FIBO: A Common Language for the Financial Industry

1. FIBO: A Common Language for theFinancial Industry David NewmanSVP, Strategic Planning Manager, Enterprise Architecture Chair, Semantic Technology Program, Enterprise Data Management Council December, 2013

2. Wells Fargo Disclaimer • The content in this presentation represents only the views of the presenter and does not represent or imply acknowledged adoption by Wells Fargo Bank. Examples used within are purely hypothetical and are used for illustrative purposes only and are not intended to reflect Wells Fargo policy or intellectual property.

3. Financial Industry Business Ontology FIBO is a common financial industry language and conceptual model developed to precisely define and harmonize data about financial contracts • Goal: to support integration of data from multiple sources and align content across business silos and organizations. • Aligned, comparable and harmonized data is essential to: • reduce data reconciliation errors and transformation processes • aggregate data across multiple lines of businesses and organizations • automate business processes • improve risk analysis The overall objective is to provide consistent data and reporting to business users, executive management, regulators and market authorities

4. 2008 Global Financial Crisis Stimulated Need for a Common Financial Language • Financial industry needs expressive global data standards for: • identification of legal entities, their jurisdictions and ownership control hierarchies • Identification of financial contracts and instruments • classification and data linkage for aggregation • actionable risk intelligence “One of the most significant lessons learned from the global financial crisis that began in 2007 was that banks’ information technology (IT) and data architectures were inadequate to support the broad management of financial risks. Many banks lacked the ability to aggregate risk exposures and concentrations quickly and accurately at the bank group level, across business lines and between legal entities. “ Principles for effective risk data aggregation and risk reporting Basel Committee on Banking Supervision, June 2012

5. Regulatory Reporting Current State ? Reports (forms) FORMS FORMS REPORTING ENTITY REGULATORY AUTHORITY • Change in Reporting requirements = • Redevelopment effort • By each reporting entity • For each system and form • Uncertainty • Content of the reports • Are we comparing like with like? • Data quality and provenance

6. Regulatory Reporting using a Common Financial Data Language REPORTING ENTITY REGULATORY AUTHORITY Data is mapped from each system of record to a common financial language Receives standardized, granular data aligned with common financial language

7. Business Data Challenges Facing Financial Institutions Today Data linkage and integration despite silos Open global reusable data standards Alignment based on meaning Highly expressive data schemas with built in rules that reflect concepts Flexible changeable schemas Rich multi-level taxonomies • Data incongruity and fragmentation often found across silos • Limited data standards • Data rationalization problems • Costly application program logic required to process data into concepts • Brittle schemas are costly to change • Rigid and limited taxonomies Current State of Business Data Desired State of Business Data

8. How Should These Regulatory and Business Data Challenges Be Resolved? • How should a common financial language be defined? • How should the financial industry tackle these risk data management, aggregation and reporting challenges? • What technologies should be employed to fulfill these requirements?

9. Semantic Web Technology can be Used to Meet These Data Challenges • The Enterprise Data Management (EDM) Council and the Object Management Group (OMG) believe that semantic web technology • is a transformational technology for defining financial data standards • is a prudent forward-leaning information management investment that will support our evolving data needs for many years to come • can map to and supplement existing legacy financial data standards

10. FIBO: A Semantically Defined Common Financial Language and Model Collaborative industry initiative to describe financial data standards using semantics Industry Standards Securities Business Entities FpML MISMO Derivatives FIX ISO Metadata Corporate Actions Loans Built in MDDL XBRL Open semantic financial data standards are exchangeable across financial institutions and regulatory authorities for data confidence, consistency and transparency

11. Semantic Technology Machine Understanding Ontology Human Understanding $ $ Machine Intelligence Reduced software costs Knowledge Base Semantic Database Rapid change Faster time to market Network Graph of Data Insights from Reasoning Conventional Data

12. Semantic Technology Basics: Describing Concepts with Ontologies Concept of “Employment” Object <<Class>> Company Subject <<Class>> Person Predicate <<PropertyClass>> worksFor has an type <subProperty> type Data (Assertion) Data (Assertion) Predicate <<PropertyClass>> isEmployedBy Assertion Assertion David <inverse> type inference type inference Predicate <<PropertyClass>> employs Object <<Class>> Employee Object <<Class>> Employer

