IFRS17 Data Quality: A Layered Unraveling of an Architectural Predicament

IFRS17’s data quality challenge isn’t merely technical; it’s a profound architectural crisis. Let’s dissect this predicament layer by layer to reveal its core.

Layer 1: The Apparent Dilemma

The IFRS17 data quality challenge, while seemingly about “data inaccuracy,” is fundamentally a re-architecture of temporal granularity. Traditional insurance data is aggregated by “policy year”; IFRS17 demands slicing by “contract group” across “reporting periods.” This isn’t a data cleansing issue; it’s an ontological transformation of the data itself.

Layer 2: The Architectural Disconnect

Delving deeper – why is this transformation so difficult?

Because insurance companies’ data architectures are optimized for operations, not financial reporting. Core systems, actuarial systems, and financial systems operate in functional silos. Their respective data models reflect departmental workflows, not a unified value measurement framework. The IFRS17 concept of “Fulfillment Cash Flows” (FCF) inherently transcends the boundaries of these three systems.

This exposes a more profound schism: the inherent separation between operational data and financial reporting data.

Layer 3: The Governance Paradox

Probing further – why is this separation so intractable?

Because the data governance power structure is fundamentally misaligned with the requirements of data flow.

• Data ownership is entrenched in the originating department (“departmental data silos”).
• Those who need data often lack the authority to define it (separation of data usage rights and definition rights).
• IFRS17 mandates end-to-end data lineage, yet governance authority is fragmented.

The core contradiction here: data’s true value is realized in its fluidity and flow, but its control is static and ossified.

Layer 4: The Cognitive Chasm

Continuing to drill down – why is this power structure so resistant to change?

Because different roles possess fundamentally divergent epistemologies of “data”:

• Actuaries view data as “assumption parameters” (adjustable model inputs).
• IT professionals see data as “system records” (immutable historical facts).
• Finance professionals interpret data as “accounting entries” (debits and credits requiring balance).
• Business operations staff perceive data as “business metrics” (KPIs for optimization).

IFRS17 demands that all four of these cognitive interpretations of data must simultaneously hold true – this is an epistemological challenge.

Layer 5: The Temporal Essence

A deeper inquiry – why does IFRS17 create this cognitive conflict?

Because IFRS17 attempts to resolve the inherent temporal paradox of the insurance business:

• The value of insurance contracts resides in the future (the present value of future payments).
• Yet, traditional accounting reports are anchored in the past (the historical cost principle).
• IFRS17 strives, at the “present” reporting date, to simultaneously accommodate past facts and future estimates.

This necessitates data being both retrospective (historically traceable) and prospective (assumptions must be updatable) – a requirement that traditional data architectures, designed for a unidirectional time flow, struggle to meet.

Layer 6: The Ontological Predicament

Reaching the foundational layer – what fundamental truth does this point to?

The insurance data predicament is, in essence, the fundamental contradiction between probabilistic existence and deterministic record-keeping.

The core business of insurance is to trade in “uncertainty,” yet financial reporting demands “certainty.” IFRS17 attempts to cushion this contradiction with concepts like CSM (Contractual Service Margin), but this merely defers the release of uncertainty rather than eliminating it.

The root cause of data quality issues: we are attempting to describe a probabilistic business essence with inherently deterministic data structures.

The Deepest Layer: Irreducible Tension

The IFRS17 data challenge ultimately points to an irreducible tension:

• Regulatory demands for transparency (all assumptions must be auditable).
• Competitive demands for opacity (actuarial assumptions are core intellectual property).
• The same data set must simultaneously satisfy these two contradictory requirements.

This is not merely a technical problem, nor solely a management problem; it is an inherent paradox within the modern financial regulatory system – forcing market participants to walk a tightrope between complete transparency and proprietary commercial confidentiality.

Back to Practice: The Architectural Response

Given that we’ve touched upon these fundamental contradictions, any effective solution must be architectural, not merely technical:

Establish a dual-layer data architecture:

• Atomic Layer: Immutable factual business records (transactional level, unprocessed).
• Semantic Layer: Flexible business interpretation frameworks (with auditable and traceable transformation logic).

This approach allows diverse cognitive frameworks to coexist within the semantic layer, all sharing a common factual foundation. However, it requires abandoning the illusion of a “single source of truth” and embracing the reality of “multiple valid truths.”

At this point, we are no longer just addressing an IFRS17 problem. We are confronting the cognitive limits reached when modern enterprises attempt to describe 21st-century probabilistic business models using 19th-century accounting paradigms.