International Financial Reporting Standard (IFRS) 17 Insurance Contracts is the International Accounting Standards Board (IASB) accounting standard for insurance contracts that replaces IFRS 4 Insurance Contracts. The IASB discovered that the existing diversity in the accounting of insurance contracts, as permitted by current IFRS 4, made such comparisons difficult for investors and other stakeholders. Furthermore, the existing disclosure requirements did not provide sufficient information to assess insurers’ financial position, profitability, and risk exposures.
IFRS 17 is intended to allow market participants to assess and compare insurers’ financial position, performance, and risk exposures across countries and sectors. It specifies how insurance contracts should be reflected on an insurer’s balance sheet and profit and loss account. It also specifies how to value insurance contract liabilities and how to present and disclose information about them.
By promoting internationally comparable accounting practises and increasing transparency in the insurance sector, IFRS 17 is expected to make a significant contribution to financial stability. IFRS 17 establishes a comprehensive framework for the accounting of insurance liabilities and should contribute to providing a fair view of insurance corporations’ financial position and performance.
IFRS 17 must be used in financial statements for annual reporting periods beginning on or after January 1, 2023.
Data integrity is one of the most significant risks for insurance companies as they prepare to implement IFRS 17 standards within their organisations. Because so much is riding on the accuracy of data, boards and supervisors are now required to provide accurate, relatable, verifiable, and complete information. Any mistakes in this area can have serious reputational, legal, and financial consequences.
Because data integrity risk is so detrimental to organisations and data-driven processes, a number of strategic measures must be implemented to mitigate these threats. However, it is nearly impossible to reduce data integrity risk with a single approach, making it preferable to employ a combination of strategies. Some of the most effective methods for reducing data integrity risks are:
Promote a Culture of Integrity – Promoting an integrity culture reduces data integrity risk in several ways. It aids in keeping employees honest about both their own work and the efforts of others. Workers in a data-driven culture are also more likely to report instances where others take shortcuts or fail to fulfil their responsibilities in the many different aspects of data integrity.
Implement Quality Control Measures – Specific people and processes are put in place to ensure employees are working with data in accordance with security and data governance policies.
Create an Audit Trail – An audit trail is a particularly effective mechanism for reducing the risk of data integrity. Audit trails are essential for learning what happened to data at each stage of its lifecycle, including where it came from and how it was transformed or used. Understanding these particulars can help to ensure regulatory compliance.
Develop Process Maps for All Critical Data – Creating process maps for critical data is an important part of governing how, by whom, and where data is used. Organizations gain greater control over their data assets by mapping these processes, ideally before data is used. These maps are critical for implementing proper security and regulatory compliance measures.
Eliminate Known Security Vulnerabilities – To help minimise data integrity risks associated with protecting data assets, it is mandatory to eliminate security vulnerabilities. This method of risk reduction necessitates subject matter expertise for identifying known security vulnerabilities and implementing mitigation measures. This work also necessitates the use of technology such as security patches.
Follow a Software Development Lifecycle – Following a software development lifecycle is a fundamental method of governing data as it travels through the enterprise. These development lifecycles are critical for comprehending the various governance protocols required for data management in accordance with regulatory and security requirements. This method is essential for understanding where data is and how it is deployed, and then using that knowledge to create sustainable practises.
Validate Your Computer Systems – Planning, mapping, and dictating what is supposed to happen with data is meaningless unless IT systems and employees are tested, validated, and revalidated on a regular basis. For example, IT teams may be tasked with mapping source fields to target systems based on the metadata of previous mapping constructs. The only way to be certain that this process is carried out is to test and validate the computer systems involved in these procedures to see if the data supports employee action.
Implement Error Detection Software – Anomaly detection software and services can assist in monitoring and isolating outliers, determining why errors occurred, and demonstrating how to avoid them in the future. This entire process is critical for keeping data integrity risk under control.
Organizations can stay ahead of these data integrity risks by relying on cloud integration platforms that include data governance and data stewardship capabilities. These all-inclusive solutions accelerate big data integration, implement governance measures for data lakes, and automate critical aspects of metadata management. They also have measures for troubleshooting and monitoring data management aspects that are critical to regulatory compliance, security, and data quality.
With more and more reliance on data quality, effective reporting under IFRS 17 will heavily depend upon the type of culture built around data capture, maintenance, management, security and confidentiality.
Although, in the past, insurance companies have never felt the need to focus on operational risks around data management, IFRS 17 is a fast approaching standard that may lead to reputational and financial risks if data controls are not developed, tested and deployed in time.
Incorrect, incomplete or otherwise unverifiable reporting can spell disaster for an insurance company not only from board members and shareholders but supervisors and re-insurers.