Programme

Training Workshop Programme

Programme

Day 1

Machine learning in risk management  

08:30

Registration

09:30

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

Johnson Poh, Head Data Science for Big Data Analytics Center of Excellence, DBS BANK

11:00

Morning break

11:30

Machine learning models

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

Aaron Hallmark, Chief Executive Officer, CATENA TECHNOLOGIES

13:00

Lunch

14:00

Risk Management via Alternate Data Sources

  • Identifying entities, relations and other metadata from unstructured data
  • Hands on python exercises for tagging unstructured data
  • Understanding graph data - rdf
    • Converting relational data to graph data
  • Tagging news items and getting real time sentiment
    • Use case demo and discussion
  • Impact of news on portfolio of securities
  • Supply chain risk management
    • Use case demo and discussion

Radha Pendyala, Enterprise Data Scientist, THOMSON REUTERS

15:30

Afternoon coffee break

16:00

Machine learning in banking, risk management & modelling

  • ML applications in banking and risk management
  • Analyse 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

Aditya Mehta, Innovation Analyst at Applied Innovation and Strategic Investments, HSBC

17:00

Q&A session

A panel of expert speakers will summarise key takeaways form their presentations and will open up for the Q&A and final thoughts on the industry’s adoption of ML tools.

Aaron Hallmark, Chief Executive Officer, CATENA TECHNOLOGIES
Aditya Mehta, Innovation Analyst at Applied Innovation and Strategic Investments, HSBC

17:30

End of Day 1

Day 2

Machine learning in trading and investment strategies

08:30

Registration 

09:00

Trading Strategies based on news and sentiment data

  • Machine readable news - format and metadata description
  • Analyzing 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, THOMSON REUTERS

10:30

Morning break

11:00

Machine Learning and Portfolio Construction in Fixed Income

  • Current status of the use of machine learning algorithms in FI portfolios
    • how ML is used in the equity world
    • the challenges of using ML in FI
  • Current evolution of ML algos

Adrian Gostick, Chief Revenue Officer, BondIT
Hillel Raz, Chief Scientist, BondIT

12:00

Lunch

13:00

Machine Learning and Portfolio Construction in Fixed Income – Part II

  • Building fixed income portfolios:
    • Problem definition – what is the goal, what are the constraints
    • Optimisation techniques
    • Use of machine learning algorithms:
      • data – predict, clean, fix
      • dimension reduction
      • attribute and behaviour prediction (i.e. default)
      • allocation of assets in portfolio
        • covariance prediction
        • index construction
  • Live example of building a portfolio (e.g. to replicate an ETF)

Adrian Gostick, Chief Revenue Officer, BondIT
Hillel Raz, Chief Scientist, BondIT  

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

Raul Abreu, Head of Conversational AI (Banking) Innovation, Artificial Intelligence Experimental Lead RBWM, HSBC

15:00

Afternoon break

15:30

Q&A session

A panel of expert speakers will summarise key takeaways from their presentations and will open up for the Q&A and final thoughts on the industry’s adoption of ML tools.

Adrian Gostick, Chief Revenue Officer, BondIT
Hillel Raz, Chief Scientist, BondIT 
Radha Pendyala, Enterprise Data Scientist, THOMSON REUTERS

16:30

End of Day 2