled as the insurer receives underwriting insight and the fleet is enabled to implement an effective loss prevention program.
  • Commercial application typically involves mandated full participation by the driving population, allowing all fleet drivers to be compared to one another and all fleets to be compared, allowing the insurer effective comparisons of companies across their book of business and allowing fleets effective comparisons amongst all of their drivers.
  • Commercial application is more focused on highly accurate driver risk prediction for underwriting insight. Driver behavior is the most actionable incremental insight the insurer can use to accurately predict future risk.
  • Commercial insurance is more focused on higher value, higher premium vehicles, with much higher loss experiences, thus a more attractive and attainable ROI is associated with effective loss prevention programs and risk identification and ultimately, improvement in loss histories.
  • Commercial insurers desire device neutrality so insureds can use their customer’s chosen technology (telematics device, video camera, smartphone App) while still providing underwriting insight to insurers. The smartphone delivers the added value of being able to be delivered across the insurer’s entire book of business at an attractive price whereas individual devices will never provide a telematics view across all insureds.
  • Commercial insurance is more focused on loss prevention efforts of fleets, while there is little focus on driver improvement with personal lines solutions. Insurers may commonly incent/co-fund loss prevention initiatives of fleets since fleets and insurers benefit through improved fleet loss experiences. Motivation/incentive for driver improvement are driven by fleet safety as well as the insurer in commercial applications. In consumer applications, motivation is typically related to a discount in insurance cost, a status only a minority of drivers will even qualify for.
  • Commercial auto is a loss leader, increased volume does not move the profit dial, accurate identification/removal or appropriate pricing of worst risks does. Thus driver risk profiling and associated risk pricing are much more important to commercial insurers.
  • Just like personal lines, negative customer selection is a huge motivation to accurately identify fleet and driver risk exposure—let your worst risks go to your competition (or price them appropriately).

Achieving More Risk-Predictive Scoring

Understanding that the commercial fleet insurance space is far different than the consumer space and that the focus is much more clearly on the identification of driving risk, there are key questions that should be answered in regard to the ability of various solutions to deliver a higher degree of risk-predictive value associated with driving behavior.
The understanding of actual driver behavior is typically the missing piece in the insurer’s quest to underwrite future risk. Industry actuarial insight is available, specific customer loss histories are available, it is a fairly simple process (and typically already done within the insurer’s actuarial skills) to determine the “risk of the driven road,” i.e.—what is the risk impact of roads, road conditions, weather, time of day, geography, and inherent safety of the vehicle. The missing piece in the underwriting equation is the differentiator between similar types of insureds—namely, driving behavior calculated accurately.
Since most TSP/video/smartphone Apps that seek to monetize the value of telematics data do that on the basis of questionable logic associated with risk determination, the insurer should understand the real predictive value of risk associated with driving and what kind of data processing is required to deliver that improved data.


Key Questions to Ask Regarding Driver Scorecards

While all drivers are certainly not created equal, it may not be so obvious that all driver scorecards are also not created equal. There are very significant differences in scorecards which directly impact the actionable nature of the scoring and the predictive risk value that they offer. The world is in a rush to “monetize telematics value.” The value of telematics data is directly related to its ability to accurately predict risk, namely associated with driver behavior.

With all of the vast potential associated with the identification of driver risk through the interpretation of vehicle motion, it seems like the cornerstone of insurance telematics assessment ought to be the accuracy of the identification of risk. In reality, however, we find very little questioning or understanding of the accuracy of the typical driver dashboard.

This guide attempts to give a framework for understanding the accuracy of driver scoring solutions and aims to identify the type of scoring that insurers can count on to be most predictive of future driving risk, hence the optimal tool to use as an additional tool in underwriting operations, pre-renewal processes, and loss prevention programs.

