Programme for Machine Learning
Programme
Machine Learning in Finance
23-24 April, Kuala Lumpur
Day 1
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09:00
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Registration
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09:30
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Machine learning models
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Deep learning
- Advanced machine learning models
Simon Goo, Executive Director Head, Risk Analytics, UOB
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11:00
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Morning break
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11:30
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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
Luke Waddington, CEO & CO-Founder, BLUEFIREAI
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13:00
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Lunch
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14:00
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Machine learning in banking, risk management & modelling – Part II
- Exposure of Current Weakness: Why am I missing big corporate stress events, why aren’t the current processes standing up to rigor?
- Buying New World Technology & Skills: Incumbent industry doesn’t yet have the technology or the skills (in depth) to execute on the “vision”.
- Adoption of “new” machine intelligence: why is always our first reaction to reject it or ask to see all the inputs?
- Invention - Small Data Problem: Especially in Asian markets we have a small data problem, how do you understand the truth with small data?
- Real AI - Intelligence Representations: The movement from description, to prescription to prediction the “difficult” super trend.
Luke Waddington, CEO & CO-Founder, BLUEFIREAI
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15:30
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Afternoon break
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16:00
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Trading Strategies based on News and Sentiment Data
- Machine Readable News - Format and Metadata description
- Analysing News Data
- Understand Real-time Sentiment Data
- Using Kalman Filter on Sentiment Data and Identifying Sentiment Regimes
- Use case Demo and Discussion
- Trading Strategies based on Sentiment Data
- Understanding Multidimensional Real time Sentiment Data
- Cross rotation strategy formulation and back testing
- Use case Demo and Discussion
- Deep Learning based trading strategy
- Use case Demo and Discussion
Radha Pendyala, Enterprise Data Scientist, REFINITIV
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17:30
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End of Day 1
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Day 2
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09:00
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Registration
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09:30
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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
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11:00
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Morning break
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| 11:30 |
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
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| 13:00 |
Lunch
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| 14:00 |
Introduction on AI drivers and applications (Python)
- Machine Learning in Finance
- Customer segmentation and visualization
- Development of predictive model for a continuous time series data set
- ATM cash demand prediction model
- Boosting your performance with Ensemble Learning including Random Forest and Gradient Boost
Mohammad Yousuf Hussain, Data Scientist, JASMINE22
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| 15:30 |
Afternoon break
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| 16:00 |
Natural Language Processing (NLP) in banking
- Really "know your clients" in their own words
- Chatbots as a means of starting a client focused dialogue
- Using "Personality Insights" so that your advisors never make a "cold call" again
- 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?
Mohammad Yousuf Hussain, Data Scientist, JASMINE22
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| 17:30 |
End of Day 2
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