This document discusses how semantic technology can provide benefits for financial services organizations. It begins by outlining common IT challenges at financial firms, such as incompatible data definitions and fragmented data. Semantic technology is presented as a way to standardize data meaning, capture business knowledge, and foster data integration. Specific use cases are described, such as creating a 360-degree customer view, enabling online financial products with RDFa, fraud detection using ontologies and reasoning, and credit risk management. The document concludes by discussing implications for enterprise architecture, such as ontology governance and business semantics management.

1. Semantic Applications for Financial Services David Newman Strategic Planning Manager Enterprise Technology Architecture and Planning Wells Fargo Bank June 23, 2010

2. Disclaimer 1 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. What Benefits Does Semantic Technology Provide for Financial Services Organizations? What are some of the business and technology drivers for Semantic Technologies from a Financial Services perspective? What are some of the critical business and technology problems that Semantic Technology attempts to remedy? What are some limitations with conventional Information Technologies that Semantic Technology improves upon? What are some Financial Service use cases that can demonstrate benefit by using Semantic Technology? 2

4. IT Organizations are often asked by the Business to: provide a holistic, comprehensive, integrated view of the Customer fulfill major data and system integration initiatives cross organizational and system boundaries to accomplish this deliver all of the above functionality faster, cheaper, smarter 3 Common IT Challenges at Financial Services Firms …

5. This must often be accomplished in environments where there exists: a preponderance of incompatible data definitions, vocabulary multiple incompatible physical data and file formats, databases, storage mechanisms a proliferation of fragmented, redundant data a proliferation of unstructured data that is inaccessible to most users dissonance between the business stakeholders definition of data and processing rules and how such data and rules are actually codified within application software Can result in high costs, slipped dates 4 Often Requires IT to Surmount Difficult Obstacles …

6. Requires New and Innovative Tools that will help IT organizations to: standardize and unify the meaning of data across the enterprise capture and persist business and technical knowledge as information assets foster data integration despite organizational boundaries give greater control to the Business for definitions of data and business rules produce better results faster and cheaper than conventional technologies Semantic Technology can help to achieve these goals! 5 That May Not Always Be Solved by Conventional Technologies

8. Data redundancy

9. Data incompatibility

10. Labor intensive tasks

11. High costs

13. Many tables are often required to capture entities and their relationships

14. Entity relationships are realized by joining data, mainly by its keys

15. Guided by Closed World Assumption – if data is not present it does not exist7

16. 8 Aligns linguistically with how we think and speak! Subject (domain) Predicate (property) Object (range) RDF Triples/ Statements What is Semantic Technology? Major step towards reducing data chaos Based upon Description Logic A mathematically verifiable symbolic logic that allows reasoning about entities and the many properties that describe entity relationships Describes entities in terms of: Concepts (classes) Relationships (properties) Individuals (instances) Makes inferencing possible Infers relationships and memberships in classes per axioms via a “Reasoner” Guided by Open World Assumption If data is not present it maystill exist! Jackson Pollock “Convergence”

18. Knowledge is accessible

19. Applying rules to data is easier and less costly

20. Data access costs should be lower

21. Faster time to marketKnowledge is open and represented by an ontology Meaning and relationships of data defined Data organization is decoupled from the schema Inferencing creates new knowledge All semantic data is Web addressable

22. Financial Information Ontology 10 Business Partner Account Account Status account Status Consumer Account Person Closed Open account ForStatus Deposit Account Consumer Credit Account Product product Type Checking Account Business Entity HELOC is Account Event Type hasAccount titleHolder is Customer has Identity Customer Online Login is Eligible For Consumer Product Credit Card Eligible Customer Account Open Gold Credit Card Eligible Customer has Pre- Qualified Consumer Credit Credit Card Home Equity Credit Risk Retail Customer Change Address Fraud Risk Retail Customer Transfer Funds Retail Deposit Country online Login Event Location describes Event United States Savings Deposit hasEvent eventForCustomer Retail Checking has Event Type Event Bad Country Online Login Event event For Country Bad Country X Suspicious Online Login Event

23. Semantic RDF “Triple Store” Example 11 Every element is a Web addressable URI! TBox Terminology Ontology Schema Inferred Triples ABox Assertions Facts Data

24. How Can We Apply Semantic Technology to Specific Financial Services Use Cases for Maximum Benefit? The following use cases represent a sampling of ways that Semantic Technology can be effectively applied in a Financial Services organization: Linked Enterprise Data: 360 Degree Customer View RDFa Enablement of Online Financial Services and Products Fraud Detection Eligibility and Suitability Rules Credit Risk Management Integrated Financial Statements Concept Extraction and Categorization from Unstructured Text Market Intelligence for Investment Analytics 12

25. Linked Enterprise Data: 360 Degree Customer View 13 Semantically enabled data that is Web addressable and “inter-linked” across the enterprise Transcends organizational boundaries and provides universal access to data wherever it resides within the enterprise (and externally) Reduces redundancy by obtaining data from its “virtualized” source Integrating data in a siloed environment is a major win for Financial Information Systems “KYC” Know Your Customer Deposits Online Banking Customer 360 degree view of customer Loans Stores “Customer Centric” Enterprise Data Cloud Corporate Twitter Facebook Risk Credit Bureau