Driver scorecards are available with virtually every solution from video providers as well as telematics suppliers (TSPs). These scores attempt to show risk associated with risky behavior predominantly identified from vehicle motion. These determinations are typically made either from accelerometer data or by calculating changes in speed/momentum over time. Customers wanted more insight into identifying and correcting driving risk. TSPs, typically relying on telematics devices provided by a small group of suppliers, used the ability of the device to set an arbitrary g-force level which allowed them to keep track of how many times a driver exceeded that g-force level. This became the foundation of the identification of a “risky event.” The threshold for the g-force level would often be set to record a “manageable/reportable” number of such events, again an arbitrary distinction based on expediency. From a safety perspective, it was concluded that the number of times a driver exceeded an arbitrary g-force momentum change was a good depiction of that driver’s risky driving.

When speaking of video solution providers, early market leaders relied on the marking of an erratic event (determined by a g-force level being exceeded) to be retrieved later and used as the basis of corrective driver feedback. They also sought to make a real time determination of what level of g-force threshold would determine a risky event and then commit a small video recording before and after the “triggering event” (the g-force level being exceeded to cause the event to be recorded). With declining costs, it became possible to continuously record video data, but the concept of marking relevant events is still pervasive, so the fleet has a tool to get at the most relevant video examples more efficiently.

So what’s wrong with this? While “good enough” or “better than nothing” scoring has been pervasive, it is not accurate enough to be relied upon to accurately predict future driving risk based on past performance. Applying better science to the interpretation of risk is not significantly more expensive, it is just better. Better science creates more predictive risk for insurers and the basis of a more accurate loss prevention/driver behavior improvement tool for the fleet.

When Evaluating Driver Scorecards and Their Accuracy, The Customer Should Ask the Provider of the Driver Scorecard the Following Questions:

1. How do you define a risky event? If the event is determined by exceeding a simple g-force or delta v/delta t calculation, the inherent inaccuracy is easy to define. Accelerometers or delta v/delta t calculations determine the relative change of vehicle momentum and correlate that to risk. accuscore-fleet-gps2The issue, however, is that it is very easy to achieve a significant change in relative momentum at low speed. At high speed, you can have a very significant braking event, for example, but the actual change in relative momentum is relatively small. This results in a preponderance of low speed events and very few high speed braking (for example) events. This is exactly the opposite as it should be since higher speed events are inherently riskier. And speed is only one of the variables that needs to be properly compensated for to achieve accurate driver risk scoring.

2. When your vendor speaks of a driver safety system, what is the focus of that program? Often vendors will quote improvements in speed management or the identification of non-driving (contextual) events as important to driver scoring. The essence of driver scoring should be the identification of risk brought into the picture specifically around how the driver drives. Correlating location information to contextual insight (what is the weather, time of day, road conditions, accident history of road, inherent safety of the vehicle, etc.) has value but these are all factors that the insurer has evolved internally over many years. From a safety perspective, there is very little that you can do to address the “risk of the driven road”. The vast majority of opportunity is tied to correcting weaknesses in driver behavior and that can only be done through the most accurate assessment of risk possible.

3. Does your vendor talk exclusively about g-forces or is it understood that changes in momentum are not necessarily significant unless understood in the context of the entire driving situation? The focus should be on energy displacement, not the exceeding of a pre-determined momentum change threshold. Energy displacement cannot be determined real-time at the device level; continuous “big” data must be interpreted on the back-end to apply all appropriate compensations to yield the most accurate risk scoring.

4. Does the solution identify severities of events? If you are just declaring a risky event to be correlated with the exceeding of an arbitrary threshold, you are basically then keeping a tally of how many times that threshold is exceeded and coming up with a driver risk score based on that. Your safety program does not exist below that arbitrary threshold and there is no segmentation of event severity above that arbitrary level. The problem is that there is a whole lot of difference between having an event that exceeds a certain threshold and the less frequent really REALLY dangerous event. You want to know the difference. In insurance speak, most driver dashboards give you a driver risk score on (poorly defined) frequency only; there is no ability to understand the true severity of any particular event. If you are not making appropriate compensations to data, there is no way of knowing the risk of an event based on a g-force reading. It is very possible to have a more dangerous event at .06 g-forces in comparison to another event at .08 g’s. Knowing both the frequency and severity of events is critical to determining risk and this only done by understanding the entire context of the driving situation.

