IFRS 17: How Discounting Shapes Financial Outcomes
| Featured Publication
American Academy of Actuaries · Contingencies Magazine · March/April 2026 |
Shasat Research Featured in Contingencies — the Magazine of the American Academy of Actuaries
Michael Winkler and Sunil Kansal’s analysis of IFRS 17 discounting has been published as a feature article in Contingencies, one of the most widely read actuarial publications in the United States.
Shasat Consulting · Michael Winkler & Sunil Kansal · March 2026
We are delighted to share that our article, IFRS 17 — How Discounting Shapes Financial Outcomes, has been published as a feature in Contingencies, the flagship magazine of the American Academy of Actuaries, in its March/April 2026 edition.
The American Academy of Actuaries is the professional association for actuaries in the United States, and Contingencies is read by thousands of actuarial professionals, CFOs, and finance leaders globally. Being selected as a feature article represents significant recognition of the research quality and its practical relevance to the profession.
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The article examines how discount rates under IFRS 17 are derived and applied in practice, with particular focus on the differences between the top-down and bottom-up approaches, the relationship with Solvency II methodology, and the practical challenges insurers face across different markets and geographies.
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Key Topics Covered in the Article
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“Find out how IFRS 17 discount rates determine present value and influence reported liabilities, as well as the practical challenges insurers face in deriving and applying them across markets.” — Contingencies, March/April 2026 |
This publication follows the earlier release of the same research by the Actuaries Institute of Australia, reflecting the global reach and relevance of this analysis. Together, these publications represent recognition by two of the world’s leading actuarial professional bodies.
The research builds on the authors’ book, Navigating IFRS 17: A Practical Guide to Accounting & Actuarial Implementation, and reflects Shasat’s continued commitment to producing rigorous, practice-oriented insight for the global insurance, actuarial, and finance community.
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Read the Full Feature Article IFRS 17 — How Discounting Shapes Financial Outcomes Contingencies Magazine · American Academy of Actuaries · March/April 2026 |
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Author Michael Winkler Actuary (SAA/DAV) at Shasat Consulting. Previously in leading actuarial positions at Swiss Re, Munich Re/New Re, and Winterthur Group. |
Author Sunil Kansal Head of Consulting at Shasat. Chartered Accountant and Fellow of the Institute of Chartered Accountants in England and Wales (ICAEW). |
Tags: IFRS 17 · Discounting · Contingencies Magazine · American Academy of Actuaries · Solvency II · Insurance Liabilities · Actuarial Research · Shasat
IPO Market Outlook 2026: Strong Global Pipeline Takes Shape
Global IPO Markets Show Strong 2025 Growth and a Robust 2026 Pipeline
Shasat Research Desk | January 2026 | Capital Markets · IPO · US GAAP · Regulatory Readiness |
8 min read |
After several years of subdued issuance driven by market volatility and higher interest rates, global IPO activity rebounded meaningfully in 2025. Improving equity market performance, stabilising valuations, and renewed investor appetite reopened listing windows across major financial centres. As 2026 begins, market data points to a growing IPO pipeline, alongside heightened regulatory scrutiny and greater execution discipline.
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202
IPOs Completed
in 2025 |
200–230
Forecast IPOs
for 2026 |
$40–60B
Projected Capital
to be Raised |
300+
Companies Queued
in Hong Kong |
2025: A Rebound in Global Initial Public Offerings
According to multiple market trackers, the global IPO market made a notable recovery in 2025. Global issuance reached one of the strongest levels since 2021, supported by double-digit equity market growth that encouraged companies to revisit public listing plans. Rising stock indices helped restore investor confidence and created more favourable conditions for capital raising.
Global IPO proceeds rose substantially compared with 2024, reflecting both increased deal activity and larger average deal sizes, particularly in major markets such as the United States and Asia. Regional variation remained pronounced:
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Regional Performance Highlights
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What’s Driving IPO Activity in 2026
Looking ahead, most IPO outlooks suggest a continued but selective recovery in 2026. Analysts forecast 200–230 IPOs globally, potentially raising $40–$60 billion, driven by interest in technology, energy transition, and mature private companies seeking liquidity. Several offerings postponed in late 2025 are expected to come to market in early 2026, supporting a stronger first quarter.
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Monetary Policy
Expectations of gradual easing continue to support valuations and improve the cost of capital for listing candidates across all major exchanges. |
Equity Markets
Resilient equity markets have restored investor confidence and widened the issuance window for first-time issuers across multiple sectors and geographies. |
Private Backlogs
Extended private funding cycles have created a large cohort of mature companies ready to access public markets for liquidity and growth capital. |
At the same time, macroeconomic uncertainty and geopolitical risks remain key considerations shaping issuer and investor behaviour across all regions.
