semantic applications for financial services
DESCRIPTION
This presentation provides an overview of the business and technical drivers for building financial service applications using Semantic Technology. Multiple use cases are provided as examples.TRANSCRIPT
Semantic Applications for Financial Services
David NewmanStrategic Planning ManagerEnterprise Technology Architecture and PlanningWells Fargo Bank
June 23, 2010
June 23, 2010
Disclaimer
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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.
June 23, 2010
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?
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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
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Common IT Challenges at Financial Services Firms …
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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
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Often Requires IT to Surmount Difficult Obstacles …
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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!
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That May Not Always Be Solved by Conventional Technologies
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Conventional Information Technologies: What’s Wrong?
Data Schema
New Data Entity
Physical Database
New Physical Table for New Entity
Application Software
Business Rules in Code
Access
Update
Define
Knowledge is encapsulated in opaque software
Data organization is tightly coupled with the schema
Data schemas reflect limited knowledge
Schemas enforce limited data integrity
Data is siloed as is its meaning
Conventional Technology Data Definition and Access Patterns
Data fragmentation
Data redundancy
Data incompatibility
Labor intensive tasks
High costs
Slow time to market
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Conventional Relational Database Example
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CustomerCustId EntityType TaxId
David_Newman Person 999-99-9999
Checking AccountAcctId Balance Overdrafts
0001-12345 $1,000 0
Customer Account Relationship
CustId AcctId ProductId
David_Newman 0001-12345 DDA
Product Hierarchy
ProductID Group Domain
DDA DEP CONS
PersonCustId Last Name First Name
David_Newman Newman David
Product Catalog
ProductID Name
DDA Deposit Account
Awareness of the physical organization of data is necessary
Many tables are often required to capture entities and their relationships
Entity relationships are realized by joining data, mainly by its keys
Guided by Closed World Assumption – if data is not present it does not exist
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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 may still exist!
Subject(domain)
Subject(domain)
Predicate (property)
Predicate (property)
Object(range)
Object(range)
RDF Triples/ Statements
Aligns linguistically with how we think and speak!
Jackson Pollock “Convergence”
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Semantic Technology: How does it Help?New Data Entity
Ontology / Semantic SchemaPhysical Database
Some Business Rules Added to Ontology
Application Software
Some Inferred Data
Some Business Rules Removed from Code
Physical Format Unchanged after New Data Entity Added
Access
Update
Define
Semantic Technology Data Definition and Access Patterns Knowledge 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
TBox (terminology)
ABox (assertions)
Meaning is consistent
Knowledge is accessible
Applying rules to data is easier and less costly
Data access costs should be lower
Faster time to market
TBox
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Product
Consumer Product
Consumer Credit
Retail Deposit
Credit Card
HomeEquity
RetailChecking
SavingsDeposit
Customer
Financial Information Ontology
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Account Status
ClosedOpen
Business Partner
Person
BusinessEntity
Country
United States
Credit Card Eligible Customer
Credit Risk Retail Customer
Fraud Risk Retail Customer
Gold Credit Card Eligible Customer
Event Type
Online Login
AccountOpen
ChangeAddress
TransferFunds
accountStatus
Event
Online Login Event
Suspicious Online Login Event
hasEvent eventForCustomer
isEligibleFor
hasPre-Qualified
hasAccounttitleHolderhasIdentity
is Customer
hasEventType
productType
isAccount
onlineLoginEventLocation
eventForCountry
describes Event
accountForStatus
Account
ConsumerAccount
DepositAccount
Checking Account
Consumer Credit
Account
HELOC
Bad Country
Bad Country X
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Semantic RDF “Triple Store” Example
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Subject Predicate ObjectCustomer rdf:type owl:Class
Customer rdfs:subClassOf Thing
CheckingAccount rdf:type owl:Class
CheckingAccount rdfs:subClassOf DepositAccount
hasAccount rdf:type rdf:Property
hasAccount rdfs:domain Customer
hasAccount rdfs:range Account
hasAccount owl:inverseOf titleHolder
titleHolder rdf:type rdf:Property
titleHolder rdfs:domain Account
titleHolder rdfs:range Customer
titleHolder owl:inverseOf hasAccount
David_Newman rdf:type Customer
0001-12345 rdf:type CheckingAccount
David_Newman hasAccount 0001-12345
0001-12345 titleHolder David_Newman
0001-12345 rdf:type DepositAccount
InferredTriples
ABox
Assertions FactsData
TBox
TerminologyOntology Schema
Every element is a Web addressable URI!
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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:
1. Linked Enterprise Data: 360 Degree Customer View
2. RDFa Enablement of Online Financial Services and Products
3. Fraud Detection
4. Eligibility and Suitability Rules
5. Credit Risk Management
6. Integrated Financial Statements
7. Concept Extraction and Categorization from Unstructured Text
8. Market Intelligence for Investment Analytics
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Linked Enterprise Data: 360 Degree Customer View
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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
Deposits
Stores
OnlineBanking
Loans
Corporate
Customer
“Customer Centric”EnterpriseData Cloud
360 degree view of customer
“KYC”Know YourCustomer
Credit Bureau
Risk
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Financial Ontologies
RDFa Enablement of Online Financial Services & Products
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Semantic search engine optimization, semantic marketing and sales
Growing evidence that RDFa will: improve rank on Search Engines
increase traffic to site
improve click-thru rates
FIs can RDFa enable: products information , e.g. terms, rates for:
loans, CDs, checking, savings, etc.