13. FIBO Conceptual Ontology Example

14. FIBO Foundations FIBO Foundations Ontology • FIBO Foundations provides the basic conceptual “Glue” • Common abstractions grounded in law and business Common business modeling framework Common relations Roles Goals, Objectives Agents, People Organizations Agreements Law Control, Ownership Location Accounting

15. FIBO Business Entities FIBO Business Entities Ontology • Types of corporate structure • Organizational hierarchies / relationships Entity Types Legal Persons Formal Organization Corporations Partnerships Trusts Ownership Control By Function Legal Entity ID

16. Industry review Industry review OMG finalization OMG finalization OMG finalization OMG finalization OMG finalization Industry review Industry review Industry review FIBO Provisional Roadmap FIBO Derivatives Domain ontology FIBO Loans Domain ontology FIBO Business Entity Domain ontology FIBO Market Data, CAE, Risk/Reporting Other Domain ontologys FIBO Market Data, CAE, Risk/Reporting Other Domain ontologys FIBO Securities Domain ontology FIBO Market Data, CAE, Portfolio, Payments Other Domain ontologys FIBO Market Data, CAE, Risk/Reporting Other Domain ontologys FIBO-Foundations Global Terms and modeling framework Final Final Final Final Final 2013 2014-2015 2012 Q1 Q2 Q3 Q4

17. Target Operational Capabilities of FIBO Leverage and integrate with other global data standards to maximize commonality and reuse (W3C, ISO, schema.org) 1 2 Provide risk intelligence e.g. identifying risk exposures across legal entity ownership hierarchies and their counterparties 7 Enable visualizations for taxonomies, financial instruments, all forms of data relationships 5 Provide standard definitions of financial contracts, concepts and business rules; financial instrument taxonomies, integrated metadata and links to related data e.g. policy and compliance rules; for human and machine consumption Enable risk data aggregations across multiple dimensions and taxonomies 6 Classify financial instruments into categories and flags instruments that lack compliance to data standards to better ensure reliability and conformity 3 Provide semantic mapping from FIBO elements to other standards e.g. ISDA UPI taxonomy, FpML, MISMO, XBRL, etc for automatic linkage and integration 4

18. Semantic Operational Processing Reasons over Data to Infer Classifications and Relationships isTradingWithis a new property relationship that is inferred based on a semantic rule and can be queried isTradingWith Fixed Float IR Swap (Ontology) Business Entity Business Entity Interest Rate Swap 4 Semantic reasoning type Inferred Swap is inferred to be a Fixed-Float IR Swap because one leg was inferred to be fixed and one leg was inferred to be floating fulfilling the definitions in the ontology Machine Facing Definition Swap_Contract and hasLegFixedRateLegand hasLegFloatingRateLeg Fixed Float IR Swap identifies identifies type Inferred Human Facing Definition Trader LLC Acme Inc An interest rate swap in which fixed interest payments on the notional are exchanged for floating interest payments. LEI LEI 3 Semantic reasoning hasLeg hasLeg party Swap party LEI5001 LEI7777 FloatingRateLeg Inferred Inferred FixedRateLeg type type notional index fixedRate notional Leg1 is inferred to be a FloatingRateLeg because any leg tied to an index is semantically defined as floating 10000000 Leg2 is inferred to be a FixedRateLeg because any leg tied to an interest rate is semantically defined as fixed LIBOR Swap1001 10000000 Leg 1 Leg 2 3.5% currency currency Data for an undefined Swap Contract before semantic reasoning performs classification and identification Semantic reasoning 1 USD Semantic reasoning 2 USD

19. FIBO Derivatives Contract Taxonomy (partial view) Derivatives_Contract Poly-hierarchicalclass … Swap_Contract Rate_Based_Derivatives_Contract Poly-hierarchicalclass Rate_Based_Swap_Contract Interest_Rate_Derivatives_Contract Is both a Swap Contract and a Rate Based Derivatives Contract Interest_Rate_Swap_Contract Faceted class Faceted class Currency_IR_Swap_Contract Interest_Rate_Based_Swap_Contract Facets provide different views of the same instruments Fixed_Float_IR_Swap_Contract Cross_Currency_IR_Swap_Contract FIBO maps this to the ISDA Fixed-Float-Cross-Currency Swap Fixed_Float_IR_Swap_Different_Currencies_Contract Fixed_Float_IR_Swap_Different_Currencies_Single_Step_Notional_Contract Classification and class names are based upon the attributes of the contract Swap FIBO performs semantic reasoning to infer the class of the swap Party Party Fixed_Rate_Leg Floating_Rate_Leg Single_Step_Notional_Amt Single_Step_Notional_Amt Currency Index Rate Currency FIBO detects different currencies