Technical and Regulatory Readiness Takes Centre Stage
Market participants increasingly note that the current IPO cycle differs from earlier post-pandemic waves. Regulators and investors are placing far greater emphasis on financial disclosure quality, governance readiness, and consistent application of accounting standards.
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“IPO execution now requires detailed coordination between finance, legal, compliance, and senior management teams. Understanding regulatory frameworks — from SEC filings to prospectus requirements across Europe and Asia — has become a core execution risk, not a procedural formality.” Shasat Market Intelligence · January 2026
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As a result, many organisations are investing earlier in internal IPO capability-building — complementing external advisory support with structured technical learning. These programmes typically focus on IPO readiness, regulatory filings, disclosure mechanics, and public-company reporting obligations.
Within this context, Shasat delivers transaction-oriented IPO and US GAAP programmes aligned with live market practices — used by finance leaders, legal and compliance teams, and advisors to strengthen technical preparedness and improve regulatory engagement across multiple jurisdictions.
Building IPO Capability Across Global Financial Centres
Reflecting the global nature of IPO activity, structured IPO and SEC filing programmes are delivered across key financial centres where listing activity remains concentrated — including New York, London, Singapore, Hong Kong, Mumbai, Toronto, and Zurich.
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IPO Requirements & SEC Filing Process — Programme Locations
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Market feedback suggests that organisations which invest early in internal IPO capability are better positioned to navigate regulatory review cycles, reduce execution risk, and sustain compliance standards after listing.
IPO Markets Stabilising With Selective Opportunity
The 2025 IPO market demonstrated renewed resilience following several subdued years. With strong activity in the United States, India, and parts of Asia, and a growing pipeline shaping up for 2026, issuers have reason for cautious optimism.
However, success in public markets increasingly depends on technical readiness and regulatory discipline, as much as on market timing. For companies considering future listings — particularly cross-border transactions — staying current with evolving disclosure requirements, accounting standards, and IPO execution practices will remain a defining competitive advantage as global capital markets continue to evolve.
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Also Published via Medium
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IPO 2025
SEC Filing
US GAAP
Capital Markets
IPO Readiness Training
Shasat
IFRS 17: Discounting and Market Complexities
In the News · IFRS 17 · Actuarial Practice · Research
Shasat Research Published by the Actuaries Institute of Australia: IFRS 17 Discounting and Market Complexities
An in-depth analysis of IFRS 17 discount rate methodology — authored by Shasat’s Michael Winkler and Sunil Kansal — has been selected for publication by the Actuaries Institute of Australia as part of its research and analysis series.
Shasat Consulting | Michael Winkler & Sunil Kansal | March 2026
We are pleased to announce that our research article, IFRS 17 Discounting and Market Complexities, has been published by the Actuaries Institute of Australia as part of its Research & Analysis series. The article was authored by Michael Winkler, Actuary (SAA/DAV) at Shasat Consulting, and Sunil Kansal, Head of Consulting at Shasat.
The publication addresses one of the most consequential and least straightforward aspects of IFRS 17 implementation — how to derive appropriate discount rates for insurance contract liabilities. Drawing on analysis of 2024 annual reports from leading global insurers including Aviva, Generali, AXA, Allianz, Aegon, Sun Life, Prudential, and Manulife, the article provides both technical depth and practical market perspective.
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The Article Covers · The top-down and bottom-up approaches to setting IFRS 17 discount rates and how they compare to Solvency II · Practical challenges including duration mismatches, currency mismatches, and liquidity premium estimation · How to construct the discount curve beyond the last observable market point · The OCI versus P&L accounting policy election and its implications · Challenges specific to developing markets where observable risk-free rates are unavailable · Market observations from 2024 insurer disclosures across EUR and USD rate environments |
The article highlights that while most insurers have adopted the bottom-up approach — closely aligned with the Solvency II Volatility Adjustment — practice varies significantly across companies and jurisdictions. Decisions around discount rate methodology, liquidity premium calibration, and the OCI election have material consequences for reported liabilities and earnings volatility.
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“We would highly recommend analysing the quantitative impact of any simplifications at the earliest possible stage — before they become embedded in reporting processes and create downstream compliance and restatement risk.” — Michael Winkler & Sunil Kansal |
The research builds on the authors’ broader work on IFRS 17, including their book Navigating IFRS 17: A Practical Guide to Accounting & Actuarial Implementation, and reflects Shasat’s ongoing commitment to producing technically rigorous, practice-oriented insight for the global insurance and actuarial community.
