[Financial Services]

Create Transparency into the Data Ecosystem

Regulatory compliance requires a systemic understanding of the data landscape.

Summary

Global IDs provides customized versions of its products for specific industries.

For financial services firms, we have customized our products to be "aware" of:

  • Industry-specific master data (e.g. client, account, counterparty, instrument, fund, etc.)
  • Industry-specific reference data (e.g. ISO codes, SWIFT codes, etc.)
  • Industry-specific messaging standards. (e.g. ISO 20022, FPML, etc.)
  • Industry-specific ontologies (e.g. FIBO-financial services business ontology, etc.)
  • Industry-specific identifiers (e.g. LEI-legal entity identifiers, etc.)

These industry-specific versions of our products address multiple industry challenges related to:

  • LEI Integration
  • Data Governance
  • Regulatory Compliance
  • Data Security & Privacy

Industry Challenge: Complexity & Cost

To create unique products and services, Financial Services institutions build or buy complex "information-centric" applications at a dizzying pace. Over decades, the accumulated network of applications becomes so complex that it difficult to understand the flow of information across the data landscape.

This lack of "systemic" understanding leads to high costs for maintenance and new application development. It also creates a variety of problems for the institution:

  • Large fines for regulatory non-compliance
  • Inability to measure systemic risk and exposure
  • Limited ability to understand customer interactions and behavior
financial data management

Our Solution: Systematic Cost Reduction

The Global IDs machine-centric approach to enterprise data management creates a foundation for firms to manage their data assets. Through automated discovery, data profiling, quality analysis and metadata documentation, we allow companies to create transparency, enhance accuracy and reduce the resources required to manage data assets.

The goal of the software is to systematically reduce costs associated with master data management. We believe that significant cost savings are possible at the data ecosystem level.

Use Cases / Projects

1) LEI Data Integration

Regulators from around the world have tentatively agreed to require all global financial institutions to embed LEIs (Legal Entity Identifiers) with their client and counterparty data. This move is expected to help create data transparency into the financial ecosystem, and permit banks to better understand risks and exposures on a global basis.

One of our software products (the LEI Integration Product Suite) was specifically created to help financial services organizations integrate all counterparty data with Legal Entity Identifiers. The software links transaction or trade data to a specific legal entity (i.e., the particular line of business and not a single parent organization) and identifies exposures within each entity, surfacing hidden risks that have traditionally been overlooked.

 

2) Market Data Rationalization

Research has shown that most financial services institutions operating in capital markets spend significantly on buying market data from exchanges and third party vendors (Bloomberg, Reuters, IDC, etc). Most institutions also agree that there could be a significant duplication in market data feeds coming from a variety of vendors, and that there is potential cost savings available if all the fields were organized and rationalized systematically.

One of our software products ( The Market Data Governance Product Suite ) was specifically created to help financial services organizations govern and rationalize market data. The software can scan and monitor ~300 types of exchange and non-exchange data feeds to understand the level of redundancy across these data feeds. Duplicative market data feeds become potential candidates for rationalization.

 

3) BCBS 239 Regulatory Compliance

Compliance with BCBS 239 requires data to be managed in accordance with 14 specific criteria. Each of these criteria emphasizes a systematic understanding of the data landscape, and a discipline associated with governing the data.

Global IDs Data Governance Suite for Financial Services supports many of the BCBS 239 criteria, and can be a foundation for governing the data ecosystem.

 

4) Data Warehouse Development

Most data warehouse initiatives are costly, multi-year implementation exercises with significant risks. Much research has shown the data warehouse initiatives have a high statistical probability of failure.

  • Source Data Discovery and Profiling
  • Source to Target mapping
  • Source Data Quality Analysis
  • Master Data Integration

End-to-End integration testing can be done at the early stages of the project. Operational problems can be understood based on continuous monitoring of source data environments. Data quality becomes measurable and project risks are reduced early in the life of the project.

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