RISK PRICING GUIDELINES FOR PAY-AS-YOU-DRIVE VEHICLE INSURANCE PRODUCTS

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1 M E T H O D O L O G Y D O C U M E N T Prepared by: Andras Kovacs, Date: October 16. RISK PRICING GUIDELINES FOR PAY-AS-YOU-DRIVE VEHICLE INSURANCE PRODUCTS w w w. i r i s - g l o b a l. o r g

2 Introduction The trend for personalized insurance Insurance personalization is a growing trend. An attractive product for the low-risk market segment is only possible by some means of differentiating low-risk versus high-risk insurance clients. Vehicle insurance industry has so far been using averaged assumptions about its clients. Traditional vehicle insurance risk management has three main shortcomings: 1. Metrics on which risk assumptions are based are not always verifiable. For example vehicle insurance rates depend on the registered place of driver s residence - the closest proxy for location of driving. Consequently, many drivers are insuring their vehicle in the name of some relative, who lives in a low risk rural area. 2. Accident history based differentiation is retrospective pricing, which may differ significantly from actual accident risk. In other words, current insurance pricing does not distinguish between good drivers and those risky drivers who simply got lucky so far. This shortcoming is especially significant with young drivers, where retrospective approach is least accurate, and with fleets, where it would be the mutual interest of insurers and fleet owners to identify risky drivers. 3. There is no reliable trace of accident circumstances. Consequently, some percentage of accident claims is fraudulent. On the aggregate, vehicle insurance industry is neutral on above shortcomings as long as it can solve these inefficiencies through averaging of costs - essentially a cross-subsidy from low-risk to high-risk drivers. By addressing above shortcomings, there is clearly an opportunity for a new player in the insurance market to gain market share through better insurance products for low-risk drivers. Some insurance companies are starting to use telematics for personalized risk management, deploying so-called Payas-you-drive insurance products. This is illustrated on the map below. Current personalized insurance deployments are however just a tip of the iceberg, as required technology has so far been prohibitively expensive for most insurance business models. True mass-market uptake of personalized vehicle insurance will reward those insurers, who efficiently address following issues: 1. Definition of insurance products, which truly exploit personalization demand and fraud detection issues - going beyond simple per-mile pricing. 2. Utilization of more cost-efficient technology than what has been experimented with so far. 1

3 Market demand for Pay-as-you-drive vehicle insurance Demand for an innovative personalized vehicle insurance product is coming from following sources: Low-risk drivers switching to our insurer for the discounts: The most profitable low-risk drivers will migrate to our Pay-as-you-drive insurance to receive the insurance discounts they deserve. Some percentage of drivers with following characteristics are expected to join:! drivers who avoid driving late nights hours.! drivers who drive less than average mileage! drivers who avoid sudden braking, and drive foresighted Low-risk drivers who have been waiting on the sidelines for the proper Casco insurance offer: Only 23% of Eastern European drivers purchase voluntary Casco insurance. This means that 77% of drivers are waiting for a more affordable insurance offer. The low-risk segment of these drivers - with above described characteristics - shall find a suitable offer via our Pay-as-you-drive product. Even a small percentage of these drivers translates into a significant market share! Aversion of fraudsters: Through the use of accelerometer within our on-board unit, an accurate analysis of the accident becomes possible. This in turn accurately identifies guilty parties and removes as significant percentage of fraud - resulting in lower product pricing for all other clients. Corporate fleets: Corporate fleet owners are interested in a product, which actively manages the risk of their fleet, resulting in less accidents. Offered personalized insurance product comes bundles with two types of services facilitating fleet managers goal:! Identification of most risky drivers (who are braking hardest, doing most over-speeding,...)! Identification of drivers, who brake some fleet safety restriction (e.g. driving outside of geo-fence area, driving during late night hours) The technology, which enables better insurance pricing for lower risk also enables fleet manager to lower risk via identification and re-education of risky drivers. 2