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Read the Full Article Published by the Actuaries Institute of Australia — Research & Analysis Series |
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Michael Winkler Actuary (SAA/DAV) at Shasat Consulting. Previously in leading actuarial positions at Swiss Re, Munich Re/New Re, and Winterthur Group. |
Sunil Kansal Head of Consulting at Shasat. Chartered Accountant and Fellow of the Institute of Chartered Accountants in England and Wales (ICAEW). |
Tags: IFRS 17 · Discounting · Solvency II · Actuaries Institute of Australia · Insurance Liabilities · Liquidity Premium · OCI Election · Shasat Research
The Time value of money in IFRS 17: how discounting shapes financial outcomes
IFRS 17 and Discounting: Understanding the Time Value of Money in Insurance Accounting
By Michael Winkler and Sunil Kansal
Applying the time value of money under IFRS 17 is one of the most technically demanding areas of modern insurance accounting.
Earlier insurance accounting standards applied discounting inconsistently. In many jurisdictions, particularly in non-life insurance, insurers often avoided discounting claims reserves. This practice created a conservative buffer within reported liabilities but reduced comparability across financial statements.
IFRS 17 fundamentally changes this approach. The standard introduces a consistent framework requiring insurers to discount all long-term insurance cash flows. This ensures that liabilities reflect the time value of money and the economic characteristics of insurance contracts.
Under IFRS 17, discount rates must reflect the financial risks associated with the insurance liabilities. At the same time, insurers must separately recognise a Risk Adjustment to capture non-financial risks such as underwriting uncertainty and claims variability.
The challenge becomes more complex in markets where reliable financial market data is limited. Without deep or liquid markets, determining credible discount curves becomes difficult. Insurers must therefore rely on estimation techniques, proxy market data, or internal modelling approaches.
Consequently, developing robust discounting methodologies has become one of the most important technical tasks for actuaries, finance teams, and risk managers implementing IFRS 17.
Read the full article:
View the publication at Fundación MAPFRE
For advisory support on IFRS 17 implementation, discount rate methodologies, or insurance valuation frameworks,
contact Shasat Consulting.
IFRS 17 Discounting: Strategic Choices with Significant Consequences
IFRS 17 · Insurance Liabilities · Actuarial Practice
IFRS 17 Discounting: Strategic Choices with Significant Consequences
The methodology choices companies make when setting discount rates under IFRS 17 carry material consequences for reported liabilities, P&L volatility, and long-term comparability across the industry.
By Michael Winkler, Actuary (SAA/DAV), Shasat Consulting | Sunil Kansal, Head of Consulting, Shasat | 28 July 2025
| 42% of insurers use same or similar rate as EIOPA |
58% use a higher discount rate than Solvency II |
30 yrs typical EUR last liquid point used in practice |
Bottom-Up dominant methodology used by most insurers |
A sum of money currently available has a greater value than the same sum to be paid in the future — due to its earnings potential in the interim. This so-called “Time Value of Money” is a core principle of finance which is also reflected in IFRS 17. The concept of discounting future cash flows was not consistently applied under previous standards — many non-life insurers deliberately avoided discounting claims reserves to include a conservative margin. IFRS 17 changes this fundamentally: discounting is now consistently required for all long-term cash flows.
According to IFRS 17, appropriate discount rates shall reflect the time value of money, the characteristics of the cash flows and the liquidity characteristics of the insurance contracts. They must be consistent with observable current market prices for financial instruments with comparable characteristics, and must exclude any factors influencing market prices that do not affect the future cash flows of the insurance contracts.
Basis Rules to Derive Discount Rates
Under Solvency II, the Matching Adjustment enables insurers to adjust the risk-free interest rate used for discounting specific long-term insurance liabilities, incorporating the illiquidity premium associated with holding less liquid assets aligned with liability cash flows. In contrast, IFRS 17 does not impose explicit restrictions on the selection of the reference portfolio — insurers have the flexibility to utilise their own asset portfolios, provided the resulting discount rates appropriately reflect the characteristics of the insurance contracts and are consistent with observable market data.
Similar to the Volatility Adjustment in Solvency II, discount rates can also be derived by starting from risk-free rates and adding an allowance for illiquidity. This gives rise to two primary approaches.
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Approach 1 Top-Down Based on a yield curve that reflects current market rates of return implicit in a fair value measurement of a reference portfolio of assets, adjusted to eliminate any factors not relevant to the insurance contracts. Starting from a portfolio yield (e.g. 5.5%), credit risk adjustments are deducted to arrive at the discount rate (~4.0%). |
Approach 2 Bottom-Up Based on adjusting a liquid risk-free yield curve to reflect the differences between the liquidity characteristics of financial instruments underlying risk-free market rates and those of the insurance contracts. Starting from the risk-free rate (e.g. 3.25%), a liquidity premium (e.g. 0.5%) is added (~3.75%). |
Most companies use the bottom-up approach — in many cases very closely aligned to the Volatility Adjustment used in Solvency II. However, there are notable exceptions. Aegon has generalised both approaches into one unified direct discounting technique where discount rates equal the risk-free rate plus a percentage of the product-specific illiquidity premium. Aviva and Phoenix (UK) use the top-down approach for annuities, where they also apply the Matching Adjustment under Solvency II.
