December 3, 2018 | Written by: Stephen Wang
Categorized: Banking | FinTech | IBM RegTech Innovations
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International Financial Reporting Standard 17 (IFRS 17) is expected to come into effect in January 2021 (though the IASB, the governing body, has tentatively agreed in Nov 2018 to postpone adoption to January 2022). Applicable to the insurance sector, it is regarded by most companies and consultants as one of the most significant changes to insurance accounting requirements over the past 20 years, with the potential to impact profitability, volatility of financial results, and ultimately the valuation of equity on the balance sheet. In preparation, companies are mobilizing considerable resources and management attention to analyze the new requirements and to manage the implications on their technology infrastructure, operating model and even business strategies over product mix and pricing.
The many implications of IFSR 17
At its core, IFRS 17 is about implementing a set of new standards over how insurers recognize profitability on their underwriting activities and about providing greater transparency into drivers of profitability through time. This contrasts with the preceding standard, IFRS 4, that allowed companies and even different entities within one same company to report with varying methodology and assumptions, leading to challenges in assessing and comparing profitability and financial health across companies.
So how does IFRS 17 encourage commonality in analysis and reporting? Essentially, IFRS 17 mandates a market-consistent approach to liability valuation while prescribing a methodology to immediately recognize losses on contracts that are expected to be onerous, and to amortize the recognition of profit over the life of insurance contracts that are expected to be profitable. Nuances and complications abound, of course, but the key prescribed steps standardize the analytical process from the types of input data required on contracts, through the grouping of contracts, the selection of analytical method, the calculation of profitability and related measures, to the production of financial statements and disclosure requirements.
Some may argue that the mathematical principles within IFRS 17 are familiar territory for insurers, in the sense that the existing capabilities of actuarial systems are by large still leveraged to calculate risk adjustments and to produce cashflow projections, which are then discounted to establish market-consistent liability values and profitability. Where the additional complexity of IFRS 17 culminates is in the large number of dimensions under which these numerical outputs must then be tracked, interpreted and managed. These dimensions include the aggregation of contracts into portfolios, groups and profitability buckets; the different treatment of contracts by type; the need to report distinctly by cohort; and above all the need to analyze changes across time whereby a whole set of other real-world business factors compound to the complexity (for example, experience not being same as original expectations, changes in discount curves, gaps between initial recognition and subsequent recognition, new business, and so on). Mathematically speaking, each dimension is a degree of freedom and like most problems, the level of complexity grows exponentially with the number of degrees of freedom.
Choosing a technology platform for compatibility and growth
The high-dimensional nature of IFRS 17 must be carefully considered by insurers as they define their target operating model and architecture, and as they select their IFRS 17 “engine” that bridges the critical gap between actuarial systems and financial systems. Each of the dimensions will represent a distinct volume of data to be managed separately. Any decision to reduce the resolution on any one dimension can be expected to impact the analytical power downstream and, in the future, as analysts are faced with aggregated “black boxes” into which they have no line of sight. For example, pre-aggregating contracts by product line would limit analysts’ abilities to explain changes in contractual service margin due to differences in experiences across subsets of contracts within.
Our view is that our technological platform can offer a level of capability that will allow insurers to tackle IFRS 17 with high accuracy, performance and flexibility. The greatest level of granularity can be achieved by supporting data and analysis at the individual contract level across all stages of the analytical lifecycle and across all dimensions. This level of granularity affords accuracy in results and provides analysts with the highest explanatory power to extract the maximum level of insights on an ongoing basis. With the extremely high volume of data, a nimble system with a high-performance in-memory architecture minimizes the movement of data and ensures that analyses can be executed at the rapid pace imposed by business stakeholders. Finally, the user interface should provide enough flexibility to craft new analytical workflows at short notice, in order to answer unforeseen questions. Indeed, the moment of truth that the industry should reckon with is the limited time window that exists between books being closed and financial results being published – a time when all business stakeholders from the analysts through to the board will scrutinize the results and ask any number or types of questions. In such a scenario, static reporting with high-level data and analytics are unlikely to meet the mark.
Like most major initiatives, regulatory or otherwise, the industry has two main options: to address IFRS 17 as a “compliance” exercise with adequate reporting but limited analytical power, or as an opportunity to enhance their capabilities to a level that empowers their organization to thrive and compete. In the data layer, some insurers have for example seized the opportunity to engage on big data projects that allow them to economize and centralize their data architecture not only for the strict purpose of IFRS 17 but as a means to strengthen other functions that rely on similar data, such as policy management or marketing. In the analytical layer, we encourage insurers to adopt a solution that can provide them with the highest levels of granularity, performance and flexibility to support their analytical needs, management decision making, and sheer ability to explain their results – something that IFRS 17 will require a lot of, it seems.
Global Offering Management Leader - Buy Side Financial Risk Solutions