1. FIBO Semantics Initiative David Newman Strategic Planning Manager, Vice President Enterprise Architecture, Wells Fargo Bank January 2012

2. 2 "We can't solve problems by using the same kind of thinking we used when we created them." —Albert Einstein 1/30/2012

3. Agenda 1/30/20123 2) Business and Regulatory Drivers 3) Briefing on Semantics as an Enabling Technology for Expressing and Operationalizing Financial Data Standards 4) OTC Derivatives POC Demonstration 1) Mission of joint EDM Council/Object Management Group Semantics OTC Derivatives Proof of Concept 5) Discussion and Next Steps

4. Industry Team Collaborating on Semantics OTC Derivatives POC NameOrganizationRole David NewmanWells FargoLead Mike BennettEDM CouncilCore Team Elisa KendallThematixCore Team Jim RhyneThematixCore Team Mike AtkinEDM CouncilStakeholder Anthony CoatesLondataSubject Matter Expert David GertlerSuper DerivativesSubject Matter Expert Marc GratacosISDASubject Matter Expert Andrew JacobsUBSSubject Matter Expert Dave McCombSemantic ArtsSubject Matter Expert Pete RivettAdaptiveSubject Matter Expert Martin SextonLondon Market SystemsSubject Matter Expert Harsh SharmaCitiSubject Matter Expert Kevin TysonJP Morgan ChaseSubject Matter Expert Marcelle von WendlandFincoreSubject Matter Expert 4 1/30/2012

5. Key Regulatory Requirements Influencing Semantics OTC POC 5 1) Define Uniform and Expressive Financial Data Standards Ability to enable standardized terminology and uniform meaning of financial data for interoperability across messaging protocols and data sources for data rollups and aggregations 1) Define Uniform and Expressive Financial Data Standards Ability to enable standardized terminology and uniform meaning of financial data for interoperability across messaging protocols and data sources for data rollups and aggregations 2) Classify Financial Instruments into Asset Classes* Ability to classify financial instruments into asset classes and taxonomies based upon the characteristics and attributes of the instrument itself, rather than relying on descriptive codes 2) Classify Financial Instruments into Asset Classes* Ability to classify financial instruments into asset classes and taxonomies based upon the characteristics and attributes of the instrument itself, rather than relying on descriptive codes 3) Electronically Express Contractual Provisions** Ability to encode concepts in machine readable form that describe key provisions specified in contracts in order to identify levels of risk and exposures 3) Electronically Express Contractual Provisions** Ability to encode concepts in machine readable form that describe key provisions specified in contracts in order to identify levels of risk and exposures 5) Meet Regulatory Requirements, Control IT Costs, Incrementally Deploy Ability to define data standards, store and access data, flexibly refactor data schemas and change assumptions without risk of incurring high IT costs and delays, evolve incrementally 5) Meet Regulatory Requirements, Control IT Costs, Incrementally Deploy Ability to define data standards, store and access data, flexibly refactor data schemas and change assumptions without risk of incurring high IT costs and delays, evolve incrementally 4) Link Disparate Information for Risk Analysis * Ability to link disparate information based upon explicit or implied relationships for risk analysis and reporting, e.g. legal entity ownership hierarchies for counter-party risk assessment 4) Link Disparate Information for Risk Analysis * Ability to link disparate information based upon explicit or implied relationships for risk analysis and reporting, e.g. legal entity ownership hierarchies for counter-party risk assessment *Swap Data Recordkeeping and Reporting Requirements, CFTC, Dec 8, 2010 *Report on OTC Derivatives Data Reporting and Aggregation Requirements, the International Organization of Securities Commissioners (IOSCO), August 2011 **Joint Study on the Feasibility of Mandating Algorithmic Descriptions for Derivatives, SEC/CFTC, April 2011 1/30/2012

6. Semantics OTC Derivatives POC Mission Mission Statement: Demonstrate to the financial industry and the regulatory community how:  utilizing semantic technology and the Financial Industry Business Ontology (FIBO) can be a prudent strategic investment to realize: data standardization data integration data linkage data classification  using currently available data sources and messaging protocols 6 1/30/2012