4 Pricing of insurance personalization This chapter justifies anticipated risk differentiation effect of personalized insurance. Applied bonus percentage calculations are relative to the average claim amount - i.e. the discount relative to current average policy price depends on claims pay-out ratio. Personalization effect is comprised of following components:!personalized risk accounting!removal of unjustified base rate biases!improved fraud detection!proportional personalized re-insurance!reduced sales costs for a unique product Personalized risk accounting: The most significant risk input parameter is the amount of mileage. While individual driver risk is proportional to driven mileage, the average is not, because different drivers have varying per-mile risk. This is illustrated on the following figure. Distance-dependence of individual motorist claims, and its averaging (from [1]) Drivers with higher annual total mileage tend to have a lower per-mile risk than lower mileage drivers. The quantification of this effect and the resulting driven distance dependency of claims is shown on the following figure. Average annual claims costs versus annual driven mileage (from [1]) 3

5 It is concluded from accident research that mileage accounting can personalize about 60% of claims costs. For discount pricing we need to look at differentiation around the value belonging to average mileage, which has been used as base rate so far. As following distribution shows, the annual mileage is averaged at km. The corresponding average cost value means that mileage personalization accounts +/- 45% claims costs from average, or in other words driven distance based bonus should be up to 45% of average claims cost. Distribution of annual driven mileage (from [1]) Risk of crash accidents can be personalized further by taking into account the effects of night driving, over-speeding, geographic area, and foresighted braking. Each of these aspects is analyzed below. Accidents during night hours (20:00-4:00) account for 26% of all accidents, while traffic during these night hours is only 11% of total mileage. Therefore if night-driving were avoided and corresponding mileage transferred to regular hours, the total accident rate would be 1.11"74%=82% of total, meaning an 18% bonus potential. In terms of per mile risk the nighttime to daytime per-mile risk ratio is 26/11 to 74/89, which equals approximately 3 times the daytime risk. Night-driving aversion bonus should be coupled in at 18%, and per-mile price of night hours should be 3 times the daytime per-mile pricing. Accident intensity Traffic intensity (from [2]) and corresponding accident intensity Over-speeding is the accident cause at about 20% of crash accidents. However this average statistics hides the very strong variation of over-speeding related accidents by age groups. The following statistics shows the percentage of drivers among fatal crash victims, whose accident was related to over-speeding. This also corresponds to accidents with high damage claim rate. 4

6 Percentage of over-speeding drivers among those who suffered fatal crashes (from [3]). Therefore the over-speeding aversion bonus should be about 30% for under-20 age group, and gradually reduced to 5% for over-75 age group. There is no clear statistics to guide how strongly should each over-speeding incident be penalized. The personalized insurance product prepared by VGW insurance adjusts insurance rate back to average after 12 over-speeding incidents per year. Insurance price-differentiation by geographic area has so far lacked the accounting of actual driving location and the accounting of annual mileage variations. Therefore the recommendation is to remove base price differentiation by geographic area, and instead distinguish per-mile pricing by geographic area of actual driving location. The variation of fair per-mile pricing by settlement size is shown on the following figure. Furthermore, inter-urban highways should be a separate category accounted at lowest per-mile rate. As conclusion, driven location based per-mile price differentiation should vary +/- 25%. When the current postal-code based accounting is corrected to a rational location price accounting, we can expect by conservative estimate that some client segment gains 10% bonus from elimination of unfair location accounting. Graph of crash accident costs per mile versus settlement size (from [4]) The effect of differentiation through accelerometer analysis is the least researched aspect so far. The following figure shows results from the study of a fleet of 200 vehicles. After two months of observation drivers were given feedback on 5

