IFRS17 is a new accounting standard from the International Accounting Standards Board (IASB) – the first in almost 15 years. The objective is to create standardisation in insurance company financial statements and align them with IFRS principles utilised in other industries.
It provides guidance on how cash flows should be treated within an insurance organisation, specifically how accounting provisions and reports should be maintained for policy level cashflows (in and out).
Compliance will require that insurers implement a robust solution that aligns their accounting entries and their reports to the new standards.
Solving the IFRS 17 data challenge for life insurance leaders
IFRS17 is being billed as the biggest set of accounting standard changes to hit the insurance industry in 15 years – posing significant data challenges to organisations attempting to achieve compliance ahead of the looming 1 January 2023 deadline.
The sweeping new set of standards will require calculations to be made on every policy, including projections on future premiums, claims and reinsurance. Policies will need to be evaluated in terms of their profitability. And judgements will have to be made on how policies fit into portfolios, groups and cohorts.
To accomplish that, organisations will need to be able to:
- Access all of their policy data in their insurance, accounting and actuarial systems
- Clean, convert and extract important values from data at the right level of granularity
- Implement an automated process for moving data through transformations from source to reporting.
For most insurers, meeting these challenges will require new ways of thinking, technology and expertise.
Data challenge #1: Access to all data
The first challenge is that the data needed to generate IFRS17-compliant reports resides in different systems in the organisation – including the policy administration, actuarial and accounting systems. The disparate sources and the complexity in mapping mean that, at its core, IFRS17 reporting becomes a “data” problem.
Gaining access to the data in those systems is complicated by the fact that almost all life insurance companies are working off multiple legacy back office systems that can be decades old. These systems present obstacles to easy data access. They may be disconnected and don’t allow easy data retrieval. This creates data silos and shadow IT solutions because they often require the application of unique manual processes to access data – which is inefficient, expensive and error prone.
In too many cases, just accessing the necessary data is a form of technological archaeology – painstaking manual work that is more complex than it needs to be.
The amount of data migration and conversion required to access what will be needed to comply with IFRS 17 is staggering. And, as with any migration, using traditional methodologies for conversion creates significant risk in a project that has a tight and non-negotiable deadline.
The risks generated by old ways of migrating data—loss or corruption of data, fear of a data breach, maintaining parallel systems—are big enough that they have long been one of the main reasons why carriers haven’t modernized their policy administration systems.
Data challenge #2: Standardisation and granularity
Another key data challenge is that, often the data is not organised in a way that can be easily consumed and processed. Data is not necessarily standardised or interrelate-able across systems, making it difficult to merge into a single usable data set without creating data integrity issues.
And that’s just the data in the core systems. The matter is complicated by the fact that much of the required data resides in spreadsheets and other ad hoc data sources. Many actuaries, for example, work off excel templates that use data manually sourced from systems other than their own and aren’t easy to integrate with.
Data standardisation and the ability to extract the values you require at the right level of granularity, are an area of weakness for many companies – especially those who are multinationals or have engaged in a lot of M&A activity over the years. The problem grows when you consider that some life insurance contracts last almost a lifetime and the decades old data within may be structured in different formats over time.
A company’s enterprise-wide data set is not likely to have the consistency needed for use in IFRS 17 calculations, unless they have been extraordinarily vigilant in maintaining a strict standard over long periods of time.
Data challenge #3: Defining the data process
The third data challenge created by IFRS17 is defining the process for moving data from source to reporting. Under previous regulations, that process was a lot more straightforward. Since revenue was recorded by posting premiums directly to the P&L, there were relatively few steps involved.
Under IFRS17, data must be accessed, then processed – transforming as it passes between different systems in order to perform the calculations necessary to generate compliant reports. The steps in the process must be well defined, and most importantly, they must be automated.
IFRS17 compliance will create nightmares for organisations that end up introducing a host of new manual process that must be completed to achieve compliance. The right automated process should enable staff to focus on high-value tasks like analysis and providing strategic input to different product lines.
Implementing the most effective and least disruptive process will require insurers to choose the right technology and conversion methodologies. This will ensure steps are automated, data risk is eliminated, and the end product is useful, not just for required reports, but to improve company-wide decision-making and accelerate innovation.
Wrap up – the IFRS 17 solution
What insurer’s need to solve their IFRS17 data challenges is the right set of tools that allow them to source their data in an efficient and effective way. These tools enable a company to manage the problem of how their data is interrelated data using an industry-standard data model.
This kind of data model allows an organisation to evaluate where each of group of data can be sourced from and access it at whatever level of granularity each system provides.
Through data-quality metrics and data massaging techniques, it enables each instance of source data to be related to the others. That way, widely disparate types of data that started at different levels of granularity can all be used for analysis.
Overcoming the significant data challenges presented by IFRS17 will require the implementation of a solid insurance data foundation, good data integration techniques and the help of the right data integration partners.
For insurers already thinking about the need to modernise their policy administration systems to accelerate innovation, increase operational efficiencies and provide superior customer experiences, IFRS17 may be the tipping point to making the decision to implement a rules-based PAS.