Outlook on Financial and Actuarial Digitalization in the Post-IFRS 17 Era

Zhou Hai March 2025

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Chinese Version

This article is based on the 2024 Society of Actuaries (SOA) and China Association of Actuaries (CAA) professional development online training course “Life Insurance Companies IFRS 17 Implementation Experience Sharing.” It analyzes strategies for insurance companies faced with implementing new accounting standards for insurance contract, examines effective IFRS 17 implementation and provides an outlook for financial and actuarial digitalization in the post-IFRS 17 era.

IFRS 17 System Construction Framework

The framework for IFRS 17 system construction can be divided into seven parts:

1. Source system and actuarial software/tools transformation: Updating and adjusting existing system data and the actuarial software or tools.
2. Data processing platform: Centralized data processing by integrating data modeling and management.
3. Allocation module: This module handles the allocation of expenses, investment income, special reserves and outstanding reserves.
4. New measurement function construction: Building new measurement functions according to IFRS 17, including full cycle rolling calculations for BBA (also known as GMM), VFA and PAA models.
5. Accounting sub-ledger construction: Establishing a new accounting sub-ledger system to support both actual cash flow voucher and measurement result conversion and multi-standard reconciliation.
6. General ledger transformation and report generation: This includes meeting the requirements for primary statements, notes and management reports under the new accounting standards, including IFRS 9 and IFRS 17 items, as well as potential profit source analysis and forecasting reports.
7. System management layer: Building a comprehensive automated platform covering data synchronization, backup, archiving, disaster recovery, system control scheduling and data quality management.

I believe that the actuarial team’s full participation is crucial throughout this framework. They need to sort historical data, products information and actuarial models; track expenses; and compare them with existing reserve measurement results and future projections. This comprehensive approach could enhance data quality and the application of actuarial techniques.

Implementation Process of the New accounting Standards for Insurance Contract

The process of IFRS 17 system construction and the implementation rhythm of medium and large insurance companies can be divided into six stages, each with different risk points:

1. Requirements analysis and design
2. System implementation
3. Public capabilities and nonfunctional requirements design
4. Transition period handling: Cleaning up historical policy data, including business, accounting¹, and general ledger data, as well as calculating asset and liability items for the transition date.
5. Testing: Including system integration testing (SIT) and user acceptance testing (UAT).
6. Trial run.

Key risk points and considerations for each stage:

1. In the process of full-scale requirements analysis, it is necessary to directly explore the source data of the company’s production environment and model the data. At the same time, it is essential to communicate with management and auditors in advance during modeling to avoid repeated modifications during implementation.
2. In the implementation phase, it is necessary to comprehensively coordinate the overall plan and progress and allocate sufficient personnel. Actuaries may face multiple parallel lines of IFRS 17 system construction, various measurement work and other daily work. Therefore, during the implementation process, it is necessary to plan and control the progress overall, allocate sufficient resources and avoid project delays.
3. Regarding public capabilities, planning margins based on data volume and financial monthly closing timeliness is necessary to avoid situations where performance cannot be optimized during implementation and forced demand changes. In the entire design process, pay close attention to the closing part, identify the parts that require manual intervention or manual breakpoints in advance, and then improve the efficiency of system calculation as a public capability or nonfunctional requirement.
4. Processing of the transition period. The transition period is a complex problem for every insurance company doing IFRS 17 projects, mainly because the company’s management hopes to achieve better initial financial results through calculations during the transition period. This process usually requires a large number of repeated calculations. Therefore, insurance companies should start the data collection and calculation work for the transition period as soon as possible and reserve more time for the company’s management to do repeated measurement work.
5. In the end-to-end UAT testing phases, insurance companies should directly access production data for verification after the connectivity test (also known as SIT testing) is completed, avoiding virtual data In IFRS17 implementation process, when real business data and financial data are poured into the system for repeated verification, various unexpected results can be discovered, indicating deficiencies or defects in the business requirements and system architecture design stages.
6. Regarding trial operation and acceptance work, by comparing the trial operation results with the reports of the comparative period in parallel, the company can verify the accuracy of the new system and generate comparative data under the new and existing accounting standards this also allows business departments to accumulate valuable experience and provide strong support for future optimization and improvement while reducing the risks after official implementation.