27. Semantic agents

28. Agent initiated search

29. Agent initiated filtering

31. Fraud Risk Pattern 16 Which consumer customers might be at risk of Online Account Takeover Fraud? Account Country Consumer Account Answers the Query: onWatchListFor some OnlineAccountTakeover and returns a set of customers at risk Product Equivalent Class: Account and productType some ConsumerProduct BadCountry product Type Bad Country X Consumer Product Risk Category hasAccount onlineLoginEventLocation OnlineAccountTakeovern Customer Event event For Customer Retail Customer Online Login Event Suspicious Online Login Event Equivalent Class: Customer and hasAccount some ConsumerAccount Equivalent Class: eventForCustomer some Customer and isEventType value OnlineLogin and onlineLoginEventLocation some BadCountry has Event Fraud Risk Retail Customer Equivalent Class: RetailCustomer and hasEvent some SuspiciousOnlineLoginEvent and hasEvent some (Event and isEventType value AccountOpen) and hasEvent some (Event and isEventType value ChangeAddress) isEventType Event Type Online Login Account Open Change Address onWatchListFor Note: Semantic solutions, other than OWL DL, could also be used to achieve the same results

32. Eligibility and Suitability Rules Outbound marketing campaign extractions based upon pre-qualification of customers for specific products Cross-Sell and Offer Generation Online preferences and Personalization Eligibility rules for: Account Acquisitions Loan Originations Money Movement Transactions 17

33. Credit Card Eligibility: Which consumer customers are eligible for a Consumer Credit Card? Account Account Status Retail Checking Account account Status Closed Open balance Equivalent Class: Account and productType some RetailChecking Double overDraftsMos Product Integer product Type Consumer Product hasAccount Consumer Credit Customer ConsumerCredit Card BasicCreditCard Credit Card Eligible Customer Gold Credit Card is Eligible For Equivalent Class: Customer and hasAccount some (RetailCheckingAccount and accountStatus value Open and balance some double[> 1000.00] and overdraftsMos some nonNegativeInteger[< "1”]) Retail Deposit Savings Deposit has Prequalified Retail Checking Gold Credit Card Eligible Customer Answers the Query: isEligibleFor some BasicCreditCard and returns a set of eligible Customers Can also answer: isEligibleFor some GoldCreditCard and returns a set of Customers eligible for all Premium Consumer Credit Card Products Equivalent Class: CreditCardEligibleCustomer and hasAccount some (RetailCheckingAccount and balance some double[> 50000.00] 18

34. Credit Risk Management Identify levels of credit risk by vetting a set of facts collected about the customer with a set of rules that govern risk 19

35. Which consumer customers are at risk from a credit perspective? Credit Risk Management: Account Retail Checking Account Consumer Credit Account Equivalent Class: Account and productType some RetailChecking Equivalent Class: Account and productType some ConsumerCredit Product product Type Consumer Product Integer delinquentDays Integer overDraftsPastMonth Consumer Credit hasAccount Credit Card Home Equity Customer Credit Risk Consumer Customer Retail Deposit Equivalent Class: Customer and ((hasAccount some (RetailCheckingAccount and (overdraftsPastMonth some nonNegativeInteger[> 1]))) or (hasAccount some (ConsumerCreditAccount and (delinquentDays some nonNegativeInteger[>= 30])))) Retail Checking Savings Deposit Risk Category ConsumerCreditRisk atRiskFor Answers the Query: atRiskFor some ConsumerCreditRisk and returns a set of Customers at risk 20

36. Integrated Financial Statements Semantically aligned financial statements ability to roll up financial information across disparate internal business units and external companies so that Financial Reports can be published/understood with higher levels of reliability and trust attain holistic view of organization’s financial health improve financial risk management for enterprise and investments XBRL (Extensible Business Reporting Language) SEC has mandated US public companies file financial reports using XBRL 3Q09 (proliferating globally) RDF/OWL enabled XBRL XML Leverage benefits of semantic technology using XBRL W3C Interest Group Rhizomik Initiative (ReDeFer  XML2RDF,XSD2OWL) 21

37. Concept Extraction and Categorization from Unstructured Text eDiscovery - categorizing, searching and accessing structured and unstructured content often for legal purposes Semantic metadata tags associated with content for optimized search Intentionally asserted by user Automatically asserted by using semantic entity extraction and Natural Language Processing (NLP) tools to identify conceptual meaning of unstructured content Customer Related Concept Extraction Semantic parsing of unstructured text using NLP and concept extraction to identify: Meaning of customer emails for Voice of the Customer Directing notifications to Bankers based upon accurate categorization of content from text for Lead Management purposes. etc. 22

38. Market Intelligence for Investment Analytics Open Source Intelligence capabilities leverage semantic analysis of news feeds and Web content pertaining to companies of interest for investment purposes Low Latency Critical Notifications to Analysts provides rapid categorization of content and processing against a set of semantically defined criteria (axioms) in order to send notifications to analysts for further investigation when an event of interest is identified 23

40. SOA is often foundational to Financial Service architectures

41. Canonical Semantic Data Schema can auto-translate data content from one interface protocol to another, increasing the level of interoperability

42. Semantic Service Repository

43. Elements within schema defined as entities within an ontology ensuring semantic alignment, clarity and expressiveness

44. Service Registry and Discovery

45. Requires capability to advertise and locate service interfaces defined by a Service Registry using semantic content for unambiguous context based search24

47. Manage and provide standards and quality control for enterprise semantic content across the enterprise

48. Enable and manage an enterprise Ontology Repository

49. Limit risk of siloed ontologies, enable federation of ontologies

50. Evolve enterprise “Upper Ontology”

51. Business Semantics Management

52. Encourage and incubate Line of Business Ontologies

53. Influence LOBs to open their data silos for Linked Enterprise Data

54. Provide business friendly interface that front-ends the Ontology

55. Semantic Technology Governance

56. Ensure effective Access Control and Trust mechanisms are provided

58. 27 (Some) Recommended Books

59. 28 Follow-up or Questions Email: david.newman@wellsfargo.com