IFRS 17: Killing Two Birds

Time is ticking for the 450 insurers around the world to comply with the International Financial Reporting Standard 17 (IFRS 17) by January 1, 2021 for companies with their financial year starting on January 1.

Insurers are at different stages of preparation, ranging from performing gap analyses, to issuing requirements to software and consulting vendors, to starting the pilot phase with a new IFRS 17 system, with a few already embarking on implementing a full IFRS 17 system.

Unlike the banks, the insurance industry has historically spent less on large IT system revamps. This is in part due to the additional volume, frequency and variety of banking transactions compared to insurance transactions.

IFRS 17 is one of the biggest ‘people, process and technology’ revamp exercises for the insurance industry in a long while. The Big 4 firms have published a multitude of papers and videos on the Internet highlighting the impact of the new reporting standard on insurance contracts that was issued by the IASB in May 2017. In short, it is causing a buzz in the industry.

As efforts are focused on ensuring regulatory compliance to the new standard, insurers must also ask: “What other strategic value can be derived from our heavy investment in time, manpower and money in this whole exercise?”

The answer—analytics to gain deeper business insights.

One key objective of IFRS 17 is to provide information at a level of granularity that helps stakeholders assess the effect of insurance contracts on financial position, financial performance and cash flows, increasing transparency and comparability.

Most IFRS 17 systems in the market today achieves this by bringing the required data into the system, compute, report and integrate to the insurer’s GL system. From a technology perspective, such systems will comprise a data management tool, a data model, a computation engine and a reporting tool. However, most of these systems are not built to provide strategic value beyond pure IFRS 17 compliance.

Apart from the IFRS 17 data, an insurer can use this exercise to put in place an enterprise analytics platform that caters beyond IFRS 17 reporting, to broader and deeper financial analytics, to customer analytics, operational and risk analytics. To leverage on new predictive analytics technologies like machine learning and artificial intelligence, a robust enterprise data platform to house and make available large volumes of data (big data) is crucial.

Artificial Intelligence can empower important processes like claims analyses, asset management, risk calculation, and prevention. For instance, better forecasting of claims experience based on a larger variety and volume of real-time data. The same machine can be used to make informed decisions about investments based on intelligent algorithms, among other use cases.

As the collection of data becomes easier and more cost effective, Artificial Intelligence can drive whole new growths for the insurance industry.

The key is centralizing most of your data onto a robust enterprise platform to allow cross line of business insights and prediction.

As an insurer, if your firm has not embarked on such a platform, selecting a robust system that can cater to IFRS 17 requirements AND beyond will be a case of killing 2 birds with one stone.

FRG can help you and your teams get ready for IFRS 17.  Contact us today for more information.

Tan Cheng See is Director of Business Development and Operations for FRG.

Top 6 Things To Consider When Creating a Data Services Checklist

“Data! Data! Data! I can’t make bricks without clay.”
— Sherlock Holmes, in Arthur Conan Doyle’s The Adventure of the Copper Beeches

You should by now have a solid understanding of the growth of and history of data, data challenges and how to effectively manage themwhat data as a service (DaaS) is, how to optimize data using both internal and  external data sources, and the benefits of using DaaS. In our final post of the series, we will discuss the top six things to consider when creating a Data Services strategy.

Let’s break this down into two sections: 1) pre-requisites and 2) the checklist.

Prerequisites

We’ve identified four crucial points below to consider prior to starting your data services strategy. These will help frame and pull together the sections of information needed to build a comprehensive strategy to move your business towards success.

Prerequisites:

1: View data as a strategic business asset

 In the age of data regulation including BCBS 239 principles for effective risk data aggregation and risk reporting, GDPR and others, data, especially that relating to an individual, is considered an asset that must be managed and protected. It also can be aggregated, purchased, traded and legally shared to create bespoke user experiences and engage in more targeted business decisions. Data must be classified and managed with the appropriate level of governance in the same vein as other assets, such as people, processes and technology. Being in this mindset and appreciating the value of data and recognizing that not all data is alike and must be manged appropriately will ultimately ensure business success in a data-driven world.

2: Ensure executive buy-in, senior sponsorship and support

As with any project, having executive buy-in is required to ensure top down adoption. However, partnering with business line executives who create data and are power users of it can help champion its proper management and reuse in the organization. This assists in achieving goals and ensuring project and business success. The numbers don’t lie: business decisions should be driven by data.

3: Have a defined data strategy and target state that supports the business strategy

Having data for the sake of it won’t provide any value; rather, a clearly-defined data strategy and target state which outlines how data will support the business will allow for increased user buy in and support. This strategy must include and outline:

  • A Governance Model
  • An Organization chart with ownership, roles and responsibility, and operations; and
  • Goals for data accessibility and operations (or data maturity goals)

If these sections are not agreed from the start, uncertainty, overlapping responsibilities, duplication of data and efforts as well as regulatory or potentially legal issues may arise.

