PROBLEM FOR
CAR INSURANCE COMPANIES
IN EUROPE
Commercial motor insurance has a loss ratio problem. In the insurance industry, a loss ratio is defined as the ratio of the total losses paid out by an insurance company on claims received (plus any adjustment expenses necessary to facilitate this) divided by the total earned premiums in the same period. Once again: the biggest cost drivers for an insurance company are coming from a high loss ratio caused by bad risk in their portfolio.
After a peak of 108.1% in 2009, motor insurers’ combined ratio fell every year until 2015, when it reached a low of 96.9%, before rising to 98.6% in 2016. Although low investment returns in a difficult financial environment were the main cause, the 2.2 % growth in the loss ratio (to 76.5% in 2016) also contributed to the deterioration in underwriting results. But these chronic underwriting losses take place in an insurance market that has experienced strong growth.
Insurers have traditionally relied on motor vehicle records (MVRs) to understand specific driver behaviour, focusing on violations and driver eligibility. While those records are helpful, they don’t tell the whole story. Not everything on the MVR translates to losses, and there are losses that are not visible in MVRs. Today insurers can only assess their actual risks in retrospect, meaning after the financial loss for claims has already been incurred. This puts insurers in an unfortunate position, where their scope for action is severely restricted.
Good drivers are disadvantaged because they overpay for their insurance to balance out bad risks. Our solution enables insurance companies to identify their risks and categorise them. It’s possible to offer good-risk-attracting policies without losing money for claims and fill their portfolio with more profitable policyholders.
Therefore, this solves the problem of high acquisition costs in the competitive insurance market. Attractive premiums attract good risks.
WHAT IF YOU CAN SELECT YOUR RISK EXACTLY?
What if you would know every single customer better than ever before and asses correctly their risk when ever you want?
BUILD A SCORE OUT OF DRIVING INFORMATION
T = Time
S = Score
DS = Driving Style
DL = Driving Location
T =S
(DS + DL)
We have decided to check if we can improve the assessment of how good the driver is. To achieve this we collect information on Driving Style + Driving Location over time and calculate the score on its basis.
As part of our scoring system we take into account the following information:
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Driver Behaviour
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Kilometers driven
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Insurance Data
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Average Road Risk Rate (how dangerous are the roads that the driver is mostly using)
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Does the driver follow unexpected non-marked roads?
Driving And Road Scoring
Personal Reliability Scoring
UNDERSTANDING SCORE AND RISK CORRELATION
We have found that drivers that score 80-100, entail a low risk of being involved in accidents.
But once the score gets lower, the car accident risk rate increases significantly.
A score between 60-80 increases your chances of getting into an accident by 137%, while if you score 0, the odds of an accident are 5.5 times higher.
PROFIT LOSS RATIO BY SCORE
So how does score translate into the Loss Ratio? Assuming an average driver falls in the 40-60 score range, the loss ratio of a 0- scoring driver is 57% higher than the average one, meaning that the insurer loses money. But with a score of 80-100, the insurer can afford to reduce the premium by even 50% and still remain profitable.