Notes on Implementation of New accounting Standards for Insurance Contract

Management support. Although the project implementation process will involve related departments such as the actuarial, finance, and IT departments, it requires the company’s management to provide sufficient support and attention to coordinate cross-departmental operations and collaboration.

System integration. The seven system integration modules contain complex data flow interactions and underlying technology stack matching scenarios. Therefore, integration should be designed and finalized as soon as possible in the early stages of the project to avoid repeated modifications.

Test cooperation. The IFRS 17 business logic is complex, and the business and IT departments must deeply understand the requirements and actively participate in the testing process. Testing requires experience analysis and evaluation, and the actuarial department should conduct unified verification at a higher level based on the coordination and cooperation of various departments, such as product design, to ensure that the test results can meet expectations.

Configuration of environmental resources. From the IT perspective, the server procurement process can be lengthy when selecting the company database and products. Therefore, it is best to prepare in advance to deal with problems such as insufficient supplier capacity and peak centralized procurement.

Data quality. The IFRS 17 project involves many business systems, and the system data is huge and has a long history. The relationships between various tables are complex.

Tight time. The IFRS 17 project implementation plan is tight, the workload can be heavy, the difficulty is high, and the fault tolerance rate is low. It appears to me that time will go fast as we approach 2026 when the new accounting standards will be fully implemented. To complete the system’s construction, sufficient time must be reserved to deal with various abnormal situations during the system implementation process. For example, changes in new business forms, new regulatory requirements, and adjustments to the company’s internal management or technical routes.

Change management. Change management aims to optimize system construction and strive to create high-quality projects. Based on the pursuit of perfection in details and special scenarios, it is also best to carefully grasp the schedule to balance the time plan and demand changes and ensure the smooth progress of the project.

Scheme and initial OBS number adjustment. The scheme for adjusting the initial OBS number needs to be carefully considered so that the company’s management can achieve the desired initial good financial results after the transition period, laying a solid foundation for implementing the new accounting standards. However, adjusting the initial number may affect the project plan of the comparative period² report running process, increase the complexity of system construction, and lead to project delays or even failures. Therefore, it is best to comprehensively assess risks and ensure that the adjustments are consistent with the project’s success.

Implementation Coping Strategies for Property Insurance

The main challenges faced by property insurance companies in handling business include the processing of massive policy data and the complexity of reinsurance contracts. For massive policy data, property insurance companies need to rely on new technologies and data processing methods, compress processing time through high-performance servers, and ensure the accurate processing of business data and cash flow data. In addition, reinsurance business, as a key area for property insurance companies, its business volume (whether assumed or ceded) is comparable to direct insurance business.

Given that reinsurance systems may be less mature than direct insurance systems, property insurance companies need to pay special attention to the completeness and accuracy of reinsurance data, solve problems in the circulation of business systems, and accelerate the sorting out of historical reinsurance contract terms, and discuss and optimize the processing methods of provision and settlement data, striving to optimize reinsurance operating procedures, simplify data processing procedures, and ensure the smooth operation of business and systems.

Consistency Mechanism Design to Guarantee Data Accuracy

The design of the business, finance and actuarial consistency mechanism is particularly complex and crucial in the process of implementing new accounting standards by insurance companies. The new accounting standards introduce the dimension of contract portfolios and require detailed data granularity. Each layer, from the underlying liability and insurance type to the final contract portfolio disclosure, needs to be processed accurately. This includes the breakdown of investment components, the definition of responsibilities in reinsurance and cession clauses, and the breakdown of responsibilities and optional responsibilities in complex product portfolios.

