shonali krishnaswamy, head, institute for infocomm research presentation at the chief data and...
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The Power of Partnerships: From Innovation to Impact
Shonali KrishnaswamyInstitute for Infocomm ResearchAgency for Science, Technology
and Research (A*STAR)
Awards & Benchmarking• IES Prestigious Engineering Award 2016 & 2015
– DBS-I2R Predictive Audit
• First Place @ IJCAI Competition Stage 1-2015 – over 750 Data Scientists Participated– IJCAI is Tier 1 / Top AI Conference
• Part of First Place Winning Team @ KDD Cup 2015 – KDD is Tier 1 / Top Data Mining Conference– 850 Participating Teams
• Best Paper @ DASFAA 2015 – Database Systems for Advanced Applications Conference
• Best Runner-Up Paper @ ACML 2015 – Asian Machine Learning Conference
Awards & Benchmarking• First Place – Beating 180 Teams - in GE Flight Quest Challenge – 2013• Third Place for EC2BargainHunter in IEEE Cloud Cup – 2013• First Place in PAKDD Churn Prediction - 2012• First Place in Fraud Detection in Mobile Advertising - 2012 • First Place in Mobile Activity Recognition Challenge – 2011• Third Place in Time-Series Forecasting Competition - 2012• Third Place in IEEE Services Cup - 2012• Fifth Place in IEEE Intl. Conf. on Data Mining (ICDM) Contest - 2012• Top 5 Innovative Ideas in the Urban Prototyping Challenge @ World
Cities Summit - 2012• Second Place in NIST Entity Linking Competition - 2011
Joint Labs and Multi-Project Collaborations For Big Data Research & Innovation
Data Analytics
Completed
Data Analytics for Risk Prediction – Branch, Trading Floor & Trade FraudData Analytics for Risk Prediction – Branch, Trading Floor & Trade Fraud
Branch Risk Detect irregularities and identify key risk drivers in branches.
1Branch
Trade Fraud Detect potential trade fraud activities
Trading ActivitiesIdentify and preempt irregular activities
2 Trading
3Trade Fraud
Toshihide IguchiDaiwa Bank1984 - $ 1.1bnBruno IksilJP Morgan 1984 - $ 1.1bnNick LeesonBarings Bank 1995 - £827m
Yasuo HamanakaSumitomo Corp1997 - $ 2.6bnJerome KievelSocété Générale2008 – £3.7bnKweku AdoboliUBS2011 – $1.1bn
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Can They Be Stopped?
Rogue TradersRogue Traders
Validate andLive Testing
• Analyze Heterogeneous Data• Compute Score based on Risk
Indicators• Detect Hybrid Irregularities (Prior
Knowledge + Anomaly Detection)• Visualize Results
Data
Transaction DataTransaction DataLimit Utilization
DataLimit Utilization
DataProfit/Loss DataProfit/Loss Data
Chat-log DataChat-log Data
External News External News
Early Detection of Trading Irregularities Engine
Heterogeneous Data Integration
Data Visualization
Data driven and learning risk surveillance model analyzing heterogeneous data to detect and predict risk events as they evolve, enabling continuous surveillance and early intervention
Early Detection and Prevention of Trading IrregularitiesEarly Detection and Prevention of Trading Irregularities
Multi-Modal& Heterogeneous Data
Institution of Engineers Singapore (IES) Prestigious Engineering Achievement Award 2015
Predictive Audit for Branch IrregularitiesPredictive Audit for Branch Irregularities
Data + Historical Risk Events
Institution of Engineers Singapore (IES) Prestigious Engineering Achievement Award 2015
Data Analytics for Predictive AuditData Analytics for Predictive Audit
IMPACT PRODUCTIVE
Improved productivity & efficiency –valuable audit resources could target higher risk organizational units
PROACTIVE Continuous surveillance on risks
PREDICTIVE & PREVENTIVEEarly detection enables risk mitigation
GREATER ASSURANCEin the adequacy & effectiveness ofinternal controls
CUSTOMER CONFIDENCEin the integrity of the financial industry
Top 5 Stay Regions of A User
38%
16%
8%
6%5%
Frequency
Stay regions of a user areextracted using a temporaland a spatial thresholds.
Finding Stay Regions from CDR Data
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Predicting the Next Place from CDR Data
All combinations of Loc-ToD-DoW ranked by #supporting instances in the history for the user
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Predicting Behavioural Groups from CDR DataA model of behavioral groups:
Behavioral groups have similar edge features .Nodes have few behavioral groups.
Distributed implementation with data parallel strategy.
1pm @ (15,9)4pm @ (14,10)
10am @ (15,10)
10pm @ (20,30)
8am @ (19,29) 7pm @ (21,30)8pm @ (5,40)9pm @ (5,39) 7pm @ (6,41)
Behavioral groups have similar edge
features
1pm @ (15,9)4pm @ (14,10)
10am @ (15,10)
10pm @ (20,30)
8am @ (19,29) 7pm @ (21,30)8pm @ (5,40)9pm @ (5,39) 7pm @ (6,41)
Edges with similar features go into the same
group
Hangout places for Group “Social Weekender” in Saturday evening.
Local neighborhood of a random sub- scriber, tagged with behavioral groups.
Outside-In Data + In-House Data
I2R Confidential
Unlocking the Potential of Data Partnerships Unlocking the Potential of Data Partnerships
100s of Features Extracted
Key Features for Predicting are
Selected
Evaluate Multiple Learning Models
Artificial Intelligence Outperforms Human Data ScientistsBy Jeremy HsuPosted 20 Oct 2015 | 16:00 GMT
For more information, please contact:Shonali [email protected]