Data Sharing Could Boost Competition and Lower Premiums in Italian Auto Insurance

Data Sharing and Competition in Auto Insurance

Efforts to reduce information asymmetries across firms are becoming more prominent in Europe’s digital regulatory agenda. In auto insurance, policies that expand access to consumer risk information are intended to increase competition by reducing the informational advantages of dominant firms. But they also raise questions about firms’ incentives to innovate, consumer welfare and privacy, and overall market efficiency.

A study of the Italian auto insurance market examines how differences in insurers’ risk-rating precision, cost structures, and product differentiation shape pricing and targeting strategies. The market offers a useful case because insurance is mandatory, insurers cannot reject consumers, and contracts are relatively standardised, which makes pricing especially important. Although Italy has a public bonus-malus system with 18 risk classes, its limited granularity means insurers rely heavily on private risk scores.

The researchers estimate a structural model of imperfect competition and find substantial differences across firms. Insurers with more precise risk-rating models tend to attract lower-risk customers, while less precise firms end up serving riskier pools. This pattern is consistent with cream-skimming, in which better-informed firms use their informational advantage to target safer drivers.

Counterfactual Scenarios

The analysis evaluates three scenarios: full transparency, in which firms observe consumers’ true risk types; a centralised risk bureau; and a privacy-oriented regime that limits firms to basic public information.

When all firms have full knowledge of consumers’ true risk types, consumer surplus rises by 16.9%. A centralised risk bureau produces a nearly similar gain of 15.7%, while the privacy-oriented regime delivers a more modest increase of 3.6%.

These gains are driven by lower premiums. Under full transparency and the centralised-bureau scenario, premiums fall by between 21.6% and 25.7% as firms compete more directly when they share more accurate information.

Winners and Losers

The effects are uneven across consumers. Low-risk consumers benefit the most when insurers have access to detailed or shared risk data. Under a centralised risk bureau, their surplus rises by more than 78%, while high-risk consumers see a reduction. With full information, that gap becomes even larger because firms can price more precisely by risk type. Under privacy regulation, by contrast, high-risk consumers benefit from more uniform pricing because firms have less ability to differentiate.

Firm profits also decline on average, but the losses and gains are distributed unevenly. Less informed firms benefit from data-sharing because they gain access to information that had previously been unavailable to them. The most informed firms suffer the largest losses as their informational advantage is eroded.

Matching and Efficiency

Information policies affect not only prices but also how consumers are allocated across insurers. When firms have access to full or centralised risk information, they can better identify the consumers they are most efficient at serving. That leads to stronger sorting, with some firms concentrating on low-risk drivers and others attracting higher-risk customers. Under privacy regulation, that sorting becomes flatter and consumers are distributed more evenly, but less efficiently, across firms.

This stronger sorting drives efficiency gains. Perfect information reduces average costs by 3.7%, or about €32 per contract per year. A centralised risk bureau achieves roughly 40% of those gains, or around €12 per contract. In a market with more than 31 million contracts, even partial improvements in matching create substantial aggregate benefits.

Policy Trade-Offs

The findings suggest that policies such as a centralised risk bureau can deliver large welfare gains by strengthening competition and improving market efficiency, even without fully eliminating information asymmetries.

At the same time, the study highlights an important trade-off. By reducing the advantage of firms with the most sophisticated risk-prediction systems, data-sharing policies may weaken incentives to invest in better predictive technologies. The results therefore point to a balancing act between promoting competition through information sharing and preserving incentives for innovation and privacy safeguards.