8:50 AM - 9:00 AM Chairman Opening Remarks

8:00 AM - 8:55 AM Morning Refreshment and Registration

ACCELERATING MACHINE LEARNING IN AN ORGANIZATION

9:00 AM - 9:40 AM Bringing Machine Learning to every corner of your business: Partnering business stakeholders in becoming a data-centric company

Johnson Poh, Head of Data Science/Practice Leader for Big Data Analytics, DBS Bank
· Breaking down the principles and science of Machine Learning to get a clear overview of what ML is and what it is not
· Demonstrating practical use-cases of ML within DBS
· Creating an open and collaborative culture across the organization that embraces the value of machine learning
· Understanding business challenges and requirements to drive innovative data-driven decisions

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Johnson Poh

Head of Data Science/Practice Leader for Big Data Analytics
DBS Bank

9:40 AM - 10:10 AM [Case Study] Getting the internal buy-in for machine learning deployment to enhance business outcomes

Pedro Uria-Recio, Vice President, Head of Analytics Centre , Axiata Group Berhad
· Changing the organizational mindset from the top-down
· Making a clear business case for machine learning
· Demonstrating measurable value of machine learning ROI beyond numbers
· Championing the use of machine learning as a way to strengthen current systems and speed up business decisions

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Pedro Uria-Recio

Vice President, Head of Analytics Centre
Axiata Group Berhad

10:10 AM - 10:20 AM Speed networking

10:20 AM - 10:50 AM Morning Tea and Networking Break

DEMONSTRATING QUANTIFIABLE RESULTS

10:50 AM - 11:30 AM Allianz: Detecting abnormal motor claims using Machine Learning

Raymond Au, Head, Allianz Asia Lab (Data Science, Innovation), Allianz
· Evaluating traditional methods for reducing motor claims leakages
· Mimicking human knowledge using Machine Learning to build a workable model for abnormal claims detection
· Improving adoption of machine learning models through integration with existing processes and systems
· Reviewing lessons learnt from PoC to Production

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Raymond Au

Head, Allianz Asia Lab (Data Science, Innovation)
Allianz

11:30 AM - NaN:NaN AM [Case Study] Improving customer conversion and rebooking rates on Agoda

Uri Weiss, Head of Data Science , Agoda.com
In this presentation, Uri will be sharing how Agoda has leveraged machine learning to enhance their customer rebooks with higher booking values on their online platform through personalization of hotel searches. Swamped with a whole suite of choices, how can Agoda personalize and make search fast, easy and seamless for customers?

The team’s first approach was to directly optimize revenue by ranking up the top producing hotels. However, this was clearly not user-centric and was far from Agoda’s vision for the ranking product: To be a discovery platform for hotels.
Uri will be sharing his experience on managing expectations of stakeholders on the use of machine learning, the project journey towards maturity, the conflict between customer experience optimization and revenue optimization, and how the current model has yielded results.

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Uri Weiss

Head of Data Science
Agoda.com

12:20 PM - 1:20 PM Networking Lunch Break


1:20 PM - NaN:NaN AM [Case Study] Improving lead generation for sales and marketing teams with Manulife’s machine learning applications

Pocholo Capule, Director and Head of Advanced Analytics, Manulife
· Evaluating customers personal preference and making sound recommendations
· Partnering business functions to understand their needs and agree on the right set of data to analyze
· Getting innovative with methodologies in evaluating data set with limited variables

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Pocholo Capule

Director and Head of Advanced Analytics
Manulife

OVERCOMING MACHINE LEARNING DEPLOYMENT CHALLENGES

2:00 PM - 2:40 PM How to productionize machine learning

Roy Goh, Director, Analytics, Service Delivery and Management, Merck, Sharp & Dohme
· Making use of Machine Learning to turn legacy data into actionable insights for business growth
· Partnering across functions to identify requirements for machine learning business applications
· Evaluating end-to-end process from building predictive model to production with business functions
· Working closely with IT to provide up to date information systems to support operations

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Roy Goh

Director, Analytics, Service Delivery and Management
Merck, Sharp & Dohme
In this interactive discussion, delegates will engage in roundtable discussions comprising 8-10 members per table for an in-depth dialogue into four issues to overcome data challenges when machine learning is applied.
 
Each discussion group will be led by an expert and will take place concurrently, with the tables rotating every 40 minutes. Delegates will have the opportunity to engage in 2 out of the 3 discussion groups over the course of the session.

Interactive Group Discussions

2:40 PM - 4:00 PM Roundtable A: How to identify and prioritize business problems that require machine learning

Roy Goh, Director, Analytics, Service Delivery and Management, Merck, Sharp & Dohme
  • Specifying business requirements and problems to solve
  • Assessing the complexity of problem that warrants machine learning
  • Acquiring domain knowledge to align solutions to the problem
  • Identifying smalls, quick wins

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Roy Goh

Director, Analytics, Service Delivery and Management
Merck, Sharp & Dohme

2:40 PM - 4:00 PM Roundtable B: How to consolidate all data to a single platform for easy accessibility and availability

Sigrid Rouam, Lead Data Scientist, Singapore Exchange Limited
  • Establishing data quality metrics
  • Evaluating the available sources of data storage
  • Processing sensitive data
  • Building a secure data governance of platform
  • Managing dynamic sources of data

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Sigrid Rouam

Lead Data Scientist
Singapore Exchange Limited

2:40 PM - 4:00 PM Roundtable C: How to attract, nurture and upskill workforce for successful machine learning deployment

Raymond Au, Head, Allianz Asia Lab (Data Science, Innovation), Allianz
  • Identifying areas for machine learning deployment and subsequently re-allocate talent for value-added work
  • Assessing the impact of machine learning on the future workforce
  • Taking a proactive approach to talent acquisition, retention and retraining

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Raymond Au

Head, Allianz Asia Lab (Data Science, Innovation)
Allianz

2:40 PM - 4:00 PM Roundtable D: How to translate trends from machine learning algorithms to actionable business intelligence

  • What data can you track and what data should you track?
  • Taking the best insights from your data and using it effectively
  • Understanding user experience of products and the pain
  • Testing end product from a user perspective


4:00 PM - 4:40 PM Afternoon Tea and Networking Break

4:40 PM - 5:20 PM Overcoming talent and recruitment challenges to kick start your machine learning journey

Tay Eu Jin, Head of Decisions Management, Ambank Malaysia Berhad
  • Defining job functions and scope when recruiting talent
  • Specifying critical business requirements and problems to solve
  • Evaluating hiring criteria: experience vs skillsets – how to work around the lack of best of both worlds?

Tay Eu Jin

Head of Decisions Management
Ambank Malaysia Berhad

5:20 PM - 6:00 PM Building a framework to integrate data, technology and processes across business units to embrace machine learning transformation

Nanthekumar Tamilselvan, Customer Loyalty Specialist,
  • Evaluating the common gaps overlooked when building the business case for ML
  • Assessing the machine learning needs to the organization and how this fits with the existing structure
  • Integrating new technology in stages by business priorities and ease of transformation

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Nanthekumar Tamilselvan

Customer Loyalty Specialist

6:00 PM - 6:05 PM Chairman Closing Remarks and End of Day One