7. Data challenges for entity and instrument identification, classification and relationships 1/30/2012 7 How can we supplement our existing investments in data management to resolve these challenges and achieve these goals? Current State of Financial Data Limited data standards Data rationalization problems Data incongruity and fragmentation Opaque data silos limits integration Cryptic codes, programs, brittle data schemas and fixed taxonomies Target State of Financial Data Pervasive data standards Data precision, clarity, consistency Data alignment and linkage Data integration despite silos Flexible and intelligent data schemas and dynamic classifications

8. Semantic Web Technology Can Help Organizations Mature their Data Management Capabilities The true value of an information management system is ultimately based upon the intelligence and expressive power of it’s data schema or model 81/30/2012 Semantic web technology provides highly advanced data schemas (ontologies) and tools that can help organizations better define, link, integrate and classify their data

9. Financial Industry Business Ontology (FIBO)  Industry initiative to extend financial industry data standards using semantic web principles for heightened data expressivity, consistency, linkage and rollups  Semantics is synergistic, complementary and additive to existing data standards and technology investments in data management! 91/30/2012

10. What is Semantic Technology? 10 A data management technology for the 21 st century that provides: a layer of intelligence over disparate data structures that is used to precisely express the meanings, concepts, and relationships implied by the data in ways that both humans and machines can understand in order to maximize data organization, integration and classification Semantic Web Stack 1/30/2012

11. What are Semantic Data Schemas (Ontologies)? Schemas based on a formal symbolic logic (Description Logics) that specifies a set of mathematically verifiable and repeatable logical patterns that are understood by machines and can be used to represent complex relations between entities in order to automatically describe real world concepts that are meaningful to humans 111/30/2012 Understands Semantic Schema (ontology)

12. Semantic Technology Basics Describes concepts in terms of: – Classes (Entities, Unary predicates) – Relationships (Properties, Binary predicates) – Individuals (instances) Makes inferencing possible – A “Reasoner” infers new data relationships and classifications after applying semantically defined rules and logical patterns to instances of data 12 DavidisEmployedBy Wells Fargo Subject > Person Subject > Person Predicate > workFor Predicate > workFor Object > Company Object > Company Aligns linguistically with how we think and speak! employs inverse subPropertyOf type 1/30/2012

13. Semantic Intelligence Utilizes Underlying Machine Based Logical Patterns 1/30/201213 Inference: Humans cause Forest Fires ABCD Inference: A causes D Underlying Machine Based Logical Pattern (Axiom) Human Concept A causes BB causes CC causes D Example: Transitive Relations expresses Use Cases : Ancestry, Dependency, Impact, Link Analysis

14. Some Examples of Semantic Axioms that Allow Machines to Represent Human Concepts 14 Subsumption Functional Properties Symmetric Properties Transitive Properties Property Chains Restriction Classes Lisa hasBirthMother Marge Person hasBirthMother Mother Person marriedTo Person Bart hasAncestor Homer and Homer hasAncestor Abraham -> Bart hasAncestor Abraham Person hasParent Person: Person hasSister FemalePerson -> Person hasAunt FemalePerson Person hasAncestor Person Bart hasParent Marge : Marge hasSister Selma -> Bart hasAunt Selma Properties can be linked together to form a chain of meaningful relationships If A has a relation with B, and B has a relation with C, then A also has a relation with C Property can have only one unique value NuclearFamily equivalentClass = hasFather exactly 1 Father and hasMother exactly 1 Mother and hasChild some Child Mother subClassOf Parent Simpsons type NuclearFamily -> hasFather Homer and hasMother Marge and hasChild (Bart, Lisa, Maggie) Describes new class by associating multiple classes, properties and values together Marge type Mother -> Marge type Parent A class (or property) is a sub-set of another class (or property) Homer marriedTo Marge -> Marge marriedTo Homer Property relation holds true in both directions of the relationship 1/30/2012