5. Does the system identify your best drivers as well as your worst? Typical telematics solutions use the (flawed) calculation of driver risk but can at least generate a score for each driver. Many (especially video oriented) solutions focus on identifying the worst examples of risky driving and intervening aggressively with those drivers. The problem with that is that there is little reporting/feedback available to the masses for them to understand their behavior and make the subtler corrections that might improve their driving safety. Driver acceptance of the overall program is greatly enhanced if a preponderance of drivers actually receive feedback about how good they are. Good drivers also take pride in this aspect of their performance and make an even greater effort to improve their scoring. While it does not create as much movement in the total overall company risk, every bit of improved driving helps and is important in positive acceptance of the program in the fleet.

6. Specific compensations have to be made to data to create an accurate depiction of risk. Earlier, we mentioned that a change in vehicle momentum is only relevant if you know the exact speed of the vehicle exactly when that event happened. In similar fashion, it should be understood that a heavier vehicle takes longer to adjust to any given driving situation or requires more braking energy to stop in the same distance; that it’s kinetic mass delivers more destructive potential, etc. Therefore, the weight of the vehicle is needed to be able to make a determination of what safe driving looks like. The heavier the vehicle, the smaller the margin for error, so the scoring must be more critical of heavier vehicles going through the same circumstances at the same speed, with the same degree of momentum change, etc.

7. Does the system compensate for trip length? A bias toward better scoring will be associated with a long uneventful trip (2 hours on a freeway with good traffic flow) than with a shorter stop and go kind of trip. Is there a method of normalizing those two trip results so they can accurately be compared from a safety perspective? How do you do that; how is a favorable bias toward longer trips compensated for?

8. Does your scorecard take into account duration of the event? If you are “event based” (looking to declare an event real-time at the device level), there is no view of the overall risk associated with the longer duration event. Example would be a long braking event with no particular moment exceeding a threshold level. Second by second analysis will offer the potential of identifying the risk in that one second, interpreting the risk of the next contiguous second, determining if that is a continuation of the event, and adding the total risky segments to arrive an overall risk score. In an event-based system, at no one point in time did you necessarily exceed the pre-determined threshold level, hence this wouldn’t even show us as an event.

9. If you are relying on accelerometers to determine momentum change, are you sure those x, y, and z readings are accurate? A small distortion, or drift, in those readings can cause significant misinterpretation of data. How do you determine if accelerometer data is true?

10. In the interest of expediency, most driver scorecards and risk prediction programs rely on an “event” to be captured real time at the device level. More accuracy is achieved through evaluation of continuous data samples. Is your vendor delivering event summaries or is complete data fully evaluated on the backend? From a “change in momentum” point of view, even on second samples contain a lot of noise, therefore typically requiring sub-sampling within the one second. How does the solution balance the need for accurate driver risk prediction with an economic data approach?

11. Is your smartphone App really smart? While the potential use of smartphones is obvious for large scale commercial deployments, there are really only a handful of smartphone App providers that have the capability to effectively use smartphones to generate the data needed to do sophisticated scoring. These providers apply advanced artificial intelligence and signal processing to differentiate between motion related to the actual vehicle vs. motion of the smartphone within the vehicle. How is the determination of risky driving behavior/events made with the use of the App?


With an event-based approach to driver scoring, all momentum change conclusions are flawed to some degree. They may create a rough picture of driving risk, which is better than nothing, but you can do much better. For maximum accuracy of driver risk prediction, continuous data should be interpreted on the back end so appropriate compensations can be made allowing the risk associated with changes in vehicle momentum to be accurately determined. Compensations must be made to guarantee integrity of data and determine risk of momentum changes in the context of the entire driving situation.