To ensure the reasonableness of external disclosure, companies need to first ensure the consistency of underlying policy contracts and insurance-type data. The requirements of the new accounting standards for data quality are much higher than those of the old accounting standards—especially at the accounting level. Actual cash flow accounting needs to be performed at the main policy granularity and then summarized to the contract portfolio level for CSM measurement. For short-term insurance, although the 1/8 method or 1/24 method is traditionally used to calculate UPR, leading companies have gradually switched to the more precise 1/365 method for individual measurement, further increasing the requirements for data granularity.

During the implementation process, system transformation can be a challenge, and it is necessary to solve the problem that historical or current systems cannot support individual or insufficient granularity of insurance types. At the same time, the issue of business, finance and actuarial consistency has been highly valued under both the new and old accounting standards, and it is necessary to design a reasonable guarantee mechanism to ensure the seamless integration and consistency of business and financial data. In summary, property insurance companies need to plan carefully and implement meticulously when implementing new accounting standards for insurance contract to ensure data quality, meet regulatory requirements and lay a solid foundation for the company’s long-term development.

Because the new accounting standards for insurance contract have a clear switchover date, companies need to deal with historically accumulated receivable and payable account balances, reserve balances and so on. The direct carryover or adjustment of this data to the new accounting standards may lead to unreasonable situations. In addition, historical differences between institutions, insurance types and even account scenarios also need to be adjusted under the new accounting standards to ensure the accuracy of financial disclosure. To address these issues, companies need to comprehensively clean up historical data under the new accounting standard construction environment to obtain a “clean” initial data set for continuous disclosure under the new accounting standards. In this process, the business, finance and actuarial consistency guarantee mechanism can ensure the seamless integration and consistency of business and financial data.

To achieve efficient and accurate data processing, companies also need to consider closing timeliness and the workload of the actuarial department, introduce more automated inspections and hard-dependent upstream and downstream data series design. For example, Accenture divides the entire process into 55 steps, starting with the acquisition of policy and business transaction data from the source, ensuring that data synchronization at the technical level is both comprehensive and accurate, and all business data that needs to undergo actuarial assessment and financial accounting is included in the assessment scope.

Second, the strongly correlated processing of upstream and downstream financial data is also very important. It aims to accelerate the financial monthly closing and actuarial monthly closing processes and ensure data accuracy and reliability through comparison and rationality analysis of actual cash flow and expected cash flow.

Finally, there is the organic integration of actuarial measurement results and financial reports. This process involves preparing the three main financial statements as well as disclosing detailed reserve changes These reports reflect changes in actuarial assumptions and risk adjustments while also including key financial information such as receivable and payable balances. To achieve the organic combination of these two, companies may need to extract and integrate information from multiple data sources, but under the mechanism mentioned above, even if the data sources are different, the consistency and accuracy of the final disclosure results can be ensured.

Responding to Faster Closing Requirements

Compared with the old accounting standards, the new accounting standards increase the linkage with expense and investment data and may introduce new measurement modules, resulting in a three- to nine-day extension of the closing time.

To address this challenge, companies would be best served by identifying the main nodes and manual breakpoints that affect closing efficiency and refine and decompose the newly added task items under the new accounting standards to reduce unnecessary upstream and downstream dependencies, achieve concurrent processing, thereby optimizing system run speed and shortening closing time.

In terms of specific challenges, the first is data checking and abnormal data fixing, which requires the establishment of a full-process or systematic data checking, reporting and automated processing mechanism. The second is model point calculation. The new accounting standards require the creation of model points for new and valid policies at the end of the month. It is recommended to decompose tasks and spread some of the work to the middle of the month to reduce the system burden at the end of the month. In addition, underlying calculations are more time-consuming under the new accounting standards because of the introduction of new liability assessment items and special cash flow calculations. Docking between actuarial assessment software and other systems is also a link with a lot of manual processing, and it is necessary to improve system run time and docking performance. The late acquisition of investment income data and the additional adjustment time that may be caused by the implementation of IFRS 9 are also factors affecting closing efficiency.

