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© 2016 IBM Corporation
Jeremy DoyleWatson Financial ServicesMay 24th, 2017
Delivering Unified Surveillance with IBM Surveillance Insights for Financial Services Surveillance
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Agenda
Industry Perspective
Introducing Surveillance Insights
Voice Surveillance
Closing Thoughts
3
Key Takeaways
1. Behaviour and Risk Profiling
Behaviour analytics is a critical component of any future surveillance solution moving forward. It is the only way as an industry we can truly realise Unified Surveillance.
2. Machine Learning
ML needs to be at the heart of your surveillance technology platform. Supervised learning will significantly reduce false positives and ensure adaptive risk model for ever increasing complexities of risk. If cognitive solutions are deployed correctly, it will be an asset that apricates value over time, rather than decline like current rules based systems
3. Collaboration
As principles of open API economy and cloud become more established within the Financial Services sector, we will see tight collaboration between banks to solve the current challenges and costs associated with risk and compliance.
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Three capabilities differentiate cognitive systems from traditional programmed computing systems…
ReasoningTheyreason.Theyunderstandunderlyingideasandconcepts.Theyformhypothesis.Theyinferandextractconcepts.
LearningTheyneverstoplearninggettingmorevaluablewithtime.Advancingwitheachnewpieceofinformation,interaction,andoutcome.Theydevelop“expertise”.Understanding
Cognitivesystemsunderstandlikehumansdo.
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What do we mean when we discuss Surveillance?
Elder FraudClient
Suitability
Conduct Risk
Sales Practices
Market Abuse
Complaints
CollusionInsiderTrading…
Pump&DumpFrontRunning…
Miss-sellingMiss-representation….
SalesMalpractice….
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Opportunity to Transform Surveillance with Cognitive
Go Beyond Rules Take Holistic Approach
Leverage behaviours and machine learning toproactively identify abnormalities and potential
misconduct without pre-defined rules
Integrated analytics, focused on total conduct across activities and channels
Machine LearningbehaviourProactive
ActivityE-comms
VoiceTrade
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Surveillance Insight for Financial Services
Fuse insights to detect suspicious activities
View linkages with instant drill-down & playbacks
Build employee profiles with personality & behaviour traits
See alerts & investigationsfrom continuously updated risk models
OrderTrade
ExecutionReference
NewsSocial
EmailChat
Voice
Reduced cost of employee non-compliance & misconduct
Faster detection of sophisticated scenarios
Risk based prioritization of alerts and reduced false
positives
Transactions
Communications
External
Surveillance
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Case Study: A Proven Foundation of Uncovering Misconduct
Accurate Results
Further Expansion
Innovative Technology
First run of the system without knowledge of the scenarios resulted in:
36 tests detecting threats within the top 3% out of a database of 5,500 profiles
IBM expanded into additional industries as incidents of misconduct increased in the headlines:
• WikiLeaks• Snowden• Texas Killings
A partnership was created between DARPA and IBM to build a cognitive Insider Threat Solution to identify serious national threats. IBM has continued to develop this asset, creating Surveillance Insight for Financial Services
which helps customers better uncover misconduct
3 Main Models
• Fraud
• Espionage
• Sabotage
DARPADefense Advanced
Research Projects Agency, an agency of the U.S.
Department of Defense
Challenge
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Use case: Identifying collusion for market manipulation
+
++
Motivation
CommunicationTrading
Risk
-Communication Event Proximity
Voice
ChateComm
Anomalous Language
Repetitive Language
Voice
Offline Conversation
Cross Conversation
• Detecting collusion is highly complex.
• It requires information from multiple sources – trading, ecomms, voice, and others such as HR system.