Variable Fee Approach: Additional Complexity
Cash flows that vary based on the returns on any financial underlying items — for example, when applying the Variable Fee Approach (VFA) for direct participating business — shall be either discounted using rates that reflect that variability, or adjusted for the effect of that variability and discounted at a rate that reflects the adjustment made.
The first option is analogous to a real-world valuation framework, using asset-based discount rates that reflect the rates of return on the underlying items. The second option permits a risk-neutral framework where risk-free rates are used both to project the underlying items and to discount the cash flows — relying on mathematical relationships within and among financial instruments.
Implementation Challenges
A notable challenge faced by many insurers is the duration and yield mismatch between own portfolios and market-referenced portfolios, where long-dated liabilities are paired with medium-term matching assets. The application of a liquidity premium based on the entity’s own portfolio can lead to meaningfully different outcomes depending on the nature of the assets held.
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Scenario 1 Illiquid High-Yield Assets If the insurer holds highly illiquid assets with long-term horizons, these typically offer yields and coupons significantly above market averages. The discount rate applied to insurance liabilities would be higher, leading to lower reported liabilities. |
Scenario 2 Low-Yield Assets If the insurer has invested in low-yield assets in a high-interest-rate environment, the discount rate would be lower, resulting in higher reported liabilities. Currency and term mismatches amplify this further in annuity and whole-of-life portfolios. |
Concrete Implementation Framework
The Canadian Institute of Actuaries has issued an Educational Note on how to derive IFRS 17 discount rates in practice.1 The process involves four key stages: establishing the last observable point on the yield curve using quoted prices from active markets; setting the ultimate risk-free rate with more weight on long-term estimates than short-term fluctuations; setting the liquidity premium to reflect the characteristics of the insurance contracts; and determining discount rates for products where cash flows vary with an underlying item.
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Desirable Characteristics of the Long-Term Discount Curve Stability — The ultimate interest rate should be more stable over time, with long-term rates expected to show lower variability than short-term rates. Smoothness — Interpolated rates should follow a smooth path from the last observable point to the ultimate long-term rate, avoiding artificial jumps. Simplicity — The approach should be straightforward to understand, audit, and implement consistently across reporting periods. |
Setting the liquidity premium is not straightforward. Market-based techniques use the spread difference between covered bonds and risk-free bonds in the same currency. However, due to missing liquidity and longer-term durations, this approach may not be feasible in many markets. Most European companies base their IFRS 17 discount rates on Solvency II discount rates, using Euro market rates up to 30 years — in contrast to EIOPA rates where extrapolation to the ultimate forward rate starts much earlier.
In many developing markets, companies struggle entirely. Risk-free rates may not exist, and the longest available duration may be a few years or less. The only viable reference point is the long-term investment return the company can achieve, with a deduction for unexpected losses. Countries whose currency is pegged to the USD are an exception — companies there typically use those rates as a starting point.
The OCI Election: A Critical Accounting Policy Decision
For each accounting period, discount rates must be updated in line with market movements, as IFRS 17 requires the use of current assumptions. For long-term liabilities, this can have a significant impact on the time value of money and on the fulfilment cash flows. Companies can determine in their accounting policy whether the effect of changes in market discount rates is recognised in P&L or in Other Comprehensive Income (OCI) — in the latter case with interest accrued to P&L at the discount rate locked in at inception.
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“At first glance, OCI seems the better choice, as discount rate volatility should not distort the P&L. However, the change in fair value of some assets — particularly those used for interest rate hedging — always flows through P&L. A careful analysis is essential before making this election.” |
Market Observations: 2024 Reporting Season
In the Solvency II environment, EIOPA-published discount rates are a natural reference point. According to an EIOPA survey, 42% of respondents reported using the same or almost the same rates, with the remaining 58% using — in most cases — a higher discount rate than Solvency II.2 58.5% use the same risk-free rate as Solvency II; for those not, the most relevant differences are a more remote last liquid point (e.g. 30 years for EUR) and a different ultimate forward rate.
Analysis of 2024 annual reports reveals consistent patterns. In developing markets with local currencies pegged to USD, companies normally use USD rates with a particular uplift. Many companies vary discount rates by product type — rates for annuities in payment are typically significantly higher than those derived from the backing asset portfolio yield. Many companies use different rates for the General Measurement Model and the VFA, with VFA rates being more conservative. EUR spreads over EIOPA risk-free rates are broadly constant across durations but drop beyond 20 years — an artefact of extrapolation methods rather than genuine market signals. USD spreads tend to increase with duration across most companies.
A Word of Caution on Simplifications
Some companies chose initially to use average rates for their interest-sensitive business that were not accurately reflecting the actual pricing rates for the various cohorts. This appeared to be a reasonable simplification given the administrative burden of maintaining multiple yield curves. However, many found that a significant number of cohorts turned onerous despite having reasonable pricing margins in reality.
