Step 1 to a successful Solvency II approach: Data Collection and Quality

In a demanding regulatory environment, data forms the foundation of any robust Solvency II approach for insurance companies. Incomplete, inconsistent, or poorly traceable data can undermine calculation reliability, weaken decision-making, and expose insurers to compliance risks.

A structured and industrialized data management approach enables insurers to ensure compliance while improving operational efficiency.

This first step in the Solvency II process is therefore critical, before modeling, calculating, and analyzing.

Data used under Solvency II comes from multiple sources: actuarial, accounting, asset, reinsurance, and more. It also comes in various formats and through different tools, with varying frequencies and levels of granularity.

The challenge is not merely to collect data, but to ensure that it is complete, consistent, reconciled across different sources, and usable within tight deadlines. In practice, more than one-third of the time spent on annual Solvency II production is dedicated to data collection and quality, significantly reducing the time available for analysis and decision-making.

Data quality must be controlled from the moment of import through several levels of checks:

• Implement automated validation rules.
• Perform blocking controls to prevent the integration of inconsistent data and correct errors without delay.
• Set up alert controls to facilitate analysis without blocking the process.
• Perform automatic reconciliations between different sources (accounting and actuarial, for example).
• Version data to ensure reproducibility and auditability.

In many organizations, data is still spread across multiple tools. Today, a centralized solution enables data to be captured (via import or manual input), structured, and made available to all stakeholders (accounting, actuarial, risk management, etc.): financial data, accounting indicators, liability data, and more.

This centralization through a single, consistent database brings greater flexibility and consistency, while saving time and improving collaboration.

Once data quality is ensured, it is essential to maintain its traceability and integrity throughout the entire process.

The supervisor expects every reported figure to be explainable, justifiable, traceable, and reproducible. This is where the concept of an audit trail becomes critical, which is a key pillar of model governance, operational risk management, and prudential credibility.

Today, traditional tools such as Excel or manual processes do not always guarantee optimal traceability and introduce significant risks, including operational errors, dependency on local files, and lack of transparency.

A dedicated Solvency II solution provides optimal data quality management and monitoring: identification of data origins, documentation of adjustments, and secure validation workflows.

Indeed, every insurer must be able to meet supervisory expectations in this area. Relying on a market-recognized solution for this critical step is a major risk mitigation factor.

Furthermore, implementing dashboards is essential for monitoring data quality. They enable rapid identification of anomalies, prioritization of corrections, and quality tracking over time compared to previous reporting periods.

This level of oversight strengthens prudential credibility and reduces pressure during audits and regulatory reviews.

Solvency II data can no longer be considered as an independent silo. It is now at the heart of a broader regulatory and decision-making ecosystem, directly feeding into other key frameworks such as ORSA, IFRS 17, and internal risk and performance management.

In this context, insurers face a major challenge: ensuring data consistency across multiple frameworks, use cases, and timeframes. The same data must be used for prudential calculations, financial reporting, and management analysis, without divergence or manual reprocessing.

This cross-functionality requires rethinking data architectures. Fragmented approaches, based on independent processing chains, inevitably generate discrepancies, redundancies, and operational risks. Conversely, an integrated architecture enables data sharing, alignment of reference frameworks, and process security.

By structuring data management around these principles, insurers go beyond regulatory compliance: they build a sustainable lever for operational efficiency, reliability, and strategic management.

Beyond technical challenges, data management relies above all on robust, structured governance driven at the highest level of the organization. In a demanding framework such as Solvency II, data becomes a critical asset that must be formalized, controlled, and documented.

Supervisors expect insurance companies not only to demonstrate data quality, but also the robustness of the systems in place to ensure its long-term control.

Data governance is no longer just a regulatory requirement: it is a key factor in reliability, performance, and credibility for insurers.

Data collection and quality are the silent foundation of a high-performing Solvency II framework. When this step is properly structured, through centralized architecture, automated controls, and robust governance, it unlocks reliability across the entire process.

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