20. FIBO Can Play a Useful Role in Risk Intelligence Transaction Repository, et.al. • ISDA Master Agreement • Schedules • Credit Support Annex • Schedules Events Credit Rating Agency FpML OTC Derivative Confirm Capture Semantics of Contractual Provisions FIBO Operational Ontologies FIBO Operational Ontologies Financial Shocks Reduce Value of Collateral Downgrade Counterparty Credit Counterparties Axioms and Rules Classify Counterparties into Risk Categories for Analytics Infer Counterparty Transitive Exposures Market Reference Data Default Events Termination Events Identify Key Contractual Actions Identify Key Contractual Events Infer Capital, Liquidity Risks et al. Classify Contract Type by Cash Flow Risk Analyst FIBO Ontologies Transfer Payments Increase Collateral * W. Brammertz, “Unified Financial Analysis: The Missing Links of Finance”, 2009 • **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

21. Legacy Data can be Processed Semantically Without Requiring Conversion or Migration Legacy data can be collected from diverse sources, mapped, integrated with FIBO, classified (based upon alignment with business concepts) and then aggregated for query and reporting purposes Integrated data Excel to Ontology Mappings Excel Adapter Excel interface query results Semantic Information Integration Platform Spread sheets semantic query One-time mapping between legacy data and ontologies is necessary. Tools can automate most of the mapping effort. semantic query semantic query Relational Adapter Relational Adapters SQL Reports SQL to Ontology Mappings SQL Semantic Triple Store Semantic vendors provide tools for data federation and integration Credit Default Swap Table Interest Rate Swap Table User Interest Rate Swap Map Legacy Relational Databases Credit Default Swap Map Only the data required for queries and reporting must be mapped FIBO Swap Contracts Ontology Ontologies

22. Proposed FIBO Architecturefor Institutionaland Macroprudential/Regulatory Oversight OFR FSOC CFTC Financial Institutions Swap Data Repositories Semantic Network Graph Analysis Trading System Mapping Mapping Harmonized Data Harmonized Data Mapping Trading & Compliance System(s) Swap Trade & Regulatory Reporting FpML Swap Data Repository System(s) CFTC System(s) Harmonized Data Mapping Mapping Semantic Information Integration Platform Semantic Information Integration Platform Semantic Information Integration Platform Mapping Mapping Validation Validation Validation Classification Classification Classification Regulatory Risk Analyst Institutional Risk Analyst Ontologies Ontologies Ontologies Legacy Database(s) Legacy Database(s) Legacy Database(s) Semantically defined financial data standards enhances data quality and fidelity between institutions and regulators, improving confidence and reducing perception of risk FIBO PROVIDES A COMMON LANGUAGE ACROSS ALL PARTIES

23. Appendix

24. FIBO Describes Business Concepts for Humanand Machine Understanding Protégé Ontology Editor Tool

25. FIBO has a Highly Expressive Financial Instrument Taxonomy FIBO has a Rich Multi-Tiered Taxonomy that can be used to Classify and Aggregate Data at Many Levels and Across Many Facets (subset of taxonomy shown in diagram) FIBO maps to other product taxonomies e.g. ISDA Gruff SPARQL query tool and Allegrograph Triple Store from Franz, Inc

26. FIBO Provides Comprehensive Visualizations of Business Concepts FIBO enables high level concepts e.g. a Fixed Float IR Swap, along with its key related concepts, to be queried for access and viewing This example is the result of a single query against the FIBO semantic glossary

27. Legal Entity Ownership and Control Relationships can be Queried and Displayed Semantic web enables data visualizations which are more holistic and descriptive than basic columnar views FIBO aligns with LEI Legend

28. FIBO Identifies Ultimate Parents, their Descendents and Trading Counterparties This capability allows for the rollup of both positions and exposures of the subsidiaries to the level of the ultimate parent for risk analysis

29. Visualization of Ownership Hierarchies and Exposures to Counterparties Solid blue lines represent ownership and control relations. Violet lines represent exposures due to trading Legend

30. FIBO can Provide Input to Graph Analytics Software to Describe Risk Score = centrality of network positions Node size based on grand total aggregate amount at risk for entity Line size based upon aggregate amounts at risk between trading parties