15. Ontology Spectrum* 15 weak semantics strong semantics Is Disjoint Subclass of with transitivity property Modal Logic Logical Theory Thesaurus Has Narrower Meaning Than Taxonomy Is Sub-Classification of Conceptual Model Is Subclass of DB Schemas, XML Schema UML First Order Logic Relational Model, XML ER Extended ER Description Logic DAML+OIL, OWL RDF/S XTM Syntactic Interoperability Structural Interoperability Semantic Interoperability From less to more expressive *courtesy of Dr. Leo Obrst, The Mitre Corporation 1/30/2012

16. Semantics Offers Differentiating Value Compared to Conventional Technologies 16 Swap Swapstream Party SwapAssoc SwapParty LegalEntity XMLRelationalSemantics Interest Rate Swap Contract SubClassOf Swap_100001234 Type Swap_Leg some Fixed_Interest_Rate and Swap_Leg some Variable_Interest_Rate Equivalent Class SwapStream_1… hasSwapStream SwapStream_2… hasSwapStream Vanilla Interest Rate Swap Contract Basis Swap Contract SubClassOf Swap_Leg Type Lingua franca of web service messaging payloads following W3C standards Used to tag data elements with standard labels that conform to a predefined schema Forms structured data hierarchies Document hierarchy can be queried While XML tags associate data to labels, the meaning of the labels is not inherently understood by the computer requiring custom program logic to process each label Dominant database implementation Highly mature software and tools Data is physically organized within tables and accessed by matching related columns in different tables that fulfill various conditions Knowledge within application logic Hard-wired and brittle schema/data Design, construction, access, mgt are labor, time, resource intensive Limited, but growing, set of software, tools Can supplement XML and relational database Can begin with knowledge representation and evolve towards operational implementations Emerging form of knowledge representation offers highly intelligent form of data organization Conceptually describes the meaning of data and its relationships in a way that both people and computers can understand Supports classification, reasoning and agility 1/30/2012

17. Semantics Supplements Existing Data Standards: Descriptively and Operationally 1/30/201217 Interest Rate Swap Contract SubClassOf Swap_100001234 Type Swap_Leg some Fixed_Interest_Rate and Swap_Leg some Variable_Interest_Rate Equivalent Class SwapStream_1 hasSwapStream SwapStream_2… hasSwapStream Vanilla Interest Rate Swap Contract Basis Swap Contract SubClassOf Swap_Leg Type Ontology XML Message Describes Rationalizes Provides data mapping, linkage and classification Operational Precisely describes data elements for better human understanding Descriptive Integrates Operational Provides data integration and advanced queries across disparate data sources Swap Swapstream PartySwapAssoc SwapParty LegalEntity Relational Data Base Swap Swapstream PartySwapAssoc SwapParty LegalEntity Relational Data Base Note: Run with Animation

18. Semantic Technology: How is it beneficial? 18  Knowledge encapsulated in opaque software  Data organization tightly coupled with schema  Multiple complex tables and data relationships  Awareness of physical organization of data required  Schemas enforce limited data integrity  -> High costs, longer TTM Conventional Technology Semantic Technology  Standard vocabulary and knowledge representation  Data organization decoupled from schema  Inferencing creates new knowledge  Consistent rules based on standard data elements ensured across domain  All data is Web addressable  -> Lower costs, faster TTM Challenges: Improvements: Data Schema New Data Entity Physical Database New Physical Table for New Entity Application Software Business Rules in Code Access Update Define New Data Entity Ontology / Semantic Schema Physical Database Some Business Rules Added to Ontology Application Software Inferred Some Business Rules Migrated to Ontology Physical Format Unchanged after New Data Entity Added Access Update Define Data Schema 1/30/2012

19. Comparative Analysis 19 XMLRelationalSemantics Describes Concepts, Taxonomies, Rich Data Relationships Concepts Understandable to Both Humans and Machines Multiple Classifications and Categorizations of Data Logical consistency and constraint checking Reasoning and Inference Capabilities Ability to change schema/model with low impact/cost Potential to Deliver Faster TTM and Lower TCO Operational Scalability, Efficiency and Optimization Industry Adoption and Prevalence of Skilled Resources Maturity of Tools and Software Current Ease of Mastery of Technology and Skills LowMediumHigh 1/30/2012

