Members Only
ERM
Accessible and Practical Risk-Based Capital Systems: IFRS17 PAA in Focus
Advanced solvency frameworks, such as Solvency II, require both a regulatory balance sheet separate from a statutory one and a standardized solvency capital model, imposing high demands that are very challenging in both setup and oversight for regulators and operators in non-mature insurance markets. This paper proposes a simplified risk-based capital scheme based on the Premium Allocation Approach (PAA) under IFRS 17, aligned with the International Association of Insurance Supervisors (IAIS) Insurance Core Principles (ICPs), to provide an accessible and practical solvency framework tailored for such markets. This approach streamlines capital calculations, minimizes data demands, and simplifies compliance processes, making it feasible for insurers and regulators in developing countries to adopt a risk-based regime without the extensive rigor of mature frameworks. As a simpler alternative, the framework is flexible enough to accommodate additional accounting standards, such as GAAPs, making it suitable for a range of regulatory environments. The framework facilitates faster implementation of risk-based regulatory systems, supporting financial stability and growth within different insurance markets. It offers a balanced solution that maintains alignment with key principles of capital adequacy and solvency, while addressing the specific constraints faced by developing nations or small insurers.
Members Only
Consulting
Cyber Insurance-Linked Securities
In this webinar we investigate the feasibility of cyber risk transfer through insurance-linked securities (#ILS). On the investor side, we elicit the preferred characteristics of cyber ILS and the corresponding return expectations. We then estimate the cost of equity of insurers and compare it to the Rate on Line expected by investors to match demand and supply in the cyber ILS market. Our results show that cyber ILS will work for both cedents and investors if the cyber risk is sufficiently well understood. Thus, challenges related to cyber risk modeling need to be overcome before a meaningful cyber ILS market may emerge.
Members Only
AI / Data Science
Measuring Non-Exchangeable Tail Dependence Using MTCM and ATCM
Quantifying tail dependence is an important issue in insurance and risk management. The prevalent tail dependence coefficient (TDC) is known to underestimate the degree of tail dependence and it does not capture non-exchangeable tail dependence since it evaluates the limiting tail probability only along the main diagonal.
To overcome these issues, two novel tail dependence measures called the maximal tail concordance measure (MTCM) and the average tail concordance measure (ATCM) are proposed.
Both measures are constructed based on tail copulas and possess clear probabilistic interpretations, where the MTCM evaluates the largest limiting probability among all comparable rectangles in the tail, and the ATCM is a normalized average of these limiting probabilities.
Members Only
AI / Data Science
IFRS 17 The actuary as a story-teller
IFRS 17: the actuary as a story-teller
In late 2022, European headquartered insurance groups held investor education events to explain the expected impact of IFRS 17 transition on their future results and financial position.
Insurers reaffirmed their previously disclosed strategic direction and targets but indicated some rebasing of key performance measures. Risk appetite and capital management (including solvency ratios) are not affected but changes are expected in return on equity, leverage, and combined ratios. The range of the expected change in key ratios is relatively wide and depends on each insurer’s business mix and the degree of alignment between accounting policy choices and approaches to asset-liability management (ALM).
This webinar session will explore observations from recent non-life IFRS 17 investor disclosures addressing key messages, policy choices, and impact on KPIs.
Members Only
AI / Data Science
Multi State Modeling of Customer Churn
Customer churn, which insurance companies use to describe the non-renewal of existing customers, is a widespread and expensive problem in general insurance, particularly because contracts are usually short-term and are renewed periodically.
Traditionally, customer churn analyses have employed models which utilise only a binary outcome (churn or not churn) in one period. However, real business relationships are multi-period, and policyholders may reside and transition between a wider range of states beyond that of the simply churn/not churn throughout this relationship.
To better encapsulate the richness of policyholder behaviours through time, we propose multi-state customer churn analysis, which aims to model behaviour over a larger number of states (defined by different combinations of insurance coverage taken) and across multiple periods (thereby making use of readily available longitudinal data). Using multinomial logistic regression (MLR) with a second-order Markov assumption, we demonstrate how multi-state customer churn analysis offers deeper insights into how a policyholder’s transition history is associated with their decision making, whether that be to retain the current set of policies, churn, or add/drop a coverage.
Applying this model to commercial insurance data from the Wisconsin Local Government Property Insurance Fund, we illustrate how transition probabilities between states are affected by differing sets of explanatory variables and that a multi-state analysis can potentially offer stronger predictive performance and more accurate calculations of customer lifetime value (say), compared to the traditional customer churn analysis techniques.