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Qualitative Data Governance and Traceability for the Increased Regulatory Environment

Piyush Srivastava
Vice President & Head Capital Markets Consulting, BFS
Virtusa Corporation


With the use of new ONTOLOGY technology, new frontiers are evolving, that enable business users to aggregate and view these aggregated exposures and limits in real time or near real time, with no programing, and the ease of having business users define their own reports on the fly. This technology has much wider ramifications, not only to speed up the process of data aggregation, but also to reduce the cost of merged data from disparate sources.

Banks are falling short of required agility

Over the past two decades, Banks and Financial Institutions have become more complex, in terms of expanding business across multiple areas and various geographies. The risk is exacerbated for the banks that have grown tremendously with multiple mergers and acquisitions, where the systems and databases are still disparate. This has reduced the ability of institutions to have a quick and unified view of their global risk data and exposures. The collapse of Lehman Brothers in 2009 saw the classic example, where banks and financial institutions were scrambling to calculate their exposures to other counterparties.  


Regulators are mindful of the context

Since financial crisis of 2008/ 2009, regulators have introduced a number of regulations to improve risk monitoring and impact analysis methodologies. Many of such reformatory regulations (BCBS 239, CCAR, Dodd Frank Acts, Large Exposure, FBO etc.) require banks to measure portfolio RISK across multiple dimensions i.e. legal entities, lines of businesses, asset classes etc., during normal and stressed economic situations.


Banks are trying to innovate and speed up the process of information collection

In order to meet the regulatory mandates, Banks are strengthening their risk reporting for their Enterprise Risk Management (ERM) frameworks covering risk measurement and risk aggregation.

Very large FRAGMENTED DATA of IT portfolio that banks have formed through legacy of acquisitions and mergers remains the epicenter of problems for ERM solutions. Banks often have to spend huge sums to consolidate all risk information into a centralized risk or finance data warehouse, which is often inadequate, un-scalable and extremely cost prohibitive. The risk data retrieval from various sources is still very manual effort intensive and sub-optimal (refer Fig 1).



Figure 1: Disparate risk data sources

In addition, Bank’s existing risk analytics does not allow risk managers to find insights from large, fragmented, and complex data sets in near real time.

The Senior Management, the Board of Directors, the CRO and all other senior line managers struggle as a result of this to find a comprehensive view of the risks facing the organization or have all the information to make critical decisions.


Knowledge engineering can change rules of the game for risk management

Knowledge based Information correlation that combines Risk manager’s intelligence and innovative linguistic models, shows promise to perform graded contagion analysis. This is done not only with a striking accuracy, but also at a fraction of the cost and risk of conventional alternatives.

Adoption of a metadata centric approach is fast becoming a trend in the risk management space, and creating a semantic structure for managing risk information is showing promise to address bank’s long lived data management challenges.

In order to promote and accelerate this approach, EDM council and OMG group together have introduced information standards FIBO. FIBO (Financial Institution Business Ontology) is an Industry initiative. It extends financial industry knowledge definitions using semantic web principles for heightened data expressivity. As an additive to existing data standards and technology investments in data management, FIBO has the potential to help bank structure its SME knowledge, and link it across the multiple dimensions (refer Fig 2)



Figure 2: Structured SME knowledge


Semantic definitions for financial data that is exchangeable across various internal and external functions and with regulatory authorities provides much desired data consistency and transparency.



 Figure 3: Complex risk finance entity relationships

While conventional database technologies have limited ability to represent the complex entities (illustrated in Fig 3) and inter-relationships that span financial networks, semantically defined network graph structures provide a framework (refer Fig 4) that traverses financial networks for performing Systemic Risk Analytics.



 Figure 4: Knowledge based information delivery

Regulators are asking for more



Figure 5: Risk metric life cycle


Along with quantitative data submissions, regulators now want banks to provide traceability of their risk management practices, governance control, and metrics across the functions and ownerships.

Existing Metadata management and traditional data warehouse based solutions offers data management within the IT infrastructure; however it does not offer an overarching view across business and IT. Ontology based information integration allows banks to meet regulator’s expectations around risk governance and control by integrating business concepts with underneath data. It provides visualized traceability of risk metrics from their origination to regulatory submissions.

To summarize, the benefits of this knowledge based approach

This new approach based on Business Ontology and Structured knowledge base will revolutionize the way the banks aggregate their data and provide analytical capabilities that includes:

  • Universal access to data wherever it resides, transcending organizational boundaries
  • Obtaining data directly from its source (transparent to location, platform, schema, format)
  • Supporting access, queries and rollups across disparate non-semantic information sources