ORSA reports are meant to be a strategic cornerstone, connecting capital, risk, and business planning. At their best, they give boards clarity on resilience, regulators confidence in oversight, and executives a compass for navigating uncertainty. At their worst, they become slow, disconnected documents that fail to offer real insight or challenge assumptions.

This article outlines a collection of common and problematic pitfalls I’ve seen in ORSA stress and scenario testing, capital modelling, and governance. Some are technical, some cultural, and all are worth addressing if we want the ORSA to do what it should: support better decision-making under uncertainty.

These insights reflect my experience across a wide range (and varying quality) of ORSAs, including independent reviews, informal and formal regulatory feedback (including from the Prudential Authority), informal discussions with regulators, and public statements from supervisors across multiple jurisdictions.

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1. Toothless Scenarios and Soft Stresses

• Many ORSA scenarios are too mild to test anything meaningful.
• Often there’s no indication of severity. Is this a 1-in-5 or 1-in-50 event? Without context, interpretation is impossible.
• Firms are sometimes surprised they survive a 1-in-200 scenario, forgetting that survival at that level is by design.

2. Recycled, Stale, or Misaligned Scenarios (and Ignored Emerging Risks)

• Same tired stresses reused each year without meaningful refresh.
• Narrative scenarios assigned numerical calibrations that don’t match the story.
• Horizon scanning is often absent or perfunctory; emerging risks must be systematically identified and tested.
• Scenario testing should anticipate what could plausibly happen next, not merely repeat past events.

3. Implausible or Alienating Scenario Design

• Unrealistic or inconsistent scenarios alienate management and the board.
• Severe scenarios are valuable, but they must be framed with historical precedent or research to be credible.
• Overconfidence in models is dangerous; even the best models can fail catastrophically, as history shows.

4. Over-Engineering vs Usefulness

• Attempting to build the “most accurate” pandemic scenario misunderstands the point: scenarios are for learning and planning, not for prediction.
• Prioritise strategic insight over technical perfection.

5. Investment Returns Detached from Reality

• While not common, flat investment income across stress scenarios is a serious modelling failure.
• Investment returns must reflect changes in asset levels and market conditions under stress.

6. LACDT: Tax Calcs Behaving Badly

• Deferred tax recoverability often lacks robust testing.
• Future stressed profits must first create a DTA before any LACDT benefit can be recognised. Tiering here can hit you – more than you considered for the base SCR calc and your QRT.
• Income vs capital gains treatment and tax fund nuances are often overlooked.
• Just because LACDT can’t be negative (per the FSIs), doesn’t mean you can’t have existing DTAs fail recoverability testing in a stress and have loss amplification from deferred taxes!

7. Tiering and Fungibility Constraints Not Considered

• Capital tiering restrictions often ignored under stress.
• Assumed fungibility between entities or tiers can be unrealistic, especially under stress scenarios.
• See the point about DTA and tiering above too.

8. Over-Reliance on Standard Formula Extrapolation

• Normal distribution assumptions are often inappropriate; t-distributions, Lognormal, Pareto tails, or piecewise fittings are better suited.
• Ideally your own experience should be able to inform 1 in 10 stresses and act as a sanity check on scaled 1-in-200 stresses.
• Thin historical experience leads to poor calibration of rare-event risks, especially for equity markets.
• And really, there are several standard formula stresses that are probably not appropriate as a starting point. Some non-life cat stresses may be too conservative – and mass lapse has its critics, but life cat risk, expense risk, and retrenchment risk stresses are likely too low.

9. Unrealistic Business Volume and Expense Assumptions

• Base cases often adopt stretch targets as certain outcomes.
• Expenses are incorrectly assumed to scale perfectly down with policy volumes, ignoring the reality of fixed costs.

10. Incurred vs Paid Confusion

• Claims incurred and claims paid are routinely confused. The impact on profit vs balance sheet and cash can be counter-intuitve.
• Timing differences, especially under IFRS 17 (LCI/CIP dynamics), matter for liquidity and solvency modelling.

11. Short Projections for Long Risks

• Three-year horizons are insufficient for long-burn risks including the obvious candidate – climate change.
• Five years should be the baseline internally, with qualitative insights over longer horizons. Yes, the reliability decreases as the term increases, but it can still be informative. You may chose to disclose only 3 years more broadly, but the longer view is important to at least understand trends.
• Long-term (10–30 year) qualitative assessments should supplement the ORSA, accounting for amplifying systemic interactions.

12. Disconnect Between ORSA and Management Forecasts

• Management runs the business based on one view; the ORSA is prepared using another.
• Without alignment, the ORSA cannot pass the use test or add value to strategic decision-making.

13. Ignoring Dynamic Risk Interactions

• Risks are often modelled in isolation.
• In reality, correlations and feedback loops matter: lapse impacts guarantees, claims experience shifts reinsurance pricing, and market volatility affects lapse and claims simultaneously.

14. Either No Management Actions, or Superhero Versions

• Some ORSAs model no management actions (overly conservative but unrealistic).
• Others assume immediate, flawless actions without delay or cost (equally unrealistic).

15. Unexplained Profit and NAV Changes

• ORSA profit projections must reconcile to balance sheet movements.
• Adjustments between IFRS and SAM/Solvency II frameworks should be clearly documented.

16. ORSA Process Too Slow to Be Relevant

• A nine-month ORSA development cycle leads to stale outputs.
• ORSA timing must be aligned with the business planning cycle and responsive to external shocks.

17. Weak QA and Model Review

• Detailed, independent model review is often absent.
• Common failures include claims timing mismatches, unrealistic ROEs, omitted asset growth dynamics, and unstated assumption interactions.

18. Boilerplate Overload, Insight Underload

• ORSAs are often bloated with standard wording, burying the important insights.
• Focus must remain on what is changing and what genuinely informs management decisions.

19. No Trigger or Process for Out-of-Cycle ORSA

• Firms sometimes only trigger an out-of-cycle (OOC) ORSAs for an SCR breach — far too late. If the risk or solvency situation (internal or external) has changed, it’s time for an OOC.
• Proportional, trigger-based OOC ORSAs must be defined and actioned when material changes occur.
• An OOC doesn’t need to cover the entire process or the full 80 page report. Just the key parts that have changed.

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20. Reverse Stress Testing as an Afterthought

• Reverse stress testing needs to explore genuinely different failure modes, not just ramp up severity.
• Defining what constitutes “failure” (capital breach, strategic collapse, or profitability death spiral) needs careful thought.

21. Weak or Missing Rationale for Scenario Selection

• Documenting why scenarios are chosen reveals how the firm prioritises risk.
• Disconnects between identified risks and tested scenarios highlight critical weaknesses.

22. Board Engagement and Use Test Failures

• Board sign-off without meaningful engagement misses the point.
• Effective risk functions bring ORSA components to the Board repeatedly during the year to drive strategic debate.

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In Closing

None of these issues is inevitable. Most stem from habits — some from lack of scrutiny, others from good intentions that weren’t tested hard enough. But if the ORSA is to support real-world resilience, it has to reflect how capital and risk actually behave. That means grounding assumptions, engaging the business, and constantly asking: “Would I act on this?†

If the answer is no, the ORSA needs work. If the answer is yes, you’re on the right track.