The mass processing of personal data has resulted in a widening gulf between the cheapest and most expensive car and home insurance.
Obtaining insurance at a reasonable price should be a universal right, but certain groups of people are being increasingly priced out of buying cover altogether. Insurers are scrutinising credit scores, occupations and even grocery bills to decide premiums, thereby penalising the most vulnerable.
Those living in less affluent areas or consumers with occupations considered higher risk have been hit by a progressively stringent ‘postcode premium’. According to the Institute and Faculty of Actuaries (IFoA), this is already costing those in affected areas £500 more per annum than other motorists.
As the poorest families have been hardest hit by rising costs, the practice of insurers gathering ever more detailed information could lead to other customers suffering increased premiums in the future. James Daley, of Fairer Finance, a consumer campaign group, warns that particular groups of people are being increasingly disadvantaged.
Higher premiums dictated by algorithms
David Heath, of the IFoA, says the issue is worsening as insurers gather additional personal data about consumers, leading to more personalised premium pricing. He and other campaigners have urged the Government to make higher costs illegal for poorer people on the basis of discrimination. He proposes a new state-backed scheme that would spread risk across insurers and provide a minimum level of protection to those who have been unable to buy cover, or denied protection altogether.
Pricing for individual risk, he claims, has had both negative and positive consequences as the insurance industry has evolved. Although lower risk customers have benefited from lower premiums, vulnerable and low-income households are often regarded as high risk. These people are being offered higher premiums, which may be unaffordable, and in some cases, are being refused cover point blank, he says.
The disparity arises as families face paying hundreds more for gas and electricity due to the escalating energy crisis and suffer the effects of rising inflation on household bills.
The IFoA found that lower earners paid £300 more on average for their car insurance as a result of living in less desirable postcodes. The higher premiums that are charged are calculated by complex algorithms that assess risk based on aspects of lifestyles. These can result in higher spending as many opt to pay in smaller monthly payments that work out as more expensive in the long run. Using credit to pay monthly has caused the poorest to pay £200 more per annum.
The growing availability of data has enabled insurers’ algorithms to dig deeper to identify the perfect price for each individual risk. Yet today’s pricing models don’t just hit the poor and vulnerable. For instance, some insurers hike prices for customers after they have been involved in an accident that wasn’t their fault.
Protection for those on low incomes
The reality is that perfectly personalised pricing is never going to be achievable, because for every statistical assumption made, some customers will be disadvantaged.
Mr Daley believes boundaries should be drawn around what data insurers use to set their prices and suggests two courses of action. First, any factor not directly linked to the risk should not be included. For example, occupation is irrelevant for car or home insurance, unless the insured drives for a living.
Second, low income needs to be added to the list of protected characteristics and subject to the provisions of the Equality Act. This would mean that insurers would not be allowed to charge more to people who pay monthly nor load premiums for those who live in poorer postcodes. Although it will result in some individuals having to pay a little more for insurance cover, the outcome should be that everyone can gain access to insurance.
A spokesman for the Association of British Insurers said the industry aims to offer fair and affordable cover to as many people as possible while balancing prices with risk.