IFRS 17 Challenges – Part 1
Data Preparation and Input
The real challenges in IFRS17 are not accounting or actuarial topics, but essentially related to data, as the new standard requires a much finer granularity and enforces very strict data management controls.
This disintegrated data situation is often the case for many insurance companies, often because IT systems have grown in silos. This makes data exchange a laborious manual task, as granularity and data language must be aligned to produce the required reports. Therefore, having professional data integration and solid business processes is key.
First of all, one has to think about granularity. The main data granularity is coupled with the company hierarchy such as subsidiaries in different countries and divisions. Hence this defines at which level data are provided. Nevertheless, it all depends on how actuals were stored in the past – aggregated or on policy level, the most granular data. This depends on your current data management policy and systems. So, you can’t change past data and have to get along with the actuals. But you can affect the granularity of the expected cash flows. There might be a significant effort necessary, but this can be worthwhile and crucial for the upcoming accounting years. Additionally, you profit from far more flexibility in terms of setting the appropriate level of aggregation.
Different aggregation levels for actuals and expected cash flows are a common hurdle (see example). Thus, you need to bring your data to the same level. If you want to use the more granular level, you have to apply an allocation key on actuals to distribute the amount reasonably to each policy (Option 1). This is quite complex and not always easy to fulfill. Option 2 – bringing the expected cash flows to the same level as the actuals is comparatively easy. The grouping follows the hierarchical structure.
Data analysis and data quality
It is an illusion that your captured data is 100% correct and no analysis as well as quality checks are required. Therefore, it is crucial to validate your data before you start calculating. It will save from last minute troubleshooting and bug fixing which is nerve-racking and cost-intensive. Within Systemorph’s solution data cleansing is handled outside the system. This approach assures wrong data won’t be uploaded. Your system just contains correct data. Furthermore, it prevents you from erroneously using false data for your calculation. In order to achieve such a clear state, soft and hard validation rules are essential. Hard rules filter fatal errors out. Hence these ones need to be checked manually and have to be gathered again. Soft data validation rules cause for instance a transformation to proper data entries. This approach ensures high data quality.
Storing and Versioning
The IFRS17 standard will also impose strict requirements in audit trails, data lineage and versioning, which is an extra challenge to most of the existing systems. Only few technologies can properly deal with such requirements. The technology for IFRS17 implementation should not only perform the calculations and reports, but also guarantee that all data states are captured appropriately. This allows easily to retrace every data version. Versioning is not only important from a compliance perspective. It grants data storing in a well-organized and structured environment. Only in this way, a proper comparison among different years can be efficiently handled. We have observed when finally dealing with real data – actuals and expected cash flows, a decent data preparation and input decides about success of your IFRS 17 implementation.
Data work is completed and now you are ready to start with the “actual work” of IFRS 17. You implement your methodology and get a real impression about the magnitude of the IFRS 17 impact. But what if your results do not match your expectations based on the UoA you decided on or you do not know what UoA is best for your company? Find out more in our next article!
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