The Human Factor: IFRS 17's Toughest Challenge is Predicting People

IFRS 17 is built on a simple premise: an insurance contract's value is based on the future cash flows it's expected to generate. These Fulfilment Cash Flows (FCF) are the bedrock of the new accounting standard. But estimating them requires actuaries to answer a deceptively simple question: how will our policyholders behave over the next 10, 20, or even 50 years? This is where theory meets the messy reality of human nature, creating significant practical challenges.

From Past Data to a Dynamic Future

Traditionally, actuaries have relied on historical data to set assumptions for policyholder behaviour like lapses (when customers cancel policies), mortality (death rates), and morbidity (illness rates). IFRS 17, however, demands a more forward-looking perspective. It’s no longer enough to assume the future will look like the past. We must now actively model how behaviour might change in response to future events, which is far more complex.

Challenge 1: The Crystal Ball of Economic Change

Policyholder behaviour is not static; it's heavily influenced by the economic environment. Consider lapse rates. If interest rates rise sharply, customers holding older savings policies with low guaranteed returns might be tempted to cash out and reinvest elsewhere. A static lapse assumption based on historical data from a low-rate environment would significantly misstate the FCF. Insurers now face the challenge of building dynamic models that link policyholder actions to economic variables like inflation, interest rates, and unemployment—a significant step up in complexity and judgment.

Challenge 2: The Wake-Up Call from Global Events

The COVID-19 pandemic provided a stark lesson in the limitations of historical data. It simultaneously impacted mortality and morbidity, driving up claims for life and health insurers. It also changed public perception of risk, which may have made people less likely to lapse their policies. An event like this renders pre-2020 data less relevant for predicting the near future. This highlights the need for assumption-setting processes that are agile and can incorporate the impact of unforeseen systemic events.

Challenge 3: The Data Dilemma and Granularity

IFRS 17 requires insurers to group policies by profitability (onerous vs. non-onerous) from day one. This means assumptions must be set at a much more granular level than before. But what happens when you have a niche product with a small pool of policyholders? The historical data may be too thin to be statistically reliable. For new products with no history at all, the challenge is even greater. Actuaries must rely more on expert judgment, industry benchmarks, and qualitative analysis, which inevitably invites more scrutiny from auditors and regulators.

Navigating the Uncertainty

Projecting policyholder behaviour for IFRS 17 is less about finding a single 'right' answer and more about building a robust and defensible process. This involves using scenario analysis to understand the financial impact of different behavioural patterns and strengthening governance and documentation to justify the chosen assumptions.

Ultimately, the shift under IFRS 17 is clear. The task has evolved from a largely statistical exercise to one that blends data science, economics, and expert judgment. For finance leaders, understanding that these assumptions are a key source of volatility and subjectivity is crucial for interpreting financial results in the new IFRS 17 world.