Alan Mann is President of Accuscore, a San Diego-based company specializing in the accurate determination of driving risk through the application of superior science. More predictive driving risk enables commercial insurers to underwrite better while also enabling the fleet to implement a cost effective and efficient driver behavior improvement program.

Building the Perfect Usage-Based Insurance Mobile App

The ideal app would use all the sensors available in the phone for data collection, including GPS, gyro, heading, and accelerometer. Smartphones have not been capable of generating the quality of accelerometer data needed for accurate scoring, but that will soon change.

Insurance carriers already know that smartphone apps, email, and online chat lead to higher customer satisfaction. The rising popularity of smartphones has also led many insurance companies to build versions of their websites optimized for mobile browsers and to offer dedicated apps that allow users to get quotes, submit claims, and manage their policies. Mobile apps for usage-based insurance (UBI) add another element to a market that has previously relied on small devices plugged into onboard diagnostic ports. Smartphones have the capability of connecting tens of millions of drivers to UBI services.

Insurance companies are unveiling mobile apps that gauge driving behavior through smartphones to set insurance discounts and renewal strategies for motorists and fleets. There is huge momentum and excitement around the use of smartphones as enablers of information flow to be used in UBI and risk identification. The value of the smartphone, of course, is that virtually everyone has one, and the incremental air time to transmit data is a trivial expense to the user. Thus, smartphone apps represent a potentially free resource which can be used to improve insurance risk prediction, a significant cost departure from previous UBI programs.

UBI programs that rely on data from smartphones will be the rule rather than the exception in the not so distant future. However, the adoption challenge is two-fold: first, the accuracy of smartphone data must be on par with the same readings from a plug-in device; and secondly, the smartphone app data must be integrated with other data to yield the most accurate prediction of driver risk. Future smartphone apps will also be able to focus on improving driver performance in an effort to continually increase safety, reduce risk, and thereby reduce costs associated with collisions—and all this with superior accuracy. It’s a tricky technical challenge; but it can be done.

Current Approaches to UBI Data Collection

Currently insurance carriers have two options for gathering data for UBI. Most personal lines insurance companies are using a plug-in OBD device to capture driving data.  While this approach offers superior data collection quality for the most accurate underwriting, plug-in devices are costly to the insurer and inconvenient for the user. Increasingly, insurers are moving to a smartphone-based solution for their lower cost structure and ease of distribution; however, the data stream of the current crop of smartphone apps is rather poor compared to the plug-in device, so data quality is sacrificed.

Many telematics systems use speed to derive acceleration events by taking the second-by-second speed delta as an approximation for acceleration, using GPS as the speed input. A correct speed calculation requires that the speed information is accurate both in mph and in time. If either is slightly off, significant errors can occur. Challenges arrive in urban canyons, forests and tunnels where GPS signal erodes or disappears all together. Significant errors will occur on the speed data.

Most current smartphone apps are not collecting accelerometer data off the cell phone; the data is derived directly from the GPS feed.  Additionally, only using GPS to determine trip start and end requires the smartphone to frequently run the GPS to determine if a trip has begun. This causes significant battery drain, so the typical application will only be able to sample the GPS every few minutes and hence often miss trip starts. A lot of the data collection problems will go away when the smartphone apps collect accelerometer data off the phone and have persistent data collection. The ideal app would use all the sensors available in the phone for data collection including GPS, accelerometer, gyro, and heading.  A multi-sensor approach allows for minimal reliance on GPS for location determination.

Challenges of Current Mobile UBI Apps

Although insurers have reached important milestones in terms of mobile UBI apps, customer reviews are a good indicator of the challenges these pioneers continue to face. Poor data collection from current smartphone applications causes customer dissatisfaction, leading to contact with customer support departments to file complaints about miscalculated data that includes inaccurate trips, mileage, and events, which also adds to the overall cost burden.  The poor data also impedes underwriting efficiency.

Most Common Complaints about Current Apps

Most Common Complaints

Online reviews have a consistent mixed theme: customers like the idea of the app, and think the concept is great and like the look and feel, but complain about accuracy, lost trips and battery drain.

