Advanced telematics systems that track granular driving behaviour – paired with artificial intelligence algorithms that can predict user-specific risk from big data – have the potential to change the auto-insurance space. Though the US was by far the largest market in the initial years of adoption (McKinsey, 2018), many other countries have caught up as auto-insurers have recognised the value of big data and begun offering consumers incentives to install physical telematics devices in their cars, or apps that track granular acceleration, velocity, and geolocation data on their phones.
As one of the few insurers that offer a telematics option in Germany, HUK-Coburg — Germany’s largest auto insurer — hopes to capture value by adjusting premiums based on behavioral data. To incentivise drivers to download their telematics app, they offer up to a 30% discount off future premiums for good driving behaviour. Granular telematics data has the potential to create value for the company in several ways – first, safe drivers receive lower premiums, which encourages them to continue safe driving practices. Existing literature suggests that when telematics is offered, there is both selection into monitoring, and a positive effect of monitoring on driving behaviour (Jin and Vasserman, 2021). Telematics provides extra monitoring, reducing the risk of moral hazard and therefore the value of insurance claims. The value of telematics data can go beyond its effects on driver safety – a person’s data is useful for figuring out their “type”, imparting useful information on how to price future premiums. Moreover, there may be spillovers – e.g., knowing the driving behaviour of one particular driver might tell insurers something about who was at fault in an accident, and the driving behaviour of others similar to them (e.g., through collaborative filtering ML models), even if they had not opted into telematics. In that sense, their data can also potentially help HUK-Coburg determine how to price the premiums of those who did not opt into telematics.
However, there are several challenges that come with structuring and pricing optimal incentive contracts, especially when customers may face several behavioural biases that might affect their choices of whether or not they opt-in (or drop-out of) telematics, as well as their driving behaviour. In a country like Germany where privacy concerns are paramount, consumers may be reluctant to opt into the app even if the monetary benefits are high. Moreover, HUK-Coburg currently observes that those with lower predicted driving scores are more likely to drop out of telematics: this could potentially be from the disutility customers get from seeing that they are bad drivers, or the fact that they might update their beliefs downwards on whether on not they will get a bonus (therefore giving up data becomes less worth it relative to the costs of privacy concerns, or other demand frictions). Drivers may mispredict their own future driving behaviour, e.g., due to illusory bias (Horswill et al., 2004), potentially leading to higher opt-in than expected at the time of contracting but contributing to high attrition rates. Understanding why drivers drop out would be important towards the long term generation and renewal of telematics data as an asset.
Additional opportunities are also rife in this space. HUK could look at the corpus of granular data (trips, distance, driving hours, velocity/acceleration data, geo-location and associated road limits, accidents, etc.), and to predict what might actually be causing accidents that lead to high insurance claims. This would help HUK-Coburg refine what type of driving behaviour to specifically target, and to improve targeting alerts to drivers, as well as messages that help improve fuel consumption. Previous work has looked at the effects of warning text messages on handheld phone use behaviour (Jin, 2023), but there are many other behaviours to target. Moreover, there is a dynamic tension between limited attention (if alerts pop up frequently, drivers unlikely to respond to all of them) versus the costs of unsafe driving. How should HUK optimise and personalise messaging to drivers? There are many opportunities to improve outcomes for both drivers and HUK-Coburg here.
Lastly, there are many other ways to potentially structure this contract that could capture more value for HUK: for example, they could offer an even lower base premium and penalise bad drivers. Or one could reclassify customers in future periods, awarding the equivalent of both bonuses and/or maluses without making their counterfactual premium explicit. Behavioural economists have often proposed that loss aversion can incentivise higher productivity or other desired behaviour – e.g., if drivers are more averse to getting a penalty than when presented with a similar bonus, they might be more predisposed to drive safer. However, previous work has also found that in certain contexts, “loss framing” incentives can lead to “gaming” behaviours that lead to negative consequences (Pierce et al., 2022). In this case, loss aversion may help on the intensive margin – getting drivers to choose safer driving behaviour contingent on opting into telematics – but may lead customers to opt out of monitoring on the extensive margin, perhaps forgoing telematics altogether. HUK may have to consider other biases that are relevant in the setting of insurance contracts (see Baiker et al., 2015), including present-bias, where customers may value a high discount now whilst discounting potential future losses. There exists a tension here: whilst a firm would want to set a low base premium to attract a wider set of customers – especially if they face present-bias – loss aversion might drive customers away from accepting a plan with penalties. Any incentive structure that involves a malus will have to take into account these competing effects.
HUK-Coburg’s strategic use of telematics data and behavioral insurance principles positions them as a leader in the auto-insurance industry. By continually investing in technology, addressing consumer concerns, and adapting to regulatory changes, they are well-positioned to capitalise on the opportunities presented by data analytics.