As IFRS 17 implementation is once again pushed back, we speak to senior actuaries about some of the most pressing issues associated with the standard
The International Accounting Standards Board has again deferred the implementation of IFRS 17, this time to 2023. Most insurance companies still have a significant amount of work to do to meet the new deadline.
With project teams covering data, systems, business structure and transition plans, smaller and localised insurance players will struggle to find the resources required. Regulatory approaches, now and in the past, will affect the challenges that insurers face. Insurers must strike a balance between the significant operational challenges and the operational and decision-making improvements on offer.
The Actuary spoke to four industry experts to find out what aspects of IFRS 17 are keeping them up at night.
Steven Morrison is senior director – research at Moody’s Analytics
How can illiquidity premia be estimated for IFRS 17?
There are established methods – one is the use of structural models to estimate the amount of credit spread, with remaining spread attributed to an illiquidity premium.
An important question that has received less attention is how to map such estimates to insurance contracts to reflect their particular illiquidity characteristics. There has been some talk of a ‘bucketing’ approach, where a fixed proportion of the illiquidity premium on some reference asset portfolios is applied, depending on the type of contract. This allows for relative ranking of illiquidity, although the choice of proportions applied for each bucket is subjective.
What are the potential impacts of insurers being allowed different discount rate assumptions?
The choice of liability discount rate will affect earnings volatility. The higher the correlation between liability and asset discount rates, the lower the volatility of the net finance result.
The choice of discount rate also affects attribution between the finance result and insurance service results. The higher the liability discount rate at initial recognition, the higher the contractual service margin, resulting in higher insurance service results as this is released. On the other hand, the net finance result is lower as the contracts unwind at a higher rate.
The total profit over the lifetime of contracts doesn’t depend on assumptions like the discount rate, but profit timing and volatility can be sensitive to these assumptions. Entities will have to understand their sensitivity and be able to explain this to investors. This may prompt the establishment of standardised benchmark assumptions and sensitivity tests to aid comparability.
How should liability discount rates be accounted for in the valuation of participating contracts, particularly where these are valued using stochastic scenarios?
Changing the yield curve in your scenario generator affects the discount rate applied to cash flows and the returns on assets. This has practical implications – the liability discount rate affects asset returns, so requires consideration in the calibration process. It also isn’t clear that it is theoretically sound – why should asset returns be affected by the assumed liability discount rate (unless assets and liabilities have the same illiquidity characteristics)? An alternative approach is to calibrate the scenario generator to a risk-free curve and apply any illiquidity premia as adjustments to output discount rates and asset returns. This way there is no impact to the scenario generator’s calibration, and the user can control the liability discount rate separately from any assumptions about asset returns.
Thomas Bulpitt is a senior consultant within Milliman’s London Life and Financial Services practice, chair of Milliman’s global IFRS 17 task force, and a member of the IFRS 17: Future of Discount Rates working party
IFRS 17 requires firms to reflect non-financial risk inherent in the insurance contract cash flows. What approaches are available?
There is no standard calculation for a ‘risk adjustment’ to a best estimate liability. Solvency II and International Capital Standards (ICS) both specify a method of calculation for the risk adjustment, namely the cost of capital approach for the calculation of the risk margin, and a value at risk (VaR) type approach for the calculation
of the margin over current estimate (MOCE), respectively. For market consistent embedded value, a variety of methods can be used, though most use a cost of capital approach.
A few commonly accepted approaches include VaR, or confidence level; tail value at risk (TVaR), or conditional tail expectation; cost of capital; and application of prudent margins. IFRS 17 is principles-based – there is no prescribed approach. Without specific rules, different interpretations can complicate this calculation. Many firms will not have the capability to calculate VaR (or TVaR) over anything other than the one-year time horizon specified by Solvency II, eg for ultimate run-off. Many will also lack the appropriate stochastic models to determine stress scenarios under a range of percentiles.
This risk adjustment needs to be expressed as a confidence level (VaR) metric to ensure comparability of results. Given the varied approaches and sophistication of firms’ models, how can comparability be achieved?
Where firms have access to stochastic models, this could be as simple as reading off the risk adjustment from the full distribution of outcomes. For many firms, however, this will not be practicable.