We would therefore highly recommend analysing the quantitative impact of any simplifications at the earliest possible stage — before they become embedded in reporting processes and create downstream compliance and restatement risk.
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Michael Winkler Actuary (SAA/DAV) at Shasat Consulting. Previously in leading actuarial positions at Swiss Re, Munich Re/New Re, and Winterthur Group. |
Sunil Kansal Head of Consulting at Shasat. Chartered Accountant and Fellow of the Institute of Chartered Accountants in England and Wales (ICAEW). |
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1 Canadian Institute of Actuaries: Draft Educational Note / IFRS 17 Discount Rates for Life and Health Insurance Contracts, June 2020 2 EIOPA: “IFRS 17 – Insurance contracts report”, April 2024 |
Tags: IFRS 17 · Discounting · Insurance Liabilities · Actuarial Practice · Solvency II · Risk Adjustment · OCI Election · Shasat
IFRS 17: what are the actuarial challenges of insurance standard?
The insurance contracts standard requires important choices to be made that can have significant impact on company financial statements creating volatility in results, and need to reflect commercial practices, argue Shasat’s Michael Winkler and Sunil Kansal FCA.
IFRS 17 Insurance Contracts requires a much higher level of detailed information than any existing accounting standard and any regulatory framework including Solvency II. Portfolios must be split depending on their type of business, their expected profitability, and their respective underwriting year. This split has to be maintained for many years.
The priority for many companies is, therefore, to enable their IT systems to provide and ‘maintain’ the required granular data. This is a very challenging task on its own but there are other implementation challenges.
IFRS 17 is a principle-based standard. Therefore, many parts are not prescriptive, and companies can make various choices on the detailed implementation. However, the final outcome – the future annual results – will heavily depend on these choices.
Most companies try to mitigate result volatility as much as possible. There are several sources for result volatility in IFRS 17, starting with the grouping of contracts. The standard requires entities to keep onerous contracts separately and to recognise losses immediately so that there is no room for cross-subsidy.
Furthermore, discounting with current rates can be a source of significant volatility. For participating businesses, the so-called ‘variable fee approach’ can mitigate this volatility up to a certain degree with the contractual service margin (CSM) serving as a buffer for interest rate movements.
The authors of IFRS 17 would argue that the CSM is, in general, supposed to reduce profit volatility. However, compared to a standard with locked-in assumptions, IFRS 17 is expected to produce more volatile results, despite the CSM.
Separation of components
An important objective of IFRS 17 is to make the insurance business comparable to other businesses, treating components that are the same as in other businesses consistently and separate from the insurance contract.
This concept applies to services that are not an integral part of the insurance cover provided. It applies also to investment components.
Splitting out investment components that are also available without insurance (e.g. fund investments) is straightforward. However, there are other investment components which can only be identified as amounts are paid independent of any insured event.
Discounted cash flows
The basis for the insurance contract liabilities are the discounted expected future cash flows. Expected cash flows are equal to best estimate cash flows only in the case of symmetric distributions. For other distributions, stochastic simulations (projections based on a set of random values) would be an adequate way to determine them.
This is a major challenge in practice. Accounting statements must be consistent over time as any variation flows through the results. However, stochastic simulations depend on random generators and tend to deliver inconsistent results over time.
Furthermore, they are difficult to audit. A better approach may be to capture the most relevant dependencies in tables (produced based on stochastic calculations) and use those for the periodic statements.
The yield curve for discounting is another challenging item. Companies normally aim for minimising the gap between the impact of market movements on the liabilities and the corresponding impact on the value of the corresponding assets, as this would be another source of volatility. However, asset liability management (ALM) is never perfect and many asset classes cannot be properly reflected in a yield curve.
CSM and amortisation
The CSM at the inception is determined in a way that there is no initial gain. The amortisation runs in line with the so-called ‘coverage units’ reflecting the extent of services provided.
There is not much guidance for the calculation of these; however, the intention seems to be that they are based on pure volume measures (e.g. total sum insured, premiums, etc). They can also include investment services, allowing earlier profits for deferred annuities without risk cover in the deferment period.
In the variable fee approach, the investment yield is flowing into the CSM, leading to a potential deferment of profits. The coverage units should reflect that, leading to higher CSM releases in early years.
If all future cash flows are in line with the initial expectations, the annual insurance results are entirely driven by the CSM and risk adjustment releases. Therefore, a careful check of alternative concepts should be done before taking decisions.
Risk adjustment
IFRS 17 requires an explicit margin for insurance risk, the so-called risk adjustment (RA), reflecting the risk appetite of the company.
There is little guidance on how the RA should be calculated; the standard only contains qualitative statements. On the other hand, the corresponding quantile has to be disclosed. This seems to require stochastic simulations which, however, should rather be replaced by tables (see above).
