Advice & Best Practice

Power and precision: Rebuilding risk models at a time of uncertainty

By Colin Wallace, director of insurance at TransUnion in the UK

Mirroring our national economic recovery post-pandemic, there remains a great deal of uncertainty when it comes to the insurance sector. Chief of all remains the volatility around consumers’ financial situations in the short and medium terms – something insurers will have to be cognisant of as new regulatory changes loom for the sector.

According to research in June for our Consumer Pulse study, which asked consumers about their financial situation, nearly half (48%) of UK households had recently experienced a change in income. This seems, in no small part, due to a number of key factors: the impact of being furloughed, mandatory pay cuts, redundancy, or on the flip side the recent receipt of benefits.

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However, what is clear is that this economic uncertainty makes judging credit risk more complex than ever for insurers. In reality, this could result in a situation where traditional data sources are routinely flagging customers as low risk when they are actually struggling with income shock or taking payment holidays elsewhere.

The same Consumer Pulse study, which has been tracking the financial impact of the pandemic for the last 18 months, found that one in five (20%) UK households say they expect to be unable to keep up with at least one of their current bills or loan payments.

Of those, 9% will be unable to cover car insurance costs, while 7% say that they will not be able to pay their home insurance. And with the courts having been shut, county court judgements (CCJs) may have been delayed, meaning some signs of risk may not be visible to insurers unless they are able to use additional data sources.

At the other end of the scale, some potential customers being flagged as high risk may have seen their fortunes improve due to decreased cost of living during lockdown. It was interesting to see 16% of consumers say that they have been able to pay down debt faster in recent months than they had previously.

This instability comes at a complicated time for the insurance sector. The implementation of impending price walking regulation from the Financial Conduct Authority is likely to make footprint growth more expensive, with stricter controls on incentives and price cuts offered to new versus existing customers. A data-led understanding of the quote pool will be key to identifying where there are opportunities for growth.

This and other ‘fair value’ considerations for instalment customers – such as making it easier to cancel auto-renewal on contracts – have the potential to reduce revenues unless offset elsewhere. Targeted risk selection may have a bigger role to play in ensuring new customers offer greater value to insurers in the long-term, and that potential problems are minimised by reducing the number of customers that will be likely to either default or consistently shop around.

There is also greater regulatory pressure to ensure affordability checks are performed on all instalment customers. Balancing this requirement alongside making the right commercial decisions means insurers will need to work harder to cater for credit histories that are improving or declining over time – calculating for example, whether customers are long-term vulnerable or have suffered only a short-term income shock.

Ultimately, the current regulatory picture, combined with Covid-19 uncertainty, makes it harder to offset the cost of attracting business. In response, insurers need to be rebuilding their risk models with greater power and precision as a priority. To do this, we believe it will be absolutely crucial for the sector to use precise data and insights to aid them in targeting customers appropriately and confidently offering more instalment finance options.

To find out more about how TransUnion can support insurers visit:

Colin Wallace is director of insurance at TransUnion in the UK

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