hendra suryanto - achieving enterprise level value from machine based learning - futuredata 2017

Achieving enterprise level value from machine based learningHendra SuryantoChief Data ScientistRich Data Corporation

Presented in

Data | Innovation | Disruption

• Mobile Lending Platform utilizing our Credit Decision Engine that leverages social media and other data sources

• Management and board with extensive global financial services experience.

• Enrich traditional consumer data with Social Media data 

• Turn data into value through innovative business models

• Focusing on consumer profiling, behaviour data and predictive analytics   

• Taking technology incubated in Australia to Asia

• RDC platforms deployed in Australia, Singapore, Canada and Vietnam: 6 successful implementations in last 18 months.

• Signed agreements with large enterprises in China, Vietnam, and US.

Market MomentumAI & Social Media Enabled Fintech

RICH

  DATA  CORP

ORA

TION

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Machine Learning – Case Studies

• Marketing: next best offers • Fraud: credit card fraud • Customer journey analytics: churns • Risk: credit scoring

Various feedback lags – various learning speed

Machine Learning – Case Studies

• Marketing: next best offers (1 minute – 1 month)• Fraud: credit card fraud (1 hour – 24 hours)• Customer journey analytics: churns (1 month – 6 month)• Risk: credit scoring (2 months – 12 months)

Various feedback lags – various learning speed

Case Study: credit risk on unsecured lending

A lender in Canada would like to automate their credit decisioning

Machine Learning

Human decision Machine decision

Case Study: credit risk on unsecured lending

Prototype     Productionise     Operationalise

Case Study: credit risk on unsecured lending

Prototype     Productionise     Operationalise

• Data scientist• One‐off /tactical

• data extract• feature 

engineering• modelling• scoring & 

decisioning

• Enterprise Architect• Automation

• data integration

• data & feature quality monitoring

• Business Team (Risk)• Process & people

• training• governance• scoring & 

decisioning update

4 weeks     8 weeks     12 weeks

From raw data to business value ($$$)

• What is it?• Why we need it?• How do we delivering it?

Even

t tab

le: Entity

 Attrib

ute Va

lue 

Timestamp (EAV

T)

Flat Table  (Entities x Features Cross Table)

Descriptive Analytics

Predictive Analytics

Prescriptive Analytics

Raw/Binary DataFeature Engineering

Analytics

Business Outcomes (in Dollars)

From raw data to business value ($$$)

• What is it?• Why we need it?• How do we delivering it?

Learning from event based data (raw data)

data collection time

Loan defaulter

Good customer

Entity: Customer

Attribute‐ValueCall Reason=Complaint

Timestamp

Timestamp is important to ensure we use historical data to predict future outcomes 

Feature engineering – the missing link

• Feature selection• Feature extraction (e.g. NLP, 

image processing)• Feature construction (e.g. 

business logic, formulae)

Feature engineering – can Deep Learning help?

Domain Expert 

Building Scoring Model

Building Decision Engine

Continuously learning from users and new data.  Any feedback will be used for future learning.

Hierarchy of Features

Machine Learning

Domain Expert

Users

UsersA

B

New data

Continuous Learning

14

19,117 Applications were processed. $10 million loan issued. 10% increase in average loan size. Bad debt rate is 5% for the under banked segment.

Gini 0.53

Gini results from all training and test data.

Gini 0.52

Gini results after excluding Ageand Work Type to comply with Canadian Regulation

Gini 0.40

Gini in production is lower than testing due to data quality issues.

After retraining, the Gini is raised to our expected threshold. It improved also due to new features.

Gini 0.55

Learning in Real LifeCase Study: Canada

Machine Learning – Achieving Enterprise Value

• Expand market share (customer acquisition)

• Increase profit per customer• Reduce cost

Machine Learning

• Prototype• Productionise ‐ automation• Operationalise ‐ continuous 

learning

Singapore

One Raffles Place #34‐04 Tower 1Singapore 048616

Australia

802, 8 West Street North SydneyNSW 2060 

E info@richdataco.com

F facebook.com/richdataco

T @richdataco

W www.richdataco.com

China

Room 509, 5th Floor, Building 2Xunmei Technology PlazaYeuhai Street, Nanshan DistrictShenzhen, China