Reinsurance
Reinsurance assumed is treated exactly like primary insurance. However, ‘reinsurance held’ is causing additional challenges. Reinsurance held is booked separately from the underlying business, because primary insurance payments are never reduced due to any failure of a reinsurer to pay their part of the claims.
The actual challenges arise from a series of significant mismatches between the underlying business and the corresponding reinsurance held.
Transition approach
One of the biggest challenges is the transition to IFRS 17. This process determines equity and CSM at transition and therefore future results.
The most appropriate approach for the transition is the retrospective application of IFRS 17. However, this is very challenging in practice and may simply not be possible for certain portfolios.
Therefore, some simplifications may be used (modified retrospective approach) or the fair value approach. The expectation of the International Accounting Standards Board seems to be that the most recent business is fully retrospective (as the standard is known for a couple of years) and the fair value approach is used for very old business only.
Given its impact, managing the transition well should have high priority.
Moving forward
IFRS 17 requires a lot of decisions on items having a major impact on future results. Companies should check alternative concepts before coming to any conclusions.
The transition has to be handled carefully as it determines IFRS equity and CSM at the start date, with a big impact on future results and an even bigger impact on future return on equity.
About the authors
Michael Winkler is an actuary (SAA/DAV) at Shasat Consulting and previously in leading actuarial positions at Swiss Re, Munich Re/New Re and Winterthur Group. Sunil Kansal, head of consulting at Shasat, is an FCA chartered accountant
Read More: IFRS 17: what are the actuarial challenges of insurance standard?
EU Green Deal Spurs Demand for Expertise in Sustainability Compliance
The European Union’s Green Deal is reshaping the regulatory landscape for businesses, with ambitious climate neutrality goals driving sweeping changes across industries. Companies are now grappling with new compliance requirements, including sustainability reporting, environmental taxonomies, and carbon border regulations, as they adapt to a more sustainable economy.
While the Green Deal aims to position Europe as a global leader in the fight against climate change, the regulatory demands have created significant challenges for organizations, particularly in finance, manufacturing, and energy. Analysts highlight the complex interplay of multiple regulations, including the Corporate Sustainability Reporting Directive (CSRD), the European Sustainability Reporting Standards (ESRS), and the EU Taxonomy.
The Challenges of Navigating EU Green Deal Regulations
Corporate leaders are increasingly vocal about the operational and financial hurdles associated with compliance. The introduction of mandatory sustainability disclosures under the CSRD, for instance, requires businesses to overhaul their reporting frameworks, integrate ESG metrics, and ensure transparency across their supply chains.
“Many businesses are unprepared for the scale and speed of regulatory change,” says a senior compliance officer at a leading European manufacturer. “The CSRD alone has forced us to rethink how we collect and analyze sustainability data.”
Moreover, the EU Taxonomy has introduced a new layer of complexity, asking businesses to classify economic activities based on their environmental impact. As one financial analyst observes, “The taxonomy requires not just compliance but also a fundamental shift in how companies define and measure sustainable investments.
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Specialist Expertise Steps In
The growing demand for expertise in sustainability compliance has fueled the emergence of specialized training and consulting programs across Europe. These initiatives are increasingly seen as essential for helping businesses bridge knowledge gaps and build internal capacity to meet regulatory demands.
Among the leading programs addressing these challenges are workshops designed to tackle the intricacies of the EU Green Deal. For example, courses on the EU Taxonomy are equipping companies with the tools to classify activities and investments effectively. Similarly, tailored training on the CSRD and ESRS guides businesses in aligning their reporting frameworks with evolving regulatory expectations.
One consultancy, widely regarded for its expertise in IFRS, valuations, and compliance, has launched a suite of targeted programs specifically aimed at navigating the complexities of the Green Deal. These include workshops that demystify the EU Taxonomy framework and offer practical strategies for preparing ESG disclosures under CSRD and ESRS.
Programs like these have become indispensable for organizations looking to stay ahead of the curve,” says a compliance manager at a multinational corporation. “They help us understand not just the technical requirements but how to embed sustainability into our broader corporate strategy.
A Broader Shift in Corporate Strategy
Experts suggest that the regulatory pressure of the Green Deal is prompting a broader cultural shift within businesses. Sustainability is no longer confined to compliance teams but is becoming a boardroom priority.
“We’re seeing an increasing demand for strategic management consulting that goes beyond compliance,” notes a source familiar with the trend. “Businesses are seeking guidance on how to turn sustainability into a competitive advantage, rather than just a regulatory obligation.”
Shasat, a consultancy known for its expertise in IFRS and risk management, has responded to this demand with programs that combine technical rigor with actionable insights. Their EU Green Deal Workshop and CSRD and ESRS Compliance Workshop have been highlighted by industry insiders as pivotal resources for organizations looking to align their strategies with the EU’s ambitious goals.
