Programme for Machine Learning

Programme

Machine Learning in Finance

23-24 April, Kuala Lumpur

Day 1

09:00

Registration

09:30

Machine learning models

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Deep learning
  • Advanced machine learning models

Simon Goo, Executive Director Head, Risk Analytics, UOB

11:00

Morning break

11:30

Machine learning in banking, risk management & modelling – Part I

  • ML applications in banking and risk management
  • Analyze large amounts of data while maintaining granularity of analysis
  • Tools to optimize and accelerate model risk management
  • Reporting requirements within financial services
  • Pre-trade risk controls and best execution analysis
  • Data privacy, security and governance laws in KL

13:00

Lunch

14:00

Machine learning in banking, risk management & modelling – Part II

  • Machine learning: Type of tasks
  • Challenges and opportunities in credit risk modelling
  • List of common and advance ML tools
  • Clustering methods for estimation of equity correlation matrices
  • Neural networks for modelling bank failures Using predictive analytics

15:30

Afternoon break

16:00

Understanding and developing an effective big data strategy

  • Extracting value from limited information
  • Three Key Components of Big Data Analytics
  • Big Data Management: Infrastructure and Technology
  • Data Availability, Accessibility and Integrity
  • Batch and Stream Processing
  • Advanced Analytics: Insights Generation and Algorithms Development 
  • Modern Data Analysis: Structured/Unstructured Data Modelling
  • Insights Consumption: Application and Visualisation
  • Case Studies: Use Cases in the Financial Services Sector

Johnson Poh, Head Data Analytics, Group Big Data and AI Technology, DBS

17:30

End of Day 1

Day 2

09:00

Registration

09:30

Machine Learning in financial trading: Theory and applications

  • Understanding data structures used for algorithmic trading
  • Benefits of AI on trading
  • Determining the dynamic trading strategy that optimizes expected utility of final wealth is a hard problem
  • Data usage across a typical trading business
  • What a data analytics platform should look like for trading
  • Research in portfolio transitions
  • Case studies

Eric Tham, Senior Lecturer & Consultant of Analytics & AI, NATIONAL UNIVERSITY OF SINGAPORE

11:00

Morning break

11:30

Unstructured data in wealth and asset management

  • Understanding "Sentiment Analysis" for people and markets
  • Building a digital wealth proposition that integrates all of these techniques
  • Constructing automated portfolios through robo-advisors or advisor dashboards.  
  • The future is in the client centric development of AI, what direction to pursue?  
  • Build or buy? 

Radha Pendyala, Enterprise Data Scientist, THOMSON REUTERS      

13:00

Lunch

14:00

Conversational AI – Beyond the chatbot hype

  • Current state of the AI industry applied to digital assistants/chatbots
  • Source, real research and development behind it
  • Challenges
    • Data
    • Scalability and production issues
  • Practical possibilities ahead for organisations  - latest trend: eKYC
15:30

Afternoon break

16:00

Machine Learning, Financial Industry Affairs & Booking Structures

  • Global financial industry regulatory & compliance landscape
  • Regulatory affairs & meaning derivation and validation
  • Booking dynamics driving trade profitability
  • Machine learning through smart routing & aggregation
  • Fluid machine-driven booking actions
17:30

End of Day 2