IFRS 17: Choosing the Right Measurement Models for Success

‒Marvin Yorke, CPA, CGA, FCCA (UK) | President Marvin Yorke Consulting

Choosing the best IFRS 17 approaches for measuring liability in an insurance company’s products is a significant challenge in itself. But, accessing and analyzing all the data necessary to make the right decisions can present an even bigger set of issues.

If insurers cannot find a way to convert and standardize their data to a level where they can perform the required IFRS 17 calculations, generating the necessary compliance reports and disclosures will be hard—if not impossible.

At its worst, failure to meet the data challenges inherent in determining measurement models can mean failure to achieve IFRS 17 compliance.

Choosing the right measurement approach is a data problem #

The three measurement approaches allowed by IFRS 17 describe how a life insurer accounts for the fact that they have received premiums, but the policy coverage extends into the future. Each of the three approaches--the General Measurement Model (GMM), the Premium Allocation Approach (PAA) and the Variable Fee Approach (VFA)--are used for different types of products. And, in the case of life insurance organizations, are used to guide how companies will deal with disclosure of Liability for Remaining Coverage (LRC).

Policy data will need to be analyzed carefully to allow the company to make reasonable judgement calls on which model to use. And that means that all data must be accessible, readable and standardized into a format suitable for performing the needed analysis. Not something all insurance companies, especially those saddled with multiple legacy systems, are easily capable of.

What are the challenges with each measurement approach? #

Each of the three different measurement approaches present their own challenges.

1. GMM #

The General Measurement Model is the default approach that any insurance company can use. It’s appropriate for longer-term contracts like Life Insurance or Mortgage policies, which cover a specified risk over an extended period.

Challenges

The main issue companies face when using GMM, is making the calculation of the time value of money on all cashflows and then applying risk adjustments. Most insurers are beginning to use GMM models but have limited prior experience working with similar models—which creates challenges around determining how to work with the necessary data.

2. PAA #

The Premium Allocation Approach is a more simplified approach than GMM. It can be used for short-term contracts—those providing less than 1-year of coverage. Complexity can result from this approach, because it can also be used for very specific longer-than-a-year contracts if the insurer can show that PAA and GMM approaches deliver comparable results.

Challenges

Proving that the results from the PAA model and GMM models are similar, is also a data challenge. Data must be accumulated, and analysis performed, to show, conclusively, that the derived outcomes of using one model versus the other are the same.

3. VFA #

The third approach is called the Variable Fee Approach (VFA), and is always required for “Direct Participating Contracts”. This measurement approach is mandated for contracts involving segregated funds, unit linked contracts, etc. and can be used for any insurance contract that involves investment components.

Challenges

In the VFA model, the same data challenges faced when using the GMM apply, but, additional data and calculations are needed to figure the roll-forward handling of the Contractual Service Margin. In short—more calculations mean more data headaches for those lacking the right data processing tools and procedures.

Data defines how contracts are grouped #

Data also poses problems when determining what level of aggregation to use for groups and cohorts. Different ways of grouping the contracts might generate distinctly different results, and companies need to identify which aggregation approach is most appropriate for their business. Again—insurers need data to make and support those decisions.

Inefficient data processes create costly inefficiency #

Further, without the use of right toolsets, insurers run the risk of creating inefficiencies in their data collation and analysis processes. Slower processes and increased manual effort mean more time and work required to generate compliance reports.

Those inefficiencies result, not just in higher costs, but also reduce the amount of time highly skilled employees are able to devote to tasks that generate much more value.

Putting it all together #

IFRS 17 provides for flexibility in choosing which method to employ to measure policy liabilities. But choosing the best approach is a data-driven challenge. And that means, insurance companies need to be able to access all the relevant policy information across core insurance, accounting and actuarial systems.

That data must be converted so that it is searchable and relevant values can be extracted for use in analysis. Without automated data processes to enable this work, IFRS compliance will require more effort and create a drag on business-as-usual processes.

Once you have solved the data challenges standing in the way of choosing the right measurement approaches, the final step in the IFRS 17 journey is to generate compliance reports—the topic of our next article in this series.

Get a free assessment of the current state of your IFRS-17 progress.