7 their amount of dangerous braking, risky lane changing, and sharp turning. All these observable acceleration metrics fell permanently by 50% in the course of the study, and corresponding accident rate for the year fell by 20%. Observed risky acceleration events on the studied fleet. Drivers receive feedback after two months of observation (from [5]) As a conservative estimate, bonus for aversion of dangerous braking and turning should be coupled in at 15%, and per-mile price of driving should be gradually increased after observed acceleration events surpass observed 2007 levels on above figure. In summary, low-risk young drivers - who get maximum discount in all above categories - are expected to cause only 0.55 " 0.82 " 0.7 " 0.9 " 0.85 " 100% = 24% of average annual average crash-related claims in their bonus category equivalent. Low-risk old drivers are expected to cause only 0.55 " 0.82 " 0.95 " 0.9 " 0.85 " 100% = 33% of average annual average crash-related claims in their bonus category equivalent. The final achievable crash-related discount depends on the claims pay-out ratio and percentage of fraudulent cases. Taking the Eastern/Southern European example of 15% fraudulent claims, and 60% claims payout rate, the achievable low-risk rate of discounts in comparison to current pricing should be (100-24) " 0.85 " 0.6 % = 39% for young drivers and (100-33) " 0.85 " 0.6 % = 34% for old drivers. Taking the Scandinavian example of very few fraudulent claims, and 60% claims payout rate, the achievable low-risk rate of discounts in comparison to current pricing should be (100-24) " 0.6 % = 45% for young drivers and (100-33) " 0.6 % = 40% for old drivers. Research in [1] shows that theft/vandalism damages depend more strongly on driven mileage than crash damages. As the following diagram shows, driving exposes vehicle to these dangers more evenly with distance. Taking damage value corresponding to average distance ( km/year), it is seen that mileage personalization accounts +/- 60% claims costs from average, or in other words driven distance based bonus should be up to 60% of average theft/ vandalism claims cost. It is usual for vehicle insurers to give furthermore 5-10% discount on theft insurance for those who have tracking capable equipment in their car, because it helps with theft deterring and recovery. Geographic 6

8 differentiation of theft/vandalism can be applied by same claim location metric as it is currently done - but it will be accounted more accurately through actual vehicle position. Average annual claims costs versus annual driven mileage (from [1]) The final achievable theft/vandalism-related discount depends on the claims pay-out ratio as well. Taking the example of 60% claims payout rate, the achievable low-risk rate of discounts in comparison to current pricing without satellite tracker should be (100-60) " 0.6 % + 5% = 29%. Removal of unjustified base rate biases: The previous section has explained that for crash risk personalization the geographic differentiation should be applied as a dynamically accounted per-mile pricing factor instead of a fixed base rate difference. The effect of this correction on low-risk segment was also estimated already. Also, it was described how theft/vandalism-related cover policy should account the actual daily vehicle location instead of some registered fixed location. An important unjustified bias researched in [1] is the gender differentiation. It turns out that current male/female price differentiation is simply an average accounting of the fact that males tend to drive more mileage and do more overspeeding than females in same age group. Such bias no longer required, as related more exact personalization was described in previous section. Those low-risk young males, who have been so far cross-subsidizing others can gain at least 10% discount on expected crash-claims through the removal of this bias. Similarly, the base rate of low-risk females will be increased by similar rate before personalization. Improved fraud detection: Crash-risk fraud stems from two main sources. First, some people simulate crashes. The employed accelerometer logging of our proposed solution helps to detect when crash severity or direction is not in line with reported vehicle damage. Second, the guilty party is sometimes swapped when crashed vehicles have large price differential. Again, employed accelerometer and position logging helps to uncover the true culpable party of the accident. Insurance experts estimate that 15% of crash claims in Southern/Eastern Europe involve one of above frauds. At conservative estimate, the employed personalization technology uncovers a third of these attempts. At 60% claims payout rate, this creates a 0.33 " 0.6 " 15% = 3% discount possibility for all clients. 7