• It requires a model that can remember new patterns of behaviour and connections and ‘self-learn’ (supervised)
• First Identify higher level risk indicators – “Trader Motivation”, “Communication Risk”, “Trading Risk and apply inference model to identify overall “Collusion Risk”:
• Communication Risk indicators eg. Repetitive language• Event proximity indicator – requires call meta data to be correlated with the alert proximity• Know Your Trader indicators – anomalies compared to the norm, • Voice anomaly indicators – requires identification offline conversations, conversations across
multiple voice channels such as turret, desk phone and mobile
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CaseStudy:RecentsuccessataTier1Bank
Modernize the communications archiving and adopt an open and flexible architecture
Consolidate and manage the supervision and surveillance of all communications and transactions
Improve market abuse detection capabilities
Replace the incumbent regulatory compliant archiving infrastructure
Enhance supervision and surveillance
Support the bank’s holistic surveillance strategy
Challenge
Phase 1 Phase 2 Phase 3
Highlights of the IBM Solution • IBM GBS services (Strategy Formulation)• Partnership with Actiance Alcatraz (Regulatory Compliant Archiving & Supervision)• IBM Surveillance Insights (Advanced Feature Extraction + Holistic Surveillance)• Entire Solution offered as SaaS on IBM SoftLayer Infrastructure
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Challenges of Integration with voice archives
*Quote Source: http://www.legaltechnews.com/id=1202752478714/-Putting-More-Ears-to-the-Ground-The-Rise-and-Challenges-of-Audio-Analytics-#ixzz43CC90IT3
Currentaudiorecordingsystemsweredesignedtocaptureandstoredataandlistentoitfromaplaybackperspectivebutlargescaleretrievalwasn’tenvisionedwhenalotofthesesystems
werecreated.*
Audioisstoredinproprietaryformats
Audioisduplicatedsothatitcanbeanalyzed
Buildingconnectorstoextractaudioforanalyticsraisesconcernoverrobustness(i.e.haveallthecallsbeenindexed?)
Limitedbulkdownloadcapabilities
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4“ “
Global Head of Legal Discovery Operations, Top 3 European Bank
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Surveillance Insights for Voice
Speech to Text
Language Analysis Tone
Analyzer
Trader Phone Calls
FLAC,PCM, WAV, Ogg, or Raw VoIP Packets
Language Translation
Complimentary Services
Converts human voice into written
word by leveraging machine to combine
information on grammar and
language structure with knowledge of the composition of
the audio signal
Natural language processing and
machine learning algorithms extract
semantic meta-data from content (i.e. people, places,
companies, facts, languages)
Linguistic analysis to detect three types of
tones from text: emotion, social tendencies, and language style
The streaming solution can invoke existing services or external services to extract
relevant information
Provides domain-specific translation in multiple languages, utilizing Statistical
Machine Translation techniques that have been perfected in our
research labs
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Case Study: Recent eComm success at a Tier 1 Bank
Phase 1 Phase 2 Phase 3
Replace the incumbent regulatory compliant archiving
infrastructure
Enhance supervision and
surveillance
Support the bank’s holistic
surveillance strategy
Challenge
IBM Solution:• IBM Strategy Formulation &
Business Case• Partnership with Actiance
Alcatraz (Regulatory Compliant Archiving & Supervision)
• IBM Surveillance Insight (Holistic Surveillance and Advanced Feature Extraction)
• SaaS on IBM SoftLayer Infrastructure
A Tier 1 Bank was looking to modernize its communications archive and adopt an open and flexible architecture.It needed a solution to consolidate and manage the supervision and surveillance of all communications
and transactions and improve the firm’s market abuse detection capabilities
1414
Closing Thoughts - Transforming Surveillance using Cognitive
• Separating meaningful data from noise and the sub-optimized classification of data to reduce false positives.
• Easily navigate through large volumes of communications: Trades/Transactions, Electronic Communications, Voice, Activity using advanced graph analytics and machine learning.
• Sophisticated behavioural analysis to build employee conduct risk profiles in order to identify outlining risks missed by traditional rules bases solutions.
• Unique Voice Surveillance capabilities, delivering highest Word Accuracy Rate output.
• Holistic Surveillance platform for a future proof solutions for the ever changing complexities of Conduct Risk.
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