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08:30: Registration and Morning Welcome Coffee

Chair:Paul Bilokon, PhD

Paul Bilokon: 

Founder, CEO,Thalesians

 Senior Quantitative Consultant, BNP Paribas  

09.00 - 09.45:  Keynote SpeechAbdel Lantere

Presenter: Abdel Lantere: Data Scientist, Quantitative Consultant, HSBC 

"Black-box Machine Learning: Improving Transparency".

"Many of the state of the art machine learning applications are based on black-box models which are difficult to interpret and explain. With more ML-based models being integrated into live decision-making systems, new challenges will be faced by various functions within banks as well as by the regulators. This talk disucsses the challenges faced and presents techniques to help provide more transparency and better understanding of the results of a given ML black-box model." 

09.45 – 10.30Using Machine Learning Methods for Volatility TradingArtur Sepp

• Statistical models for realized volatility estimation and forecast
• Model selection using machine learning
• Supervised machine learning and learning to rank
• Applications for volatility trading and asset allocation 

Presenter: Artur Sepp, Director, Senior Quantitative Strategist, Julius Baer

10.30 – 11.00: Morning Break and Networking Opportunities

11.00 - 11.45: Machine Learning Models Dr. Miquel Noguer Alonso

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

Presenter: Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University 

11.45 – 12.30: Presenter & Topic to be confirmed

12.30- 13.30: Lunch

13.30 – 14.15: "Machine Learning for Financial Systems: Where it can be Competitive" Tomaso Aste

  • Machine leraning ad artificial intelligent have been developed for domains that are very different to finance
  • Financial data are noisy and training sets are very scarse
  • Socio-economic systems continuously evolve and never repeat identical patterns
  • New tools must be developed to operate with machine learning in these systems

Presenter: Tomaso Aste: Professor of Complexity Science, UCL Computer Science 

14.15 – 15.00: Fast MVA Optimisation using Chebyshev Interpolants 

  • MoCaX Smart grids based on Chebyshev spectral decomposition
  • Machine Learning accelerated with MoCaX fast pricing
  • Application: MVA optimisation in real time. With the massive acceleration to compute Greeks with MoCaX, it is possible to evaluate a Monte Carlo simulation of SIMM in fractions of seconds. This in turn makes it possible to revalue an MVA objective function as frequently as required by the optimisation algorithms.

Presenter: Mariano Zeron: Head of R&D, MoCaX IntelligenceAndrés HernándezManager, Financial Services Risk Consulting, PwC

15.00 - 15.30: Afternoon Break and Networking Opportunities

15.30 - 16.30: Unsupervised Anomaly Detection in Finance  

Claudi Ruiz Camps

'ABN AMRO Clearing Bank works with considerably large amounts of data every day and we design and implement Deep Learning models to approach  some of our business cases. One example is, how to find real time anomalies (strange behaviors) in our data by using Unsupervised Anomaly Detection with TensorFlow and Spark. The output is being visualized with Tableau in order to express the anomalies and to make data-driven business decisions.'   

Presenter: Claudi Ruiz Camps: Machine Learning, Deep Learning Specialist, & Marleen Meier: Quantitative Risk Analyst, Data Visualization, ABN AMRO Clearing Bank N.V.  

16.30 – 17.15: Learning the Optimal Risk

Marco BianchettiMarco Scaringi

  • Portfolio optimization from a risk management point of view
  • Eligible risk optimization strategies
  • Optimization metaheuristics and machine learning
  • Test cases
  • Mathematical precision vs effective risk hedging

Presenters: Marco Bianchetti: Financial and Market Risk Management, Head of Fair Value Policy & Marco Scaringi: Financial and Market Risk Management, Quant Risk Analyst, Intesa Sanpaolo

17.15 – 18.00: Machine Learning & Ai in Quantitative Finance Panel


  • Paul Bilokon: Founder, CEO,Thalesians, Senior Quantitative Consultant, BNP Paribas & Visiting Lecturer, Imperial College


  • Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University
  • Artur Sepp, Director, Senior Quantitative Strategist, Julius Baer 
  • Abdel Lantere: Data Scientist, Quantitative Consultant,HSBC
  • Ignacio Ruiz: Founder & CEO, MoCaX Intelligence
  • Claudi Ruiz Camps: Machine Learning, Deep Learning Specialist, ABN AMRO Clearing Bank N.V.


  • What is the current state of utilisation of machine learning in finance?
  • What are the distinct features of machine learning problems in finance compared to other industries?
  • What are the best practices to overcome these difficulties?
  • What's the evolution of a team using machine learning in terms of day to day operations?
  • What is a typical front office 'Quant' skillset going to look like in three to five years time?
  • How do we deal with model risk in machine learning case?
  • How is machine learning expected to be regulated?
  • What applications can you list among its successes?
  • How much value is it adding over and above the “classical” techniques such as linear regression, convex optimisation, etc.?
  • Do you see high-performance computing (HPC) as a major enabler of machine learning?
  • What advances in HPC have caused the most progress?
  • What do you see as the most important machine learning techniques for the future?
  • What are the main pitfalls of using Machine Learning currently in trading strategies?
  • What new insights can Machine Learning offer into the analysis of financial time series?
  • Discuss the potential of Deep Learning in algorithmic trading?
  • Do you think machine learning and HPC will transform finance 5-10 years from now?
  • If so, how do you envisage this transformation?
  • Can you anticipate any pitfalls that we should watch out for