services e.g. bill payments, financial advice
Context based semantic search
Semantic agents Agent initiated search
Agent initiated filtering
Agent initiated transactions
Base Ontology
RDFa Enabled Web Page
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Fraud Detection
Effective Pattern Detection and Event Correlation From a set of known facts (Events), and a set of rules (Axioms), a
Reasoner infers membership in classes that reflects a specific pattern indicating fraud risk
Expectation of reduced cost in comparison to conventional technologies
alter Tbox dynamically to define new patterns and relationships
yields faster results, lower maintenance and deployment costs
Utilizes Open World Assumption (OWA) Reasoning
Effective Link Analysis Once a common entity is known e.g. bad phone number; other risky
relationships may emerge by invoking queries that perform Graph Pattern Matching to identify fraud networks or other victims
Unique Naming Assumption not supported by OWA
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Customer
Retail Customer
Fraud Risk Pattern
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Country
BadCountry
Bad Country X
Product
Consumer Product
Event Type
Online Login AccountOpen
ChangeAddress
hasEvent
hasAccount
isEventType
productType
onlineLoginEventLocation
Account
Consumer Account
Equivalent Class:
Account and productType some ConsumerProduct
Event
Online Login Event
Suspicious Online Login Event
Equivalent Class:
eventForCustomer some Customer and isEventType value OnlineLogin and onlineLoginEventLocation some BadCountry
Which consumer customers might be atrisk of Online Account Takeover Fraud?
Fraud Risk Retail Customer
Equivalent Class:
RetailCustomer and hasEvent some SuspiciousOnlineLoginEventand hasEvent some (Event and isEventType value AccountOpen)and hasEvent some (Event and isEventType value ChangeAddress)
Answers the Query:onWatchListFor some OnlineAccountTakeoverand returns a set of customers at risk
onWatchListFor
Risk Category
OnlineAccountTakeovern
Equivalent Class:
Customer and hasAccount some ConsumerAccount
eventForCustomer
Note: Semantic solutions, other than OWL DL, could also be used to achieve the same results
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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
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Product
Credit Card Eligibility:
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Account Status
ClosedOpen
Consumer Product
Consumer Credit
Retail Deposit
ConsumerCredit Card
RetailChecking
SavingsDeposit
Customer
Credit Card Eligible Customer
Account
Retail Checking Account accountStatus
isEligibleFor
hasAccount
productType
Doublebalance
IntegeroverDraftsMos
Equivalent Class:
Customer and hasAccount some (RetailCheckingAccount and accountStatus value Open and balance some double[> 1000.00] and overdraftsMos some nonNegativeInteger[< "1”])
Equivalent Class:
Account and productType some RetailChecking
Answers the Query:isEligibleFor some BasicCreditCardand returns a set of eligible CustomersCan also answer:isEligibleFor some GoldCreditCardand returns a set of Customers eligible for all Premium Consumer Credit Card Products
Which consumer customers are eligible for a Consumer Credit Card?
hasPrequalified
Gold Credit Card Eligible Customer
Equivalent Class:
CreditCardEligibleCustomer and hasAccount some (RetailCheckingAccount and balance some double[> 50000.00]
BasicCreditCard
Gold Credit Card
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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
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Credit Risk Management:
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Customer
Credit Risk Consumer Customer
Account
Retail Checking Account
hasAccount
productType
IntegerdelinquentDays Integer overDraftsPastMonth
Equivalent Class:
Customer and ((hasAccount some (RetailCheckingAccount and (overdraftsPastMonth some nonNegativeInteger[> 1]))) or (hasAccount some (ConsumerCreditAccount and (delinquentDays some nonNegativeInteger[>= 30]))))
Equivalent Class:
Account and productType some RetailChecking
Answers the Query:atRiskFor some ConsumerCreditRiskand returns a set of Customers at risk
Which consumer customers are at risk from a credit perspective?
Product
Consumer Product
Consumer Credit
Retail Deposit
Credit Card
HomeEquity
RetailChecking
SavingsDeposit
Risk Category
ConsumerCreditRisk
Consumer Credit Account
Equivalent Class:
Account and productType some ConsumerCredit
atRiskFor
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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)
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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.
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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
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Better Service Oriented Architecture Service Oriented Architecture
SOA is often foundational to Financial Service architectures Canonical Semantic Data Schema can auto-translate data
content from one interface protocol to another, increasing the level of interoperability
Semantic Service Repository Elements within schema defined as entities within an ontology
ensuring semantic alignment, clarity and expressiveness
Service Registry and Discovery Requires capability to advertise and locate service interfaces
defined by a Service Registry using semantic content for unambiguous context based search
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SOA
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Implications for Enterprise Architectureand Data Management Organizations Enterprise Ontology Governance:
Manage and provide standards and quality control for enterprise semantic content across the enterprise
Enable and manage an enterprise Ontology Repository Limit risk of siloed ontologies, enable federation of ontologies Evolve enterprise “Upper Ontology”
Business Semantics Management Encourage and incubate Line of Business Ontologies Influence LOBs to open their data silos for Linked Enterprise Data Provide business friendly interface that front-ends the Ontology
Semantic Technology Governance Ensure effective Access Control and Trust mechanisms are provided Ensure effective Quality of Service mechanisms are provided to achieve
desired performance, availability, recoverability (etc.) standards
EA
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(Some) Providers of Semantic Technology
Ontology Editors
Triple/RDF Stores
Middleware
PelletRacerPro
Reasoners
Sesame
OWLAPILanguages
NetKernel
Business SemanticsManagement
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(Some) Recommended Books
