We recently sat down with Mike Lubansky, Dun & Bradstreet's Leader of Alliances Strategy, focusing on Capital Markets, Compliance, and Tax Solutions, to talk about FIBO, the increasingly important data standard being developed in the financial industry.
Mike leads a team of solution strategists focused on developing new uses for Dun & Bradstreet content in the Capital Markets and Compliance space. He also works closely with D&B's Partner Innovation Center, which is developing various prototype solutions, including use of graph database technology and FIBO to address Capital Markets challenges.
What is your view on FIBO, its value and importance for financial and investment firms?
"The Financial Industry Business Ontology (FIBO) is a business conceptual ontology developed by the members of the EDM Council. FIBO provides a description of the structure and contractual obligations of financial instruments, legal entities and financial processes. FIBO is used for harmonization of data across repositories as a common language (i.e. Rosetta stone) for risk analysis and business process automation."
Like many dynamics in this industry, the birth of FIBO goes back to the financial crisis. That event gave way to the realization to those in the financial industry that they needed to get a better handle on their data. FIBO is an extension of that need to apply standards to data, to understand where data resides, to find value in data assets and to use data to meet regulatory needs.
People in Capital Markets now have much greater awareness that the financial economy and "real economy" are interconnected. Standards like FIBO allow financial institutions to broadly work together within a complex industry.
For example, a bank, based on all its existing data sources, needs to clearly tie those multiple sources of first-party- and third-party data together within a common language that can be understood inside the bank and externally to other financial institutions and regulators.
FIBO will get exposure to regulators through industry proof of concepts. If they agree that it's useful, they may choose to mandate banks to adopt it as a formal standard. This would be mutually beneficial for banks and regulators, allowing banks to gain valuable insights from better data management, and regulators to have a common language that enhances meaning for regulatory reporting.
What are the pain-points financial companies are experiencing that FIBO helps solve?
Many industries have the challenge of rationalizing disparate sources of data and consolidating quality data into one place that is consistent and easy to understand, including when the meaning of data is interrelated.
Capital Markets has unique dynamics that make the approach to data standards, data management and resulting insights especially challenging:
- Some financial products are non-standard by their very nature, for example derivatives products. These financial instruments have resulting contract data that is robust and complex.
- Not all financial transactions/swaps are traded on exchanges (over the counter), but rather through bilateral agreements between counterparties, leaving room for contractual data interpretation by counterparties and regulators.
- There are diverse types of financial instruments, many which were created rapidly and without the structure of data standards.
Without visibility across the portfolio to the inherent risk in transactions, contracts and the parties connected to them, financial institutions themselves risk inefficient and poor capital deployment decisions, as well as being non-compliant with regulatory requirements.
For the data geeks in the house, can you go a little deeper into how FIBO works and how it's being developed?
FIBO has different working groups and they're each building out ontology semantics around different aspects of financial services:
- FIBO Foundations - business entities, indices and indicators
- FIBO Contract Ontologies - securities, derivatives, funds and loans
- FIBO Pricing and Analytics - pricing, yields and analytics
- FIBO Process - corporate actions, securities issuance and securitization
- FIBO Future - portfolios, positions and blockchain
FIBO uses semantics and ontologies:
- Semantics is the same concept used in the creation of the worldwide web and hyperlinks. Semantics gives meaning to each point of data - whether structured, semi-structured or unstructured. Semantic technology is used to implement a data standard by mapping or attaching precise meaning and is built on dictionaries and taxonomies.
- Ontologies extend the semantic meaning to describe the relationships between data elements.
What role and value do supplemental business data and analytics provide to enhance FIBO's use?
Risk aggregation is a hot topic for meeting regulatory requirements - as an example, the Basel Committee on Banking Supervision (BCBS239.) This is where FIBO with enhanced third-party entity data plays a role.
The FIBO Foundations mentioned above include the category of business entities. To get a full picture of the business situation and do risk aggregation, a master record is needed that ties a financial instrument - such as a derivative transaction - to the right business entity. Once tied together, the master record can be enriched with data from a financial institution's internal systems and trusted third-party sources.
A financial services organization may have a reference database with business entity data, but it may not have as clear a view of business entities that are related in terms of ownership and control relationships. Real value emerges when core entity data includes related entity associations, such as legal ownership corporate hierarchies (where one company owns greater than 50% of another company), alternative linkage (minority ownership relationships where one company owns less than 50% of another company) and beneficial ownership (where an individual owns or controls more than 25% of a company’s shares or voting rights, or who otherwise exercises control over the company or its management.)
Once a financial services firm can see the related hierarchy of transactions and interrelated business entities, it can then create more meaningful analytics and perform risk aggregation to understand country risk and transitive risk exposure.
Utilizing all relevant data sources available and ontology to structure the data into a clear view of business entities and their relationships help reveal risk and opportunity. And it's catching the attention of the industry.
Stay tuned for the second part of this blog, which will illustrate an active proof of concept with a leading bank who is testing the FIBO standard with D&B data inside an analytical tool for risk analysis purposes.