Sustainability as a Business Imperative
As the Green Deal reshapes the European economy, the ability to navigate its complex regulatory environment is becoming a business imperative. Industry observers agree that those who adapt early will be well-placed to lead in the era of sustainable growth.
For businesses aiming to future-proof their operations, courses like Shasat’s EU Taxonomy Reporting Workshop and ESG Masterclass offer a roadmap for integrating sustainability into core business practices.
While compliance remains challenging, the emergence of these targeted training programs and expert advisory services ensures businesses are equipped to navigate the complexities of the Green Deal—and seize the opportunities it presents.
The Devil in the Details: Challenges Reading IFRS 17 Statements
With the first series of annual statements published under the new IFRS 17 standard, is the new standard achieving the desired transparency and comparability, or is variability undermining these objectives?
The Devil in the Details: Challenges Reading IFRS 17 Statements
Del dicho al hecho : desafíos al leer informes NIIF 17
Reseña: Este artículo da una descripción detallada de los hallazgos de la primera serie de estados anuales publicados recientemente en todo el mundo, siguiendo la norma NIIF 17. Dada la naturaleza basada en principios de la nueva norma de seguros, se esperaba que se aplicara una amplia gama de métodos diferentes. Esta variabilidad fácilmente podría poner en peligro dos de los objetivos fundamentales de la norma: la transparencia y la comparabilidad. Los autores examinan los informes anuales de 20 compañías de seguros a nivel mundial, incluidas AEGON, Allianz Group, Aviva, AXA, Abu Dhabi National Takaful, BNP Paribas Cardif, China Life, CNP, Generali, Manulife, Munich Re, New China Life, Old Mutual Limited ( Sudáfrica), Phoenix, Ping An (China), Prudential plc, QBE (Australia), Sun Life, Samsung Life (Corea) y Tawuniya (Arabia Saudita).
https://documentacion.fundacionmapfre.org/documentacion/publico/es/media/group/1125129.do
What is Generative AI? Understanding Its Distinction from Traditional AI
What is Generative AI? Understanding Its Distinction from Traditional AI
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Shasat Research Desk | May 2024 | Enterprise AI · Generative AI · Financial Services · RegTech |
8 min read |
As Artificial Intelligence reshapes the global economy, a new and fundamentally more powerful class of AI has emerged. Generative AI — the technology behind large language models, image generation systems, and autonomous reasoning tools — does not simply analyse data or follow rules. It creates. It writes, reasons, synthesises, and produces outputs that did not exist before. For financial institutions, this distinction has direct consequences for risk management, regulatory compliance, investment strategy, and the future of work.
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$1.3T
Projected GenAI
Market by 2032 |
40%
Finance Tasks
Automatable by GenAI |
3×
Faster Compliance
Checks vs Manual |
60%
Firms Actively
Piloting GenAI in 2024 |
Traditional AI vs Generative AI: The Defining Distinction
For most of its history, artificial intelligence operated within carefully defined parameters. A credit-scoring model learned from historical loan data to predict default risk. A fraud-detection system identified anomalous transactions by comparing them against known patterns. A regulatory reporting tool extracted structured data from defined fields and formatted it for submission.
These are genuine achievements. Traditional AI systems have saved financial institutions billions and materially improved risk management across the industry. But they share a fundamental constraint: they can only work with what they have been explicitly trained to do. Present them with a scenario outside their training distribution and they fail — or produce a confidently wrong answer.
Generative AI breaks this constraint entirely. Instead of learning a set of rules, it learns the underlying statistical structure of vast amounts of data and uses that understanding to produce entirely new outputs — generalising from what it has learned to address problems it has never encountered before.
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How Generative AI Works: Deep Learning and Probabilistic Modelling
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Neural Network Architecture
At the core of every Generative AI system is a deep neural network — typically a transformer — trained on billions of data points. Rather than being told what to look for, these networks learn statistical relationships across the entire training corpus, producing outputs that are contextually coherent and technically grounded across domains they were never explicitly programmed for. |
Probabilistic Output Generation
Unlike deterministic algorithms, Generative AI models sample from a probability distribution to generate responses — giving the technology its characteristic fluency and adaptability. Modern financial deployments use retrieval-augmented generation (RAG) to ground outputs in verified data sources, substantially reducing hallucination risk in regulated environments. |
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“Where traditional AI answers questions, Generative AI creates answers that did not previously exist — and this distinction has profound consequences for how financial institutions manage risk, monitor compliance, and develop competitive strategy.” Shasat Market Intelligence · May 2024
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Real-World Applications in Financial Services
Generative AI is moving rapidly from pilot to production across three core domains in financial services — each presenting distinct use cases, measurable efficiency gains, and governance considerations that require active management.
