Artificial Intelligence is deemed to be the main driver of the 4th Industrial Revolution. IDC predicts that investment in AI will grow from $12bn in 2017 to $57.6 by 2021, while Deloitte Global predicts the number of machine learning pilots and implementations will double in 2018 compared to 2017. As a result, companies from every industry have been spurred on to seize the trend and innovate – from virtual assistants to cyber security to fraud detection and much more. The majority of C-level executives have identified and agree that AI will have an impact on their industry. However, only 20% of C-level executives admit they have already adopted AI technology in their businesses, according to research conducted by McKinsey. So, there is plenty of scope for change and improvement. The Finance industry is anticipated to lead the way in adoption of AI with a significant projected increase in spending over the next three years.

Until recently, practitioners have faithfully relied upon neo-classical models to measure performance, whether it’s in financial organisations or marketing corporations. AI is the new technology that offers an automated solution to these processes. It has the capability to replicate cognitive decisions made by humans and also remove behavioural bias adherent to humans.

Machine learning and sentiment analysis are specific techniques that are applied in AI. These techniques are maturing and rapidly changing the landscape of FINTECH. In order to process and understand the masses of data out there, machine learning and sentiment analysis have become essential methods that open the gateway to data analytics. To keep up with the ever-expanding datasets, it is only natural that the techniques and methods with which to analyse them must also improve and update.

This conference will help you to demystify the buzz around AI and differentiate the reality from the hype. Learn about how you can benefit from the unprecedented progress in AI technologies. Participants will be presented with real insights on how they can exploit these technological advances for themselves and their companies.

Topics Covered Include:

  • Fundamentals and applications of machine learning and deep learning
  • Pattern classifiers, Natural Language Processing (NLP) and AI applied to data, text, and multi-media
  • Sentiment scores combined with neo-classical models of finance
  • Financial analytics underpinned by qualitative and quantitative methods
  • Predictive and normative analysis applied to finance
  • Behavioural and cognitive science
  • The future of AI and its impact on industries

Why participate?

  • Hear from leading subject experts from UK, US, Europe and India/Hong Kong
  • Programme includes the latest state-of-the-art research, practical applications and case studies
  • Expect technical and in-depth presentations and discussions; we bring to you the latest in the global FINTECH scene and stimulate your brain cells!
  • Excellent networking opportunities throughout the days with all participants, including presenters, investors and exhibitors.

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Call for Participation

We are inviting speakers – thought leaders, subject experts and start up entrepreneurs – to share their knowledge and enthusiasm about their work and their vision in the field of AI, Machine Learning, Sentiment Analysis and Deep Learning.

We understand that successful projects are written up as “White Papers”. Please share these with us. But projects that did not achieve their targets – “Black Papers” – are of interest to us too. They can be very important topics of discussion / panels that you can present. Talk to us about both, we welcome your input.

Please complete the speaker’s response form and submit a proposal to present at this event.

Previous Programme

  • 08:00 -

    Registration and Coffee

  • 08:45 -

    Introduction and Welcome - Professor Gautam Mitra, OptiRisk Systems/UCL (Programme Chair)

  • Session Chairperson: Edward Fishwick, Managing Director and Global Co-Head of Risk & Quantitative
    Analysis at BlackRock -


  • 09:00 -

    How I survived the AI winter (& plan to survive the next one)

  • 09:30 -

    News Sentiment Everywhere!

  • 10:15 -

    Hierarchical Natural Language Representation Using Deep Learning

  • 10:45 -

    Introduction to Sponsors

  • 10:50 -


  • 11:15 -

    Enhanced Trading Strategy using Sentiment and Technical Indicators

  • 11:45 -

    Blowing Bubbles: Quantifying How News, Social Media and Contagion Effects Drive Speculative Manias

  • 12:15 -

    Social Trading – Developing Signals from Social Sentiment

  • 12:45 -



  • 13:45 -

    Big is beautiful: How data from email receipts can help predict company sales

  • 14:15 -

    Bringing Data to Life at the Bank of England

  • 14:45 -

    Panel Session: Alternative Data

  • 15:30 -


  • 16:00 -

    The Application of AI to Quantitative Systematic Strategies, Opportunities and Risks

  • 16:30 -

    Including News Data in Forecasting the Macroeconomic Performance

  • 17:00 -

    Asset Classification Based on Machine Learning Techniques

  • 17:30 -

    Drinks Reception and Networking

  • Session Chairperson (morning): Professor Gautam Mitra, OptiRisk Systems/UCL -

  • 08:55 -

    Welcome and Introduction to Day 2 - Professor Gautam Mitra, OptiRisk Systems/UCL

  • 09:00 -

    AI-Machine Learning and Deep Learning in FinTech

  • 09:30 -

    Enhanced prediction of sovereign bond spreads through Macroeconomic News Sentiment

  • 10:00 -

    Mining News Topic Codes With Sentiment

  • 10:30 -


  • 11:00 -

    Finding Alpha Signals with Artificial Intelligence + Influencer Analysis + Big Data

  • 11:30 -

    How to measure intangible assets - the missing factor for value investing

  • 12:00 -

    The State of The Art in New Sentiment Visualization

  • 12:30 -



  • 13:30 -

    Panel Session: Does AI Beat Classical Models?

  • Session Chairperson (afternoon): Dr Ronald Hochreiter, Vienna University of Economics and Business -

  • 14:15 -

    How AI Can Predict Crypto Assets by Using Sentiment

  • 14:45 -

    Contemporary Deep Learning Methods for Building Investment Models Based on Graphical Time-series Representations

  • 15:15 -


  • 15:45 -

    Rapid Conditioning of Risk Estimates Using Quantified News Flows

  • 16:15 -

    Going Native with Japanese News Analysis

  • 16:45 -

    Machine Learning for Hedge Fund Selection

  • 17:15 -

    Close of Conference

Previous Speakers

Anders Bally


Rajib Borah


Humberto Brandão

Federal University of Alfenas

Matteo Campellone



Douglas Castilho

University of São Paolo

Nishant Chandra

AIG Science

Francesco Cricchio


Pierce Crosby



Sanjiv Das

Santa Clara University, USA

Ivailo Dimov


Christina Erlwein-Sayer

OptiRisk Systems

Edward Fishwick



Joao Gama

University of Porto

Peter Hafez


Ronald Hochreiter

WU Vienna University of Economics and Business & Academy of Data Science in Finance

Claus Huber

Rodex Risk Advisers


Dan Joldzic

Alexandria Technology

Christopher Kantos


Jakub Kolodziej


James Luke



Asger Lunde

Aarhus University

Gautam Mitra

OptiRisk & UCL

Jordan Mizrahi


Lyndsey Pereira-Brereton

Bank of England


Richard Peterson

MarketPsych Data

Guillaume Vidal

CEO, Walnut Algorithms

Xiang Yu


Andreas Zagos

Intracom GmbH


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Price per day

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Price per day

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  • Standard Price - 15000+GST/per day
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