20. Potential Benefits of using Semantic Technology 20 Reduce Complexity Reduces reliance on arcane legacy data structures and cryptic codes by using more meaningful, natural language friendly constructs Reduce Complexity Reduces reliance on arcane legacy data structures and cryptic codes by using more meaningful, natural language friendly constructs Evolve Global Data Standards, Enable Data Integration and Classification Provides model and infrastructure to define the meaning of information in order to represent the semantics of data standards; as well as integrate, link and classify incongruent data Evolve Global Data Standards, Enable Data Integration and Classification Provides model and infrastructure to define the meaning of information in order to represent the semantics of data standards; as well as integrate, link and classify incongruent data Reduce Costs (People and Technology) As understanding of data increases, costly data reconciliation efforts by analysts can be reduced Improved data federation and reduced data management costs can potentially be realized Reduce Costs (People and Technology) As understanding of data increases, costly data reconciliation efforts by analysts can be reduced Improved data federation and reduced data management costs can potentially be realized Improve Agility As regulatory/industry views and assumptions change, semantics allows data schemas to rapidly reflect change without incurring massive data and application program restructuring efforts Improve Agility As regulatory/industry views and assumptions change, semantics allows data schemas to rapidly reflect change without incurring massive data and application program restructuring efforts Increase Functionality using Reasoning and Inferencing Capabilities Using logically consistent rules and semantic definitions, programs called reasoners can infer data to be classified into special business defined categories and relationships Increase Functionality using Reasoning and Inferencing Capabilities Using logically consistent rules and semantic definitions, programs called reasoners can infer data to be classified into special business defined categories and relationships 1/30/2012

21. Business and Operational Ontologies 21 Defines Transaction types Defines contract types Defines leg roles Defines contract terms Operational Ontology (Semantic Web) IR Stream IR Swap Agreement has party is a swaps Includes only those terms which have corresponding instance data Requirement #1: Define Uniform and Expressive Financial Data Standards Model from Sparx Systems Enterprise Architect Business Ontology (AKA “conceptual model”) provides source for Narrowed for Operational use 1/30/2012

22. Anatomy of a Semantic Data Standard 22 RDF Type OWL versionInfo Semantic Metadata Model SKOS definition DC source ODM Model RDFS seeAlso SKOS altLabel RDF,RDFS, OWL: W3C Semantic languages DC: Dublin Core Metadata Elements SKOS: Simple Knowledge Organization System rdf:type LegalEntityIdentifier skos:altLabel LEI skos:definition A legal entity identifier (LEI) is a unique ID associated with a single corporate entity dc:source SIFMA (Securities Industry and Financial Markets Association) overview discussion of Legal Entity Identifier (http://www.sifma.org) owl:versionInfo Version 1.0.0 rdfs:seeAlso Office of Financial Research; Statement on Legal Entity Identification for Financial Contracts SKOS altLabel Semantic Metadata  Multiple access options over the web via the authoritative standards body  Hyperlink to semantic web standard from documents  Community participation and interaction  Query access via formal semantics repository including links and synonymous terms for knowledge  Improved governance  Provenance and evolution recorded  Model files for download in multiple tools Community Access to Standards Requirement #1: Define Uniform and Expressive Financial Data Standards 1/30/2012

23. Semantics can operationally classify undifferentiated Swaps and show relationships 23 Classes are inferred using rules that query the content of the data Data is linked together via relationships called properties * Gruff 3.0 courtesy of Franz, Inc. Vanilla_IR_Swap has_Swap_Legs some Variable_Interest_Terms and has_Swap_Legs some Fixed_Interest_Terms Requirement #2: Classify Financial Instruments into Asset Classes 1/30/2012

