Distributed Information Management System
Using FIBO to Analyze and Manage Risk
Michael Lang, David Schaengold,
EDMC, March 2012
Agenda
Background on a Distributed Information Management System (DIMS)
– How does semantics deal effectively with highly distributed data?
Demo ‐ Financial Services use case
– Classifying Interest Rate Swaps with distributed data, rules and inference
– Assessing risk across LEIs
Data Sources
FactSet provided excellent data on Legal Entity Structures
Open Finance provided excellent data on swap instruments
EDMC provided the Financial Instrument Business Ontology (FIBO)
Thanks to all!
Semantic Technology
Anyone can say Anything about Anything
Standards
URI/URL - the universal identification and location scheme
RDF – basic description language and data model
OWL – the logic based description language - FIBO
SPARQL - the query language for the RDF data model
RIF – RDF based rules language
Benefits
URIs give universal identifiers (non-local) identifiers to “things”
RDF is schema-less; extensibility is not an issue
SPARQL - defines federation capabilities
OWL – combines data, metadata and logic
RIF – Rules Language (close up the open world)
Assessing Risk
We need to classify all instruments by type so aggregated exposure can be calculated
– This is tricky for swap instruments as the
contracts can vary
We need to calculate total risk for all counterparties by type of instrument
– The web of corporate relations can be difficult to navigate, but can be determined and integrated
– We need to classify legal entities by type
The Last Forty Years
Siloed Information Management Systems
All data in a single shared databank
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Rigid schemas, resistant to change
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Data and metadata are different things
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Query processor only knows about its local data
expressed in a fixed schema
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Excellent ACID / CRUD capability
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Very difficult to use for comprehensive analytics
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The Next Forty Years
The information management technology for the next forty years will all rest on precise, formalized descriptions of “things”
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Schema
Data
Mappings
Provenance
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Rules
Locations of data
Logic
Processes
Anyone can say Anything about Anything The Next Forty Years
Distributed Information Management Systems
• Data, metadata, and logic ‐ everywhere
• all of it will be discoverable & machine readable
• Extensibility ‐ constant change is assumed
• Provenance – all data can found and used properly
• Reasoning and inference (rules and logic)
• Emergent Analytic capability
IT infrastructure is going to get smart
Distributed Information
Management System
Assessing Risk
We need to classify all instruments by type so aggregated exposure can be calculated
– This is tricky for swap instruments as the contracts can vary
We need to calculate total risk for all counterparties by type of instrument
– The web of corporate relations can be difficult to navigate, but can be determined and integrated
– We need to classify legal entities by type
Rules and Inference
1‐ In order to determine the types of swaps: – Write FOL rules to say precisely what collection of properties each type of swap must have
– Evaluate all of the properties of each instrument according to each rule
– Assert the type
2‐ Follow the same procedure for classifying legal entities
Some rules are in FIBO, some are external
Our Solution
Distributed Information Management Systems (DIMS)
enables a new enterprise management paradigm
• Assumes enterprise data is widely distributed
• Assumes change is constant
• Meanings and locations are computer readable and easily interpreted by humans
• The provenance of all information in the enterprise is described and understood by the IT infrastructure
• Enables a truly agile analytic capability
• Enables enterprise‐wide Governance, Risk, and Compliance • Provides sophisticated knowledge management The Shift is On
Distributed Information Management System
Emergent Analytics is available now
Questions
Revelytix.com for additional information
Thank You