Addactis - Comment interpréter et maîtriser vos comptes IFRS 17 ?

IFRS 17 & Data: Key Challenges and Solutions for actuaries and financial teams

17/10/2024

< onerror="this.style.display='none'" />

The implementation of IFRS 17 highlights the role of data in financial reporting for insurance contracts.

Ensuring data accuracy and integration from multiple sources is a significant challenge for actuaries and financial teams. These challenges must be addressed to achieve transparency in financial results.

This article discusses the key data challenges under IFRS 17 and offers solutions for successful implementation and data quality.

IFRS 17 Overview

The implementation of IFRS 17

The implementation of IFRS 17 presents a significant challenge for actuaries, financial teams and management, requiring correct measurement and transparency of financial results.

Keys elements such as Revenue and liability recognition, Evaluation of obligations, CSM, Contract grouping introduce a new level of complexity.

However, this shift also enhances transparency and comparability in financial reporting with a focus on cash flows and more detailed financial disclosures.

IFRS 4 vs IFRS 17

Unlike IFRS 4, which allowed greater flexibility in reporting, IFRS 17 establishes a more structured approach to evaluating insurance contracts.

The role of data in IFRS 17

Data plays a central role in the successful implementation of IFRS 17. Actuarial, financial systems and general management challenge is to ensure correct measurement and transparency of financial results.

It requires complex data processing and transformation of projections; It generates complexity due to the lack of a clear definition of how certain calculations should be performed (non-prescriptive = methodological decisions) and the lack of the necessary granularity in the data.

It implies a strong integration between systems and areas of the company, as well as a significant impact on run times and storage capacity.

Type of data required

Data Management

1. Storage

Data must be stored in secure and accessible systems, ensuring the protection of sensitive information. Using structured databases allows for efficient management and rapid data retrieval.

2. Treatment

Data processing involves cleaning and standardizing information to ensure its quality. This includes correcting errors, eliminating duplicates and standardizing formats, which facilitates more accurate analysis.

3. Analysis

Data analysis is key to the valuation of insurance contracts. Advanced analytical tools enable cash flow modeling, risk assessment and informed decision making. The use of predictive analytical techniques can improve the accuracy of actuarial projections.

Data-related challenges

Data complexity: Diversity and volume of data to be managed

Required level of granularity (policy level) with a requirement for traceability from start (source) to end (reporting).
Different formats, Empty data, Data in the user’s machines and/or different systems per area.
Understanding of data and new criteria, Reviews are required from start to finish of the process, there is no single responsible party.
Change of methodologies (current vs IFRS17) and this does not make the data comparable.
Different currencies (functional vs. reporting). Structure of the one being taken to OCI or P&L.

Systems integration

Systems integration can be challenging as it requires the integration of different systems and data sources: Excel, Access, Text files, cloud etc.

Regulatory compliance and data security

Essential Challenges for IFRS 17 Compliance and Security Integration:

IFRS 17 Compliance

Lack of training in IFRS 17
The current analysis is not the same as that of IFRS 17 with the changes.
Understand the discounts in the LIC.
Updating the databases, this means that the calculation cannot be replicated.

Data Security

Having so much diversity of data with so many different sources and people make it complex to reach a middle ground.
Free” tools such as R, Phyton, etc.
Not having an integrated information system increases the risks of traceability and availability of information.

Solutions and best practices for Data Management

Technologie and tools

Use of software and data management tools

Best practices: Data structured in the same way to make report generation easier. E.g. Same date format
Repository: Avoid having the data on the computer, but on an internal corporate network. Organization of data repository E.g. Established formatting rules
Data management tool: The most common tool is Microsoft Excel, but unless you have programmed macros, it is very difficult to check automatically.
Software usage: It would be ideal to have a software, understanding the capacity of the company. Keys: data management support, Information management, Organization and Traceability.

Data governance

Implementation of policies and procedures for data management

Connection between equipment actuarial team and accounting team: How do I continue? How do I switch from one equipment to another? It is important to have a defined system where all the information is unified, first actuarial, then accounting. Data control policies should be in place, such as validation policies for moving forward. Who goes first, who goes second? IFRS 17 brings them together.

Data Processing: Establish policies prior to analysis. What procedures do I have defined? Who performs reviews? Who gives the go for the next step?

Training and awareness

Importance of training teams in data management

Teams must know the why and why not of the importance of data quality.
Awareness of the effects of the process, from the beginning to the end.
What does your participation involve?
Importance of verification.
If there are defined structures, human error is reduced, it helps to ensure good data quality.

Why improving data management

Improving data management is essential for maintaining accuracy. Manual processes increase the risk of errors and uncertainty.

A structured and well-defined system and/or process allows the traceability of information from start to finish and at the required level of granularity, as this is relevant in obtaining reliable and consistent results.

Having an information management process is important to avoid errors in the data that may lead to the presentation of results that reflect incorrect figures to the market.

Training teams on the standard is not only essential to validate the reliability of the results, but also to understand the company’s financial situation and the variables that have the greatest impact (e.g., estimation assumptions, variation in discount curves, cash flow, etc.).

The quality and accuracy of the data are essential to accurately reflect the financial situation of the insurers.

Implementing effective data management practices enables insurers to comply with IFRS 17, ensuring accurate calculations and informed decisions and optimizing their operations.

Ensuring data quality not only meets the requirements of IFRS 17, but also strengthens strategic decision making within the organization.