http://2016.semantics.cc/reginald-ford

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Automating Financial Regulatory
Compliance Using
Ontology+Rules and Sunflower
SEMANTiCS 2016, September 12-15, 2016, Leipzig, Germany
Reginald Ford, Grit Denker, Daniel Elenius
SRI International
Wesley Moore, Elie Abi-Lahoud
Quarule, Inc

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$3M fine and COO barred from
industry: SEC Charges Owner of N.J.-
Based Brokerage Firm With
Manipulative Trading (May 1, 2014)
Ignored a Rule
Failed to Supervise
Violated a Rule
Failed to Report
Layering…gamed the market
Violated a Rule

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Fines and Penalties since 2007*
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* Committee on Capital Markets Regulation

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Core Work of Compliance Programs
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An Example: Regulation W
• The Federal Reserve Board’s (FRB) Regulation W (Transactions Between
Member Banks and their Affiliates) implements Sections 23A and 23B of
the Federal Reserve Act (FRA).
– Bank affiliate includes any company that controls the bank, any company under
common control with the bank, and certain investment funds that are advised by
the bank or an affiliate of the bank.
• Protects the financial integrity of banks, for example:
– Limits RegW-covered transactions with affiliates that are not subsidiaries
– Imposes collateral requirements on extensions of credit
– Prohibits the purchase of low-quality assets by banks from their Reg W affiliates
or sister banks
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RegW Covered Transactions, Exemptions, and
Prohibited Transactions
• Covered transaction is one that the bank is required to report under RegW
rules, e.g.:
– Securities issued by affiliates
– Securities purchased from an affiliate
• Exemptions, e.g.,
– Intraday credit to affiliates
– Riskless principal transactions
– Purchase of assets, other than securities issued by affiliates, that have ready,
liquid markets for buying and selling
• Not permitted, e.g.:
– Transactions with counterparty exceeds 10% of bank’s capital stock and surplus
– Transactions with all counterparties exceeds 20% of bank’s capital stock and
surplus
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Approach to Regulatory Compliance
Subject Matter Experts
and Technologists
Regulations,
Related Laws,
P&Ps, etc.
Production Data
from IT systems,
spreadsheets, etc.
Monitoring &
Surveillance
Reports
SMEs and Technologists
capture Regulations and
Policies in a machine-
understandable format
The System executes Machine
reasoning over
regulations/Policies and your
data
To produce Surveillance and
Monitoring reports that can be
reconfigured, escalated and
regenerated
Sunflower
Based
Intelligent
Desktop or
Web
Service
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8. About the EDM Council
• The EDM (Enterprise Data Management) Council is a financial services industry consortium,
working to improve data management across the industry
• One of their primary focus areas is on content, and in particular, on development of content
standards that define and facilitate conversations about
 Business entities
 Financial instruments
 The terms and conditions of financial contracts
 Classification to support aggregation and analysis
• Membership includes
 The largest banks in the US (Bank of America, Citigroup, Bank of New York Mellon, JP Morgan Chase,
State Street, Wells Fargo …) and internationally (Bank of Tokyo, Barclays, Canadian Imperial Bank,
Credit Suisse, HSBC, UBS, and many others)
 The largest insurance companies and brokerage firms
 The relevant regulatory agencies (CFTC, OFR, SEC, FINRA, FDIC, Federal Reserve, etc.)
 Many of the largest consulting firms and vendors who operate in the financial services industry
Copyright © 2013 Thematix Partners LLC