Outlook for Financial and Actuarial Digitalization of Insurance Companies in the Post-IFRS 17 Era

From an internal perspective, the data of the new system group will be widely used in key areas such as claim processing, strategic planning, financial report production collaboration, risk management, liability-side cash flow stress testing and asset-liability matching, which will provide strong data support for insurance companies. This data will promote the improvement of internal management efficiency and the scientific nature of decision-making.

From an external perspective, the new system group will help insurance companies optimize product design, customer service and regulatory reporting. As regulatory requirements become increasingly stringent and data granularity requirements increase, the system group integrating business, finance and actuarial data will better adapt to regulatory needs and improve reporting efficiency and accuracy.

In addition, the new system group also provides optimization space for risk management models, reinsurance contract benchmark pricing models, and catastrophe models (for reinsurance companies) while supporting advanced applications such as sales channel management and profit source analysis. These applications all require insurance companies to have comprehensive considerations and a complete framework when building new system groups.

There is a “1+2+n” mode framework that can be referred to where 1 represents the underlying data storage or data management technology base, and 2 includes actuarial data management and data governance processing, which complement each other. This also includes the concept of a “data mart”—the data mart provides a unified underlying data source for the actuarial department, supporting multiple actuarial application directions including assessment, experience analysis and product design.

This unified data source ensures data consistency and reliability, making subsequent experience analysis and value assessment results more credible and providing a solid data foundation for the digital transformation of insurance companies. In addition, actuarial data governance is also an important mechanism to validate data quality, ensuring that the data obtained by the actuarial department truly reflects the actual business situation of the insurance company through consistency checks and other means.

In the “n” part, i.e., the actuarial application level, these applications are essentially secondary processing based on underlying data, providing support for internal management, external reporting, product design and investor management. These applications have unlimited scalability and empower multiple business lines or modules of insurance companies. In terms of technology and data processing, optimizing the technical base is the key to improving data processing capabilities and actuarial work efficiency, enabling the actuarial department to issue reports and analysis results more agilely. Traditional database technology may have difficulty processing massive data sets, and its computing performance and scalability may be poor. Modern databases with massively parallel processing architecture (MPP) and other new technologies can find a balance between processing large amounts of data and tight deadlines, while ensuring data security and integrity.

With the completion of the new accounting standards system group construction, the underlying data preparation zone and the source-aligned data processing zone have provided a solid data foundation for the actuarial department. This data not only supports existing management needs but also strongly supports the design of future new products and the development of management accounting systems. The indicator monitoring under the new accounting standards will be more detailed and transparent, providing better tools for the company’s refined management.

Coping Strategies for Business Side of Life Insurance

Life insurance companies balance multiple complex factors when dealing with financial and actuarial challenges. The financial side faces heavy monthly closing tasks, long chains and the dual pressure of simultaneous implementation of new accounting standards. It is best to optimize technical solutions, reduce dependencies between monthly closing tasks and promote concurrent processing.

The actuarial side focuses on constructing model point data, a basic task with a deep historical accumulation. It requires special personnel and special projects to overcome the differences in statistical calibers between the new and old accounting standards and verification challenges. In addition, the interaction complexity between actuarial software, data platforms and sub-ledger systems needs to be tested in advance to ensure smooth system interaction.

Detailed inspections are also required for data consistency and upstream and downstream linkage. Finally, for the processing of historical policy data with low quality during the transition period, it is necessary to adopt some simplified processes to accelerate the data processing during the transition period, as well as account for the analysis and repair of historical data. In addition, reserve sufficient time for the subsequent report running process during the trial operation.

Statements of fact and opinions expressed herein are those of the individual authors and are not necessarily those of the Society of Actuaries or the respective authors’ employers.

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