Back in March, The Semantic Web Blog wrote an article about FIBO, the Financial Industry Business Ontology that’s on its way to being an Object Management Group series of standards. There, we explored its value as an open semantic standard that can be used by financial institutions and industry regulators, both to support conformance to federal regulatory reporting requirements and for internal business processes and risk analysis.

To continue the discussion about the operational value of FIBO, we recently spoke with key participants developing the standard: David Newman, Strategic Planning Manager, Vice President, Enterprise Architecture, Wells Fargo Bank, who is lead of the industry team collaborating on semantics OTC (over-the-counter) derivatives proof-of-concept, and Mike Atkin, managing director at the Enterprise Data Management (EDM) Council, where FIBO was born and is included as content of EDM’s Semantics Repository.

FIBO, as Newman explains, is two things: A business conceptual ontology and an operational ontology delivered together. In fact, at the upcoming SemTech San Francisco conference, Newman will join with Mike Bennett of the EDM Council to jointly discuss these two sides of the FIBO coin (registration information for the conference can be found here). The business conceptual ontology provides the semantic model that reflects concepts, how they are intertwined and what kind of conceptual abstractions they derive from. “So it provides a very precise visual representation of financial concepts that have been vetted by subject matter experts. The operational ontology is aligned with the business conceptual ontology, but it’s a subset and it’s geared to real pragmatic operational implementations,” Newman says.

Financial institutions can invoke a semantic reasoner that would use those business-concept definitions, as represented in W3C-standard RDF OWL language, to automatically categorize, say, undifferentiated financial swaps into their actual correct and real asset classes – wherever the data about those swaps resides. And in financial institutions, that can be everywhere from legacy enterprise systems to spreadsheets.

“The knowledge that defines what things are no longer has to be coded and expressed in an application program. Much of that knowledge is defined in the ontology. So now you can semantically represent that in a single standard place that a reasoner can perform operations against,” he says. Ask the ontology for fixed-float interest rate swap contracts and it delivers the set of swaps that fulfill the semantic definitions.  “Ontologies themselves become intelligent operational programs that become standardized.” Not having to codify knowledge in multiple programs that have long lead times for change – and being able to change assumptions, views, and definitions in a rapid and flexible way when necessary – can be a huge time- and cost-saver for the industry.

Clearly also propelling those savings is the fact that an industry standard built around leveraging and reusing FIBO relieves financial institutions of the burdens of the disparity and incongruity surrounding their own data. “Once you have a standard language you can take all your data silos and roll it up to one common view of terms and conditions and trades and business deals and legal entities and all the complexity of our industry,” Atkin says. “Without a common key it is really hard to map and align between all those various repositories. And a lot of banks are doing this right now with FIBO.” Others are still stuck in what he calls “reconciliation hell,” mapping between data sets in order to link information.

Metadata annotation – a rich set of explanatory information including source and data provenance that will accompany key elements in the ontology – is important for helping technologists and business analysts align on meaning rather than on words. So, for example, FIBO extends to leveraging sources such as ISDA (International Swaps and Derivatives Association) for linking to its interest rate swap concept that it has created as an XML element. “With metadata annotation you can click on an element in the ontology and point to a rich set of explanatory information and links both internally and externally to provide holistic, unambiguous information about that element, so there never can or should be controversy about what something is from the data point of view,” Newman says.

FIBO In Action

Semantic tech vendor Revelytix has been active in helping show financial institutions how the FIBO ontology can produce business benefits, with the help of its knoodl distributed information management system for creating, managing, analyzing, and visualizing RDF/OWL descriptions. “If you can’t evaluate the semantics in an ontology against existing data sets, it’s not much use to you,” says Michael Lang, CEO, chairman and founder. Using sample data from FactSet and OpenFinanance, it demonstrated a use case focused on financial swaps at the recent Demystifying Financial Services Semantics event in New York City, with SPARQL federation across four disparate data stores of varying structures and rules written in RIF to identify Swap classifications presented in customer dashboards.

 

Knoodl can use the power of the ontology to generate inferences and also drive federation by mapping the data sets to FIBO, so that, for example, true ownership of Swap assets can be revealed, which a financial institution can use to understand its exposure to another party. “This is very important to the financial services industry – a big reason for the liquidity crisis was that banks didn’t know who the real counterparties [they were dealing with] were,” says David Schaengold, Director of Business Solutions at Revelytix. Says Lang, “Almost none of these firms can do this now, in spite of spending tens or hundreds of millions on custom systems to do this.”

Without FIBO and software tools that use OWL and RDF that can operate over distributed data sources to provide such analysis, it will be difficult if not impossible to meet upcoming regulatory requirements around risk analysis, he says.

FIBO is indeed key to lowering risks, says Newman: “The health of the financial system will be better because we will be connecting and linking data and therefore getting clearer visibility into the data, so that the system functions better.”