4: Have a Reference Data Architecture to Demonstrate where Data Services Fit

Understanding the architecture that supports data and data maturity goals, including the components that are required to support the management of data from acquisition through distribution and retirement is critical. It is also important to understand how they fit into the overall architecture and infrastructure of the technology at the firm.  Defining a clear data architecture and its components including:

  • Data model(s)
  • Acquisition
  • Access
  • Distribution
  • Storage
  • Taxonomy

are required prior to integration of the data.

5. Data Operating Model – Understanding how the Data Transverses the Organization

It is crucial to understand the data operations and operating model – including who does what to the data and how the data ownership changes over time or transfers among owners. Data lineage is key – where your data came from, its intended use, who has/is allowed to access it and where it goes inside or outside the organization – to keep it clean and optimize its use. Data quality tracking, metrics and remediation will be required.

Existing recognized standards such as the Global Legal Entity Identifier (LEI) that can be acquired and distributed via data services can help in the sharing and reuse of data that is ‘core’ to the firm. They can also assist in tying together data sets used across the firm.

Checklist/Things to Consider

Once you’ve finished the requirements gathering and understand the data landscape, including roles and responsibilities described above, you’re now ready to begin putting together your data services strategy. To build an all-encompassing strategy, the experts suggest inclusion of the following.

1:  Defined Data Services Required

  •  Classification: core vs. business shared data and ownership
    • Is everyone speaking a common language?
    • What data is ‘core’ to the business, meaning it will need to be commonly defined and used across the organization?
    • What data will be used by a specific business that may not need to be uniformly defined?
    • What business-specific data will be shared across the organization, which may need to be uniformly defined and might need more governance?
  • Internal vs external sourcing
    • Has the business collected or created the data themselves or has it been purchased from a 3rd party? Are definitions, metadata and business rules defined?
    • Has data been gathered or sourced appropriately and with the correct uniform definitions, rules, metadata and classification, such as LEI?
  • Authoritative Data Sources for the Data Services
    • Have you documented where, from whom, when etc. the data was gathered (from Sources of Record or Sources of Origin)? For example, the Source of Origin might be a trading system, an accounting system or a payments system. The general ledger might be the Source of Record for positions.
    • Who is the definitive source (internal/external)? Which system?
  • Data governance requirements
    • Have you adhered to the proper definitions, rules, and standards set in order to handle data?
    • Who should be allowed to access the data?
    • Which applications (critical, usually externally facing) applications must access the data directly?
  • Data operations and maintenance
    • Have you kept your data clean and up to date?
    • Are you up to speed with regulations, such as GDPR, and successfully obtained explicit consent for the information?
    • Following your organization chart and rules and requirements detailed above, are the data owners known, informed and understand they are responsible for making sure their data maintains its integrity?
    • Are data quality metrics monitored with a process to correct data issues?
    • Do all users with access to the data know who to speak to if there is a data quality issue and know how to fix it?
  • Data access, distribution and quality control requirements
    • Has the data been classified properly? Is it public information? If not, is it restricted to those who need it?
    • Have you defined how you share data between internal/external parties?
    • Have the appropriate rules and standards been applied to keep it clean?
    • Is there a clearly defined process for this?
  • Data integration requirements
    • If the data will be merged with other data sets/software, have the data quality requirements been met to ensure validity?
    • Have you prioritized the adoption of which applications must access the authoritative data distributed via data services directly?
    • Have you made adoption easy – allowing flexible forms of access to the same data (e.g., via spreadsheets, file transfers, direct APIs, etc.)?

2: Build or Acquire Data Services

 To recap, are you building or acquiring your own Data Services? Keep in mind the following must be met and adhere to compliance:

  • Data sourcing and classification, assigning ownership
  • Data Access and Integration
  • Proper Data Services Implementation, access to authoritative data
  • Proper data testing, and data remediation, keeping the data clean
  • Appropriate access control and distribution of the data, flexible access
  • Quality control monitoring
  • Data issue resolution process

The use and regulations around data will be constantly evolving as will the number of users data can support in business ventures. We hope that this checklist will provide a foundation towards building and supporting your organization’s data strategies. If there are any areas you’re unclear on, don’t forget to take a look back through our first five blogs which provide more in-depth overviews on the use of data services to support the business.

Thank you for tuning into our first blog series on data management. We hope that you found it informative but most importantly useful towards your business goals.

If you enjoyed our blog series or have questions on the topics discussed, write to us on Twitter@FRGRISK.

Dessa Glasser is a Principal with the Financial Risk Group, and an independent board member of Oppenheimer & Company, who assists Virtual Clarity, Ltd. on data solutions as an Associate. 

 

RELATED:

Data Is Big, Did You Know?

Data Management – The Challenges

Data as a Service (DaaS) Solution – Described

Data as a Service (DaaS) Data Sources – Internal or External?

Data as a Service (DaaS) – The Benefits

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