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FIBO Example: Classification of Securities (fragment)
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Leveraging Ontology with Rules
• Ontologies provide a conceptual framework for modeling domain
knowledge, but . . .
• When it comes to modeling complex policies and regulations, we need
more expressiveness in the form of rules
• Automated reasoning and inferencing tools can use rules to infer new facts
from existing facts and data
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Why Flora-2 and Not OWL+SWRL for Ontology+Rules?
• Flora-2 is a highly expressive knowledge representation language and
associated reasoning engine
– Developed and maintained primarily by Michael Kifer of SUNY-Stony Brook and
Coherent Knowledge Systems
– Built on top of XSB-Prolog
• Flora-2 advantages over OWL+SWRL include:
– N-ary formulas, negation-as-failure, aggregation, higher-order predicates,
functions, frame syntax for classes and instances, infix mathematical expressions,
prioritized or default rules, and knowledge base update operators
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What is Sunflower?
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Sunflower Highlights
• Synchronized suite of tools for ontology and rule editing,
understanding, and validation
– Text and graphical editors, search, graph views, query UI
• Provides English explanation of analysis results
• Provides importers from various formats (SQL, CSV, OWL, RDF,
SWRL) and live connectors to databases/triple stores
• Provides an API to enable integration with enterprise solutions
• Declarative semantic language supports faster development than
traditional procedural software
– Changes easily made, for example, to perform “What If” Analysis
• Provides server for web applications
• Supports reasoning about hard and soft constraints and thus can
rank solutions
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List of loaded projects
and ontologies and
Knowledge Bases (KBs)
Textual view of rules, ontologies and KBs (Flora-2)
Sunflower IDE Main Window
The IDE is for people with some training in semantic technologies.
We also have specialized web browser interfaces for end-users.
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FIBO Ontologies Translated from OWL to Flora
• FIBO OWL ontologies and SWRL rules are
translated (within seconds) into Flora.
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Example Queries
Questions to answer:
• Am I in compliance with RegW? Yes/No
• Why? / Why not?
CounterpartyBank
1) Is counterparty an affiliate?
2) Is transaction covered?
3) Is amount permitted?
Covered transactions with an affiliate cannot exceed 10 percent of a
bank's capital stock and surplus, and transactions with all affiliates
combined cannot exceed 20 percent of the bank's capital stock and
surplus.
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Reg-W Covered Transaction Scenarios (extract)
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Subject Matter Experts (SMEs) described 17 Reg-W scenarios

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Reg-W Rule Development
Translate each row into a formal, machine-readable Flora-2 rule
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Transaction Trade Data
Automatic ingestion of production data into
Sunflower: CSV & SQL DB importer
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Reg-W Transaction (in Ontology Editor)
Transaction
instances
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Queries
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Query Results for “Which transactions are (or are not)
reportable and permitted?”
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Understanding and Tracing Analysis Results
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Comparing Results
• Analysis results can be stored and compared to previous analysis results
(e.g., data changes, hypothetical scenarios)
1. Save analysis results
2. Change the Moody rating of the CVS_Care asset from Baa1 to BBB+ using the
ontology editor
3. Re-execute query and compare the result
• New results in boldface, old results italicized and grayed out
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Sunflower Benefits For Regulatory Compliance
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Declarative
interpretations of
regulations shared
across the enterprise
Compliance officer can
modify declarative rules
locally/provisionally to
perform “what if” analysis
Transparent compliance decision making
Automatic ingestion of/
live connection to data
from IT SQL/RDF
systems, spreadsheets,
etc.
Rationale behind
compliance analysis
results in English
Can ask “why not?” as
well as “why?”

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Additional U.S. and
international locations
www.sri.com
Thank You
Questions?
Contacts:
grit.denker@sri.com
reginald.ford@sri.com
daniel.elenius@sri.com
wesley.moore@quarule.com
elie.abi-lahoud@quarule.com

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Backup slides
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Benefits for Regulatory Compliance
Issue Current Sunflower
Adapt to new regulations &
controls
$30K-$500K+
3-12 months
$10K-$40K
1 week to 1 month
Automated monitoring for rule
violation
0-20% >95%
Rate of false positives 75-95% <10%
Prevention and Detection N/A ~40%
Demand for qualified
compliance staff
High Low
Sharing across enterprise Hours or days
(sometimes never)
Seconds or minutes
Data integration Manual Automated
Transparency Poor Results easily understood
by all stakeholders
Stakeholder collaboration Compliance officer, indirect ALL Stakeholders, direct
Evidence chain Obscure and scattered
(Spreadsheets, e-mail, etc.)
Integrated evidence chain
with traceable provenance
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Why Flora-2?
Open source, human readable, expressive rules,
ontologies, semantic web features

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Flora-2/Eclipse vs. OWL/SWRL/Protege
• Much more powerful rule language than SWRL
– Proper negation, if/then/else, aggregation, higher order, n-ary predicates, etc
– SWRL can’t create new individuals (only new numbers)
– Built-in type checking and metadata support
– Debugging statements in rules, error messages with line numbers
• HiLog is expressive enough for the “meta modules” (tracer, natural
language, task engine)
– This makes it easier to make changes to these modules and adapt them for
different purposes
– Results in improved tracing & natural language result explanation
• Human readable source
– Easy to see/edit things in context
– Syntax highlighting
– Supports diffing
– Avoids corruptions
• Easier to add more views to the UI as Eclipse plugins
• Based on XSB Prolog, maintained by Michael Kifer from SUNY Stony Brook