Insights From the 2024 Survey of General Insurance Reserving Practices in Australia

To support this objective, the General Insurance Practice Committee (GIPC)’s GI Reserving Working Group conducted a short survey in June 2024 to:

• capture current reserving practices and help practitioners benchmark their methodologies against industry norms;
• gather insights on the appetite for and barriers to adopting alternative approaches that could benefit both practitioners and policyholders; and
• support the GIPC in evaluating and potentially refining professional standards and education programs.

This survey was distributed to all general insurance Fellows in Australia through an Institute’s email.

Survey overview

The survey comprised of four sections:

• general reserving approaches;
• large claims reserving;
• catastrophe/event loss reserving; and
• adjustments for AASB 17.

The response rate was quite low, with a total of 29 respondents participating in the survey and about half providing answers to the majority of the questions.

More than half of the respondents were Reserving Actuaries or held equivalent roles, while a third were Chief or Appointed Actuaries. Despite the response rate, the Working Group feels that the survey has provided a useful snapshot of current practice and some specific insights of note.

Key findings

General reserving practices

GI actuaries shared their experience with various reserving methods across broader insurance classes (Figure 1).

Aggregate-level reserving is more commonly used than granular-level reserving. The Chain Ladder Method and its variants are the most frequently employed techniques, followed by Bornhuetter-Ferguson (BF) and Payment-Based Methods. The BF Method, in particular, is predominantly used for low-frequency, high-severity classes, which also saw the highest response rate.

Figure 1 – Overall reserving methods currently used in each class

60% of respondents expressed general satisfaction with the methods they use. The primary factor influencing model selection is predictive performance, with GI reserving actuaries selecting models based largely on their alignment with portfolio characteristics. Team capabilities, time and budget are secondary considerations in comparison.

GI actuaries are generally satisfied with the current modelling, especially due to their sufficient performance, ease of use, ease of overlaying judgement and ability to explain results.

They expressed dissatisfaction with the level of automation and efficiency in the current processes. Many highlighted the over-reliance on Microsoft Excel and the associated complexities caused by linking multiple spreadsheets.

When describing their vision of the ideal reserving model, respondents emphasised several key features:

• easy interpretation of data, allowing users to quickly understand its implications;
• a balance between complexity and accuracy, avoiding over-parameterised models;
• flexibility to incorporate judgments and adjustments to accommodate various stakeholder inputs;
• ability to integrate with pricing models; and
• reduced reliance on Excel spreadsheets.

Large claims reserving

75% of respondents model large claims separately from attritional claims and more than half (56%) aggregate large claims across similar classes. Frequency and severity analysis is most commonly used for large claims reserving, as shown in Figure 2.

Figure 2 – Large claims reserving methods currently used in each class

Actuaries described their ideal model for large claims as one that can easily track the impact of assumptions compared to historical data. A key feature would be the ability to predict which claims are likely to become large, enabling proactive management. Over time, such a model would be calibrated to improve its predictive accuracy based on new data.

Catastrophe/event loss reserving

Most respondents (78%) model catastrophe/event losses separately.

In the early stages following an event, actuaries typically make volume and size selections based on insights from similar historical events, overlaid with development patterns from past data.

Expert input from the perils and claims teams also plays a significant role in shaping initial case estimates. Claims are often analysed at a granular level, considering damage type and historical similarities.

As new experience data becomes available, there is a shift from relying on historical patterns to focusing on broader peril trends and emerging claims data. The ultimate expected cost is continuously refined based on updated information, though the overall approach remains consistent with a heavy reliance on claims management expertise. Scenario testing and simulation are commonly used to model the uncertainties and variability inherent in the estimates.

The primary criteria for choosing catastrophe models are ease of use and the ability to overlay judgments. However, while actuaries are generally satisfied with the models’ ability to explain results, many expressed dissatisfaction with their predictive performance.

Impact of AASB 17

Several respondents shared their experience with the implementation of AASB 17. Only one respondent noted a material impact on reserving practices, citing changes to discounting methodologies and differences in risk adjustment compared to risk margins.

This respondent also highlighted the significant time and effort required for implementation, noting that management reports continue to be prepared using the previous basis, as it remains more useful for internal purposes at this stage.

Conclusion and next steps

The survey results highlight several key areas for improvement in reserving practices. There is a clear desire for more automated, flexible and integrated reserving models that can accommodate evolving data while retaining explainability and ability to overlay expert judgement.

In light of these findings and in response to respondents’ feedback, the GIPC could advocate for more granular industry data, along with resources such as papers and presentations on emerging trends and risks. There is also a need for practical guidance on the application of machine learning techniques in reserving practices.

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