9 Proportional personalized re-insurance: As some of the insurance risk is expected to be traded with a re-insurance company, the issue of dynamic insurance personalization comes up during re-insurance contract definition. We recommend PartnerRe as reinsurance agent, as they are willing to accept insurers risk personalization products. Reinsurance costs consequently remain same percentage of total revenue as in case of traditional vehicle insurance products. Reduced sales costs for a unique product: Middlemen broker costs of traditional insurance are generally at 10% of total yearly revenue. As current vehicle insurance products are very similar to one another, insurance brokers and vehicle salesmen gain the upper hand in guiding customer decisions. As proposed vehicle insurance personalization is a unique product in the insurance marketplace, it will garner more direct interest from benefiting driver segment. Therefore sales agents costs shall be considerably lower, and are expected at 5% of total yearly revenue. This enables the possibility for a further 5% pricing discount. Summary of insurance pricing effects: Summing up described pricing effects of vehicle insurance personalization, the following pricing discounts are achievable per vehicle insurance type, relative to traditional insurance products: Crash risk insurance 1 :!Lowest risk young male drivers: 57% - 60%!Lowest risk young female drivers: 37% - 40%!Lowest risk old drivers: 42% - 45%!Drivers who turn out to be representing average risk: 8% - 5%!Average customer of proposed personalized insurance: 26% Theft/vandalism risk insurance:!lowest risk drivers: 34%!Drivers who turn out to be representing average risk: 10%!Average customer of proposed personalized insurance: 22% This significant significant discount possibility for low-risk drivers can be achieved through the accurate risk assessment methodology of proposed solution. The cost-efficient implementation provides quick return for low-risk drivers on their technology investment, and thereby ensures mass-market uptake for insurance personalization. References: [1] Victoria Transport Policy Institute: Distance-Based Vehicle Insurance Feasibility, Costs and Benefits 1 the value within indicated range depends on the amount o insurance fraud 8

10 [2] UK Transport Statistics Bulletin: Road Traffic Statistics (2001) [3] National Highway Traffic Safety Administration: Traffic Safety Facts (2005) [4] Cambridge Systematics: Crashes vs. Congestion What s the Cost to Society? [5] T-Mobile UK s fleet risk management trial data (2007) Related service bundling There several possible supplementary services, which can be provided in conjunction with insurance personalization. Whether these services are provided free of charge for the sake of customer retention or at a service fee, is a matter of insurer s product strategy. Fleet management support for corporate customers As described in the previous chapter, fleet managers may query vehicle utilization and filter risky drivers through a user-friendly web-based management interface. Stolen vehicle tracking If a personalized Casco client reports vehicle theft, stolen vehicle tracking will be turned on. This helps in locating and recovering the stolen vehicle, and also serves as deterrent for thieves when tracking capability is visibly marked. Vehicle theft protection Clients of personalized liability insurance purchase an on-board unit with capability to check for presence of preconfigured mobile phone before enabling ignition. This is an important client protection against vehicle theft and robbery. Support of mobile phone based navigator software Since proposed lower-priced unit also serves as a Bluetooth based GPS sensor, personalized liability insurance clients may use free mobile phone based navigation software - such as Nav4All or Google maps - without any GPS hardware requirements. Virtually all current phones support needed Bluetooth and Java capabilities. Insurance clients may save the price of a personal navigator device. 9

11 Odometer certification Resetting of the odometer before vehicle re-selling is one risk facing used-vehicle buyers in Eastern and Southern Europe. If a trusted odometer certification service were available, buyers would gladly pay some fee for the certificate of odometer reading integrity. Proposed insurance organization can provide such certificate for the duration of personalized insurance contract. In case of vehicles which are always insured through a personalized insurance product, such certificate provides a trustworthy information about odometer integrity. It is also anticipated that on-board unit costs would be governmentally subsidized in the future, in exchange for facilitation of mileage based vehicle tax collection or emergency calling services. That is however a future prospect still, with first related trial activities now underway. 10

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