“I like the concept of the app, but I don’t like the fact that when I am driving the app is constantly searching for my location draining the battery. I have never had to keep my phone constantly on the charger. It would be nice if it didn’t drain my battery.”

“Good look and design, but doesn’t record all of my trips so I am missing out on points. Sometimes it works but 9 out of 10 trips are not being recorded.”

“I really like the app. It’s a great idea to give incentives to make people drive safer. My only issue is that the app crashes all the time. I’m lucky if I get my trip saved before it crashes.”

What are the Requirements of the Ideal Insurance Mobile App?

Chart Mobile

  • Accurate Data Collection: Trip start and stop times; miles driven; time of day; meaningful driving events; and data for driver behavior score. By leveraging the technology in the smartphone, the app would detect driving within seconds of beginning a trip. Setting the smartphone in motion triggers the smartphone accelerometer and the application starts recording the activity right away. When the activity ends, the app falls back in to hibernation.
  • Multi-sensor data evaluations: allows for minimal reliance on GPS.
  • Ability to detect both slow behavior changes and rapid changes, such as a crash.
  • Low Battery Consumption: Typical use would consume under 8% of smartphone battery on a daily basis.
  • Automatic Passenger Trip Cancellation: If app is running on both the driver and the passenger phones, back-end will cancel trip gathered most likely by passenger.
  • Automatic filtering for anomalous in-vehicle device movement: Detects when the driver is moving the smartphone vs. movement of the vehicle, and able to generate viable accelerometer data even from a non-fixed mount phone.
  • Commercial: Ability to automatically assign trips to company account and personal account according to on-duty schedule and allow further on/off shift adjustments.
  • Continuous Driver Behavior Monitoring: Not simply event-based data collection.
  • Optional Behavior Based Coaching: Implement targeted driver improvement programs and address issues quickly. Identify other patterns of poor driving, not just events.

How Can the Ideal App Be Used?

Telematics-based insurance can finally gain traction, because instead of requiring physical devices that must be manually installed in a car, the data required can now be collected through mobile-phone apps that are much easier to download and install. Customers in every market are migrating to smartphone apps including personal lines insurance, commercial insurance, and fleet management.

Who Will Benefit?

Personal Lines: As a replacement for plug-in devices.

Commercial Lines: Paradigm shift for commercial underwriting because the driver behavior score can be summarized to company level along with total miles driven and therefore contribute in the underwriting process.

Fleet Level: Even without insurance participation, a fleet manager can use the app to monitor and improve driver behavior at a very low cost.


Mobile devices and smartphone apps are leading-edge tools that most people own and are the key to big scalability for insurers’ UBI programs. As data collection using smartphones improves, insurers will be better able to align their strategy with the smartphone as the primary data collection point making it the superior choice. The most successful mobile UBI apps will be those that integrate multiple smartphone sensors, profile a driver’s risk to the most accurate possible level, and make the overall experience better for the customer. Penetration will keep up with evolving technology as limitations are overcome. Superior science applied to more accurate prediction of risk would decrease collisions by identifying which drivers are most likely to be in an at-fault collision. Superior technology applied to data collection via smartphones would increase customer loyalty.

In the interest of an expedient solution, insurance carriers have been delivering deficient scoring of driver risk and settling for good enough. With accurate data collection, the ideal app will have the ability to generate traditional UBI information including trip start, trip end, miles and time of day. Furthermore, due to highly accurate accelerometer data, the app could also collect driver behavior throughout the trip in continuous mode and move away from the simple event-based systems to a more meaningful understanding of driving trends and habits. A continuous monitoring system would greatly improve the driver behavior assessment and hence risk assessment of each mile driven. Smartphones have not been capable of generating the quality of accelerometer data needed for accurate scoring, but that will soon change.

(Image credit: Dollar Photo Club.)

Posted on: https://iireporter.com/building-the-perfect-usage-based-insurance-mobile-app/