Leaning On Lineage

April 18, 2018 –

Mechanisms To Pass Internal And External Regulatory Audits

Not a day goes by that there isn’t an article on the application of blockchain or artificial-intelligence technologies in the financial markets. However, one has to think about a problem first and then apply the appropriate technology to provide business value. Since AxiomSL serves more than half of the world’s globally systemically important banks (GSIBs), the need to describe how regulatory filings are arrived at, is at the core of withstanding an audit process.

Meaningful Metadata

The first step that AxiomSL built into its data-management platform is the concept of being able to describe the data upon arrival from a core account-processing system, the general ledger, or a data warehouse within a bank’s environment. These data descriptions include: source system details, date and timestamps, reference data, data type, etc.; all critical data elements used for reporting.

Data Ancestry

Once the data is in the AxiomSL data eco-system, the next logical step is to classify the data elements and place them in pre-processing tables. This is a key step in understanding all the elements before any regulatory filing calculations have taken place. The metadata described above enables validation and enrichment of the data sets. For instance, having key metadata information for elements including exposure, maturity, probability of default (PD), loss-given default (LGD), and risk rating helps to ensure the accurate calculation and reporting of Basel risk metrics including risk-weighted assets (RWA).

Business Rules And Calculations Supported By Lineage

The next logical step for preparing a regulatory filing is using conditional statements with Boolean logic against the pre-processing tables to develop a preliminary perspective of the said filing. Here, having metadata for each data element used in the calculation, provides many benefits, including the ability:

  • For users to quickly respond to inquiries related to the business rule and data transformations by report owners/internal audit.
  • To assess the impact of updating or decommissioning a data attribute in the bank’s data warehouse environment.

Finalizing the Filing

From a preliminary filing to a finalized report, there are several steps in the approval process. Senior business and audit leadership involved in the approval process should ask the following questions:

  • What business logic and data aggregation rules are used in the generation of a reporting line, and by extension a regulatory filing?
  • What checks are in place to catch discrepancies or reconciliation issues if any, especially in cases where the same financial instrument is reported on multiple regulatory reports?

The Post-Filing Audit

While we have satisfied the internal decision makers in the organization, regulators also ask a series of questions about past filings that may include:

  • How do you ensure that all the data sources are captured and no critical data set is dropped off while reporting key regulatory reports?
  • How do you keep track of business logic updates between different reporting cycles?
  • How do you automate laborious and often manual data classification and stewardship tasks?

Static Versus Dynamic Data Lineage

Getting enterprise-level data lineage right, and in an automated fashion, is challenging work for large financial institutions with many disparate technology systems. Depending on the system’s capabilities, data lineage is extracted manually as a one-time activity (static data lineage) with policies around keeping it current based on a larger architecture or sourcing update; or managed in an automated fashion where the technology system keeps the lineage accurate and up to date without any manual intervention. The support for dynamic data lineage is especially important for technology systems that are used for regulatory filings since these reports are updated frequently by the regulator and the corresponding update to the sourcing and business rules to satisfy the regulatory change.

About AxiomSL and Data Lineage

AxiomSL combines deep industry expertise with an intelligent platform and applications to deliver financial regulatory reporting, liquidity, capital and credit, operations, trade and transactions, and tax analytics. Its global footprint spans 70 regulators across 50 jurisdictions, serving financial institutions with more than $39 trillion in total assets. AxiomSL’s expertise in implementing data lineage can be summarized as follows:

  • Automated out-of-the-box dynamic data lineage for reporting solutions implemented on AxiomSL’s platform.
  • Ability to trace the data flow of a financial instrument from the initial source to final report.
  • System-generated technical documentation reports listing all data sources, business rules, and data aggregations used to generate a regulatory report.
  • Ability to easily identify the list of golden data sources, critical data elements utilized for the various regulatory solutions implemented using AxiomSL’s platform.
  • Ability to extract data lineage information from AxiomSL’s platform to be imported in different enterprise data lineage solutions used by the financial institution.
  • Support for both drill-up (data source to report) and drill-down (report to data source) lineage.

The orchestration of people, processes, and technology enables firms to leverage data as an enterprise asset and start weaving regulatory compliance investment into the fabric of risk-enabled decision-making.

For more information, please contact Varun Singhal at: vsinghal@axiomsl.com or by phone at +1212 248 4188 X 270


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