It may be possible, subject to interpretation, for a firm to assume that the risk margin from Solvency II is appropriate for the purposes of IFRS 17, and therefore determine the confidence level required by IFRS 17 by calibrating a normal (or other) distribution using the best estimate and 99.5% VaR under Solvency II, and then solving for the implied percentile. This approach is similar to the Prudence-MOCE approach considered under the ICS regime.
Other approaches do exist; one would be to develop a joint distribution of the relevant risks and sample from that distribution to generate a large number of multivariate scenarios. These could be consumed by efficiently designed cash flow models to generate a loss distribution to which the risk adjustment could be compared, in order to derive the resulting percentile or confidence level.
Wijdan Yousuf is a capital solutions actuary at Aon and chair of the Contractual Service Margin working party, many members of which contributed to this section
IFRS 17 permits portfolios of insurance contracts to be divided into more ‘groups’ than it minimally requires. But how far should an insurer go?
Reasons being considered to go more granular than the minimal requirements are: ensuring adequate information for effective management; and maximising the ability to use results for other analysis.
Grouping at a higher level of granularity enables ‘cross-subsidies’ between contracts with different levels of profitability. This comes at the cost of losing vital information about the worsening profitability of certain contracts – hampering the ability to take corrective actions quickly and effectively manage the business.
Granular data enables insurers to ‘slice and dice’ results any way, gaining insights into hidden trends split by region, age, gender, distribution channel, etc.
What are the key areas of complexity when considering the impact of IFRS 17 on with-profits business?
The standard still requires accounting down to the level of annual cohorts for with-profits business. The risk-sharing implicit in with-profit funds makes it problematic to rationally allocate cash flows down to specific cohorts of contracts.
Many UK with-profits policies have guaranteed annuity options (GAOs). Insurers typically administer these as two separate contracts: 1) with-profits savings and 2) annuity. IFRS 17 seems to push towards accounting for policies with GAOs as a single contract, introducing operational complexity and potential accounting mismatches.
What are coverage units and what are some challenges being faced here?
Coverage units affect the timing of profit recognition under IFRS 17. They represent the amount of service an insurer provides to policyholders. The determination of coverage units must allow for insurance coverage, as well as investment service provided (if any). Questions that make this determination problematic include: a) whether investment services provided should be deemed constant irrespective of the size of the policy, or whether it should vary based on it (eg increasing as the size of a unit-linked policy becomes larger, decreasing as it becomes smaller); and b) how a company should weigh insurance coverage and investment service provided when determining coverage units.
Mehul Dave is director (actuarial and insurance) at Deloitte Southeast Asia and chair of the Singapore GI IFRS17 working party
How will IFRS 17 impact general insurers operationally?
The technical actuarial challenges from IFRS 17 relate mainly to discounting and risk adjustment. These may be new concepts for insurers in certain jurisdictions, and so new methodologies and approaches must be developed. European insurers, having been through Solvency II, may have had the ‘mindset shift’ towards the cash flow-type valuation approaches that IFRS 17 requires, whereas Asian insurers have not had this experience.
As most write short-duration business, the majority of insurers will prefer the simplified measurement model – the premium allocation approach. As such, operational implications for general insurers will be more significant than the purely technical challenges.
With more to ‘get done’ before closing regular accounts under IFRS 17, insurers will need to look for ways to be efficient in their reporting processes. For example, analyses of change will need to be more detailed, with more granular assumptions being set. Automating the dashboards to produce waterfall charts and breakdowns will reduce manual effort. The onward implications for planning, internal and external metrics, and capital will need to be incorporated once IFRS 17 financials are adopted.
How will general insurance (GI) actuaries need to adapt?
With increased transparency and granularity in the financial statements, there will be a greater onus on actuaries to explain movements and reconciliations. The flow of data and information between the actuarial and finance functions is likely to increase.
GI actuaries who have historically focused on earned liability reserving will need to devote more effort to unearned liability reserving. For jurisdictions where cash flow modelling is not typical, GI actuaries will need to get to grips with a new approach, as well as the dynamics of discounting and interest rate effects on profitability.
Greater transparency and the loss recognition requirements for onerous contracts will bring granular drivers of profitability into the spotlight. This will encourage reserving, pricing, strategy, marketing and sales functions to interact more closely.
IFRS 17 will change the role of the GI actuary. Investment in technology and efficiency, coupled with emphasis on granular analysis, will refocus actuaries’ efforts towards stewardship of data and analysis within the organisation, and drive more active involvement in strategic decision-making.