Bad Data = Bad Decisions

Why Insurance Companies Fail

Poor data quality isn’t just an IT problem - it’s a strategic vulnerability costing insurers billions.

Our analysis reveals how flawed data silently erodes profitability, destabilizes reserves, and threatens solvency.

Research shows the staggering impact: 28% of reserve deficiencies stem from data quality issues, while insurers lose 3-5% of potential premium annually through incorrect classifications.

The Four Dimensions of Insurance Data Quality

Addressing data quality requires understanding its key dimensions in an insurance context.

1. Accuracy: insurance data must correctly represent the real-world entities and events it describes. Using outdated business descriptions for risk classifications can lead to inaccurate assessments, causing insurers to underprice high-risk policies or overprice low-risk ones. This misalignment reduces profitability, increases claim payouts, and weakens competitive positioning in the market.

2. Completeness: all necessary data elements must be present for decision-making. Incomplete loss histories for commercial accounts can result in flawed decision-making in the insurance sector. Without accurate data on past claims, insurers may miscalculate risks, leading to inaccurate premiums, insufficient coverage, or higher exposure to losses. Reliable loss histories are essential for making informed underwriting decisions.

3. Consistency: data should maintain integrity across systems and processes. Inconsistent handling of multi-peril policies can lead to reporting challenges for insurers, such as inaccurate data, misclassification of risks, and compliance issues. These discrepancies make it harder to analyze performance, assess risk exposure, and meet regulatory requirements effectively.

4. Timeliness: data must be available when needed and reflect current reality. Outdated exposure data fails to capture recent business changes, leading to inaccurate risk assessments and flawed decision-making in insurance. Without up-to-date information, insurers may overlook emerging risks or misprice coverage, impacting profitability and client trust.

Insurance Data Quality Affects Innovation

Beyond direct losses, poor data quality prevents innovation, with 67% of insurers citing it as a primary barrier to deploying advanced analytics.

We examine why insurers struggle particularly with data quality, from legacy system fragmentation to extended data supply chains and evolving risk landscapes.

Through real-world case studies including Reliance Insurance, HIH Insurance, and AIG, we demonstrate how data problems cascade into catastrophic business failures.

The path forward requires understanding the four dimensions of insurance data quality - accuracy, completeness, consistency, and timeliness - and building a data-conscious culture throughout your organization.

Full article is available here on SubStack.