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Finance & Investment
Quantitative strategy generation, portfolio optimisation, earnings analysis, and investment research synthesis at a speed no human team could match. |
Operations
Intelligent automation of knowledge-intensive tasks, AI-powered customer service, production scheduling, and operational decision support across sectors. |
RegTech
Regulatory text analysis, compliance obligation mapping, automated reporting, and continuous horizon scanning across IFRS, Basel IV, GDPR, and ESG frameworks. |
Generative AI in Finance and Investment
The finance sector has moved faster than almost any other industry to adopt Generative AI. The combination of vast data volumes, complex analytical requirements, and high-value decisions makes it an almost perfect fit for what the technology does best. Hedge funds and asset managers use Generative AI to synthesise macro data, earnings transcripts, and alternative data into actionable investment theses at a speed no analyst team could replicate.
Citadel, one of the world’s largest hedge funds, has deployed Generative AI to identify alpha signals across vast, heterogeneous datasets — developing quantitative trading strategies that outperform traditional factor-based approaches in several key market segments.
Financial institutions are also deploying Generative AI for proactive risk management. By simultaneously analysing historical loss events, market stress scenarios, and macroeconomic indicators, these systems identify emerging risk concentrations well before traditional early-warning systems would flag them — giving risk managers time to act rather than simply respond.
Generative AI in Operations
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Case Study — Manufacturing
Siemens employs Generative AI to optimise production schedules in real time across global facilities, proactively identifying bottlenecks and reducing unplanned downtime — delivering measurable improvements in throughput and asset utilisation. In manufacturing, production optimisation algorithms analyse real-time sensor data and historical performance metrics simultaneously, enabling interventions that traditional rule-based systems would miss entirely. |
Case Study — Banking Customer Service
Bank of America’s virtual assistant Erica, powered by Generative AI, has handled over one billion client interactions — delivering personalised financial guidance and streamlining everyday banking at a scale no human team could support. Intuit’s deployment of GenOS across Credit Karma, QuickBooks, and TurboTax further demonstrates how Generative AI is being embedded into the core of everyday financial platforms used by millions globally. |
Generative AI in Regulatory Technology
Regulatory technology represents one of the highest-impact applications of Generative AI for financial institutions. The volume and complexity of requirements — spanning IFRS 17, IFRS 9, Basel IV, GDPR, MiFID II, and rapidly evolving ESG disclosure frameworks — make manual compliance monitoring both costly and inherently error-prone.
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“Generative AI execution in financial services now requires detailed coordination between finance, technology, legal, and compliance teams. Understanding governance requirements — from model validation to explainability and audit trails — has become a core execution risk, not a procedural formality.” Shasat Market Intelligence · May 2024
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JPMorgan Chase uses Generative AI to analyse regulatory texts across jurisdictions, map compliance obligations to internal policies, and automate compliance checks — significantly reducing the time required to assess the impact of new regulatory requirements and ensuring consistent standards across the organisation.
The shift is from reactive compliance to continuous, proactive horizon scanning. Generative AI monitors regulatory publications in real time, drafts impact assessments for human review, and maintains an up-to-date compliance picture across every relevant jurisdiction — transforming the compliance function from a cost centre into a genuine strategic asset.
What is Driving Generative AI Adoption in Financial Services
Several structural forces are accelerating Generative AI adoption across financial institutions, moving it from innovation agenda to operational imperative.
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Data Volumes
The exponential growth of unstructured data — earnings calls, regulatory filings, market commentary, client communications — has created demand for AI systems capable of synthesising information at scale. |
Regulatory Complexity
The expanding body of financial regulation across IFRS, Basel, ESG, and AML frameworks creates compliance burdens that manual processes cannot efficiently absorb. Generative AI offers a scalable solution. |
Competitive Pressure
Early movers in Generative AI adoption are demonstrating measurable advantages in cost efficiency, analytical depth, and speed of decision-making — creating urgency for peers to accelerate their own programmes. |
The Road Ahead: Integration, Not Replacement
The most important thing to understand about Generative AI in financial services is what it is not. It is not a replacement for human expertise, professional judgement, or ethical oversight. It is a tool — an extraordinarily powerful one — that expands what skilled people can achieve.
For financial institutions navigating IFRS 17, IFRS 9, Basel IV, and evolving ESG disclosure requirements simultaneously, Generative AI offers a compelling lever: the ability to dramatically expand analytical capacity without a proportional increase in headcount — freeing expert resource for the strategic work that creates real competitive advantage.
Those organisations that begin building the internal literacy, governance infrastructure, and technology foundations now will be significantly better positioned as the technology continues to mature. Those that wait risk a compounding capability gap that becomes progressively harder to close.
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Shasat Consulting — Enterprise AI Practice
Shasat’s Enterprise AI & Intelligent Automation practice helps financial institutions design compliant, auditable, and commercially impactful AI strategies — from governance framework design through to model validation and implementation. |
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