24. Semantic Representation of Contractual Provisions for Risk Classification 24 Requirement #3: Electronically Express Contractual Provisions Note: OTC POC Phase 2 in process Define Axioms Identify Key Contractual Events Identify Key Contractual Actions  ISDA Master Agreement  Schedules  Credit Support Annex  Schedules Downgrade Counterparty Credit Credit Rating Agency Default EventsTermination EventsIncrease CollateralTransfer PaymentsClassify Counterparties into Risk Categories for Analytics Reduce Value of Collateral Events Counterparties OTC Derivative Confirm Classify Contract Type Infer Counterparty Exposures Risk Analyst Transaction Repository, et.al. *Report on OTC Derivatives Data Reporting and Aggregation Requirements, the International Organization of Securities Commissioners (IOSCO), August 2011 **Joint Study on the Feasibility of Mandating Algorithmic Descriptions for Derivatives, SEC/CFTC, April 2011 Market Reference Data FpML FIBO Ontology Operational Ontology 1/30/2012

25. ?entity Legal Entity type ?legal Name hasExactLegalName ?parent hasImmediateParent ?swap partyToSwap ?amount notional Amount AtRisk Transaction Repository Z Semantics offers Advanced Query Capabilities 25 Requirement #4: Link Disparate Information for Risk Analysis ?entity Legal Entity type ?legal Name hasExactLegalName ?parent hasImmediateParent ?swap partyToSwap ?amount notional Amount AtRisk Transaction Repository Y  Data is queried using graph pattern matching techniques vs. relational joins  Queries can process inferred data and highly complex and abstract data structures  Queries can federate across semantic endpoints (using SPARQL 1.1)  Data can be aggregated and summarized (using SPARQL 1.1) Risk Analyst ?entity Legal Entity type ?legal Name hasExactLegalName ?parent hasImmediateParent ?swap partyToSwap ?amount swap Notional Amount Transaction Repository X Query all Transaction Repositories to report on the sum total of aggregate exposure for all counterparties and their parents involved in all swaps associated with an interest rate swap taxonomy Note: TBD in future phase of POC Interest Rate Swap Basis Swap type Vanilla Interest Rate Swap subClassOf 1/30/2012

26. Semantics Offers Federation via Linked Data 26 Requirement #4: Link Disparate Information for Risk Analysis  Semantically defined data that is Web addressable and “inter-linked”  Transcends organizational boundaries and provides universal access to data wherever it resides internally within the network (and externally via “Linked Open Data”)  Obtains data directly from its source (transparent to location, platform, schema, format)  Can support access, queries and rollups across Swap Data Repositories Semantic Enterprise Information Integration (EII) Platform Swap Data Repository Database Note: TBD in future phase of POC Ontologies Links to the Semantic Web Linked OTC Data Cloud Legal Entity Data Provider Risk Analyst Swap Data Repository Database Aggregated Linked Data Query 1/30/2012

27. Data XML Relational Semantic Application Unstructured Schema XM L Relational Application Business Semantics Conceptual Models Primarily for Human consumption Conceptual, design-time, and non-operational Community engagement and update process when warranted Standard terminology, concepts and descriptions for reference, knowledge, data reconciliation, rationalization and governance Integrated ontologies, Upper ontologies for broader meaning Semantic Usage Patterns can be Deployed Incrementally and in Tandem with Existing Technology 27 Reference Ontologies Primarily for Machine consumption Ontologies narrowed for operational usage Supplements and operates in tandem with conventional technology Runtime access to knowledge, reference data, metadata Canonical domain models for mappings and interoperability Semantic graph pattern matching queries and automated reasoning Data Inferred Schema Inferencing and Classification of Source Data Heterogeneous source data ingested, validated for inconsistencies, and transformed by Semantic Reasoner into domain ontology to fulfill mapping rules Source data inferred by Reasoner, using formal axioms or rules, into abstract classifications, new data relationships/linkages Semantic rules engine can be optionally accessed Query time reasoning can be optionally utilized ABox Inferred TBox ABox Inferred TBox Data Inferred Schema Data Federation and Linked Open Data Data semantically linked, integrated and accessed both internally and externally using RDF linked URIs which are Web addressable Federated query of semantic and non-semantic data stores using canonical semantic domain model for data interoperability and inferencing Semantic Application XML Unstructured RDBMS Relational Unstructured XML Unstructured RDBMS Relational Unstructured XML Rules Engine Conceptual OntologyOperational Ontology Requirement #5: Meet Regulatory Requirements, Control IT Costs, Deploy Incrementally 1/30/2012

