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

Publicity and Potential Impact

report:

Huge Potential of Impact:

…

DBS-I2R Joint Lab Collaboration

Made in Singapore

Going Global

The Future of Auditing is Auditing the Future

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

9

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

SingTel-I2R Joint Lab Collaboration

14

Deep Consumer Insights, Life-Style, Attitudes and Behaviours

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

15

Train Depot

16

Finding Commuter Behaviour from CDR Data

17

Finding Commuter Behaviour from CDR Data

18

Predicting the Next Place from CDR Data

All combinations of Loc-ToD-DoW ranked by #supporting instances in the history for the user

19

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.

What’s Next ?

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 Krishanswamyspkrishna@i2r.a-star.edu.sg