How Quality Data Can Improve Insurance Risk Evaluation
Torino, 10th April 2026
The Challenge of Risk Evaluation
Product recalls are one of the most complex risks manufacturers face, involving safety concerns, regulatory scrutiny, and significant financial impact. According to the National Highway Traffic Safety Administration (NHTSA), recalls can affect thousands or even millions of units.
For insurers, evaluating this risk is challenging because it depends on how products are designed, manufactured, and controlled in practice.
Yet underwriting still relies largely on historical data and high-level information, often missing how risk is actually managed today.
Could operational quality data provide a clearer picture of product recall risk?
The Problem: How Insurance Risk Is Evaluated Today
Underwriting product recall insurance requires insurers to assess how likely a manufacturer is to experience a defect that leads to a large-scale recall event.
To do this, insurers typically rely on several types of information:
- questionnaires completed by the manufacturer
- past claims or loss history
- company size, product type, and production volume
- certifications such as ISO or IATF quality standards
- internal underwriting analysis and expert judgment
These inputs help insurers form a general understanding of the company’s exposure. However, they often represent a static snapshot rather than a continuous view of operational risk.
Research on commercial property and casualty underwriting highlights that traditional underwriting processes still depend heavily on manual inputs and fragmented data sources, even as insurers seek to incorporate more analytics and structured information into their decision-making (McKinsey: How data and analytics are redefining excellence in P&C underwriting).
As a result, underwriters may pass on potential opportunities because they are unable to properly assess and price the associated risks.
Why Quality Data Matters for Risk Evaluation
Product recalls rarely appear suddenly. In many cases, they are preceded by signals within manufacturing and quality systems.
Examples include:
- increasing defect rates
- supplier non-conformities
- warranty claims
- customer complaints
- production deviations
- repeated corrective actions
- quality incidents reported internally
These indicators are essential for assessing risk levels and determining how that risk should be priced. They show how frequently defects occur, how quickly they are detected and contained, and what it costs to repair them.
Why Insurers Need Quality Data
Better risk selection
Underwriters aim to distinguish between higher-risk and lower-risk exposures. When insurers can see how manufacturers manage defects, supplier quality, and corrective actions, they gain a clearer understanding of operational risk.
Modern underwriting increasingly relies on improved data sources and analytics to support these decisions. According to McKinsey research on insurance underwriting, better data and analytics can significantly enhance insurers’ ability to evaluate risk and make more informed underwriting decisions (McKinsey: How data and analytics are redefining excellence in P&C underwriting)
More accurate pricing
Insurance pricing is intended to reflect the underlying risk of the exposure. If underwriters only see historical claims or high-level company information, pricing may not fully capture how risk is being managed today.
Operational quality data can provide evidence of:
- process stability
- defect containment capability
- supplier quality performance
- corrective action effectiveness
This information can help insurers evaluate risk more accurately.
A clear example is extended warranty coverage. Insurers typically price these products based on historical failure rates and general assumptions about component reliability. However, when detailed data is available on critical components, such as failure frequency, repair cost, and detection time, pricing can be significantly more accurate.
Instead of relying on averages, insurers can assess how specific components perform in real operational conditions, allowing for more precise risk evaluation and better-aligned pricing.
Greater transparency between manufacturers and insurers
Large manufacturers often invest heavily in structured quality control, testing, and supplier management systems, which can increase confidence from an insurance perspective.
In contrast, smaller manufacturers may still rely on unstructured or fragmented quality processes. This raises an important question for insurers: how accurately can risk be assessed when visibility into quality management is limited?
Structured quality data can help manufacturers demonstrate how they monitor and control operational risk. This transparency benefits both sides: insurers gain better insight into exposure, while manufacturers can show evidence of strong quality management.
The Emerging Opportunity: Connecting Quality and Insurance
Manufacturing is becoming increasingly data-driven, with quality systems capturing detailed information on defects, suppliers, and corrective actions.
At the same time, insurers are seeking better ways to assess and price risk using more reliable data. This creates an opportunity to connect manufacturing quality management with insurance risk evaluation.
This is similar to extended warranty pricing, where underwriters often rely on assumptions about component reliability without structured operational data.
When quality data is structured and accessible, insurers can better assess how risk is managed and price it more accurately. For manufacturers, it provides a clearer way to demonstrate quality performance and operational control.
Rcalls structures and organizes this data, enabling more transparent risk assessment and more informed underwriting.