28. FpML Swap FpML Swap OTC POC Semantic Building Blocks and Methodology 28 FIBO Swap FIBO Swap FIBO-FpML Swap Bridge Legal Entity FIBO Swap Bridge FpML Instances SPARQL Queries 2) Build operational ontology for Swaps from FIBO 1) Build conceptual ontology for Swaps in FIBO 3) Build operational ontology for Swaps from FpML 4) Build operational ontology for Legal Entities 5) Build bridging ontologies that tie together individual ontologies 6) Ingest FpML Swap data into FpML Swap ontology 8)Invoke Reasoner to a.associate data in FpML Swap Ontology to FIBO Swap Ontology b.classify Swap Contracts into taxonomy levels according to their attributes 9) Perform queries to fulfill regulatory use cases and reports OTC POC Operational Ontologies Legal Entity FpML Swap Bridge Legal Entity Knowledge FIBO Model Upper Ontologies LE Instances 7) Ingest Legal Entity data into Legal Entity Ontology Reasoning 1/30/2012

29. OTC Derivatives Semantic POC Demonstration Swap Ontology Classification and Reasoning Semantic Query 291/30/2012

30. Semantic Building Blocks for Financial Data Standards and Risk Management Semantic Descriptions of Financial Data, Concepts, Relationships and Rules Higher Level Concepts (Upper Ontologies) MortgagesSecuritiesDerivatives Human Facing Machine Facing Descriptive Semantic Foundations for Financial Data Management Operational Data Consistency Data Traceability Data Taxonomies Data Mapping and Integration Data Classification and Categorization Inferred Conclusions and Data Linkage Graph Pattern Matching Data Federation Data Rollups and Aggregation Transparency Asset and Risk Categories Systemic Risk Analysis Trust ConceptualOntologies Data and Knowledge Representation Reasoning and Inferencing Advanced Queries Financial Data Standards Holistic Data Linkages and Bridges Runtime Knowledge Expressivity Implementation Ontologies... Financial Industry Business Ontology (FIBO) 1/30/2012 30

31. Adoption can be an Evolutionary Process that may Lead to Strategic Value Is still early in its lifecycle; tools are relatively immature and language standards are still evolving, vendors are small Does require a learning curve to understand how the “semantic reasoner” thinks in order to best utilize the technology; which can take time and investment to develop Will not necessarily replace current object oriented and relational database technology in the foreseeable future; but can be used to better enable and enhance conventional technology Positions users that are adopters of its knowledge representation and reasoning capabilities to achieve valuable benefits not easily achievable using conventional technologies by themselves Semantic Technology: 31 Making the Investment in Semantic Technology By embracing semantic technology and FIBO as a basis for enhancing financial industry data standards we are making a strategic investment to improve our data management capabilities by using the tools of the 21 st century 1/30/2012 31

32. Invitation to Financial Regulators, Market Authorities and the Financial Industry 32  Financial regulators to support and participate in a formal collaboration with financial industry participants and standards organizations such as ISDA, ISO, XBRL, FIX, MISMO, OMG, etc. to refine and implement FIBO as the standard financial instrument and entity ontology for regulatory reporting, business processing and risk analysis  Financial regulators to act as catalysts in forming a public/private partnership to create best practice reference architectures for operational semantic implementations. 1/30/2012 FIBO  Continued extension of the semantic proof-of-concept work to support the analytical requirements of regulators, market authorities and financial institutions  OTC Derivatives (Contractual Provisions, Credit Default Swaps)  Asset Backed Securities (Mortgage Backed Securities, Collateralized Debt Obligations)  Continued extension of the semantic proof-of-concept work to support the analytical requirements of regulators, market authorities and financial institutions  OTC Derivatives (Contractual Provisions, Credit Default Swaps)  Asset Backed Securities (Mortgage Backed Securities, Collateralized Debt Obligations)