Artificial Intelligence is deemed to be the main driver of the 4th Industrial Revolution. Investment in AI has grown at a phenomenal rate with companies investing $26-39bn in 2016. Adoption in 2017, however, remains low. As a result, this has spurred companies from every industry 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.

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. Application of AI in the financial service industry is fundamental to the competitiveness and automation process for years to come. This event is a sophisticated conference that not only interrogates and explores the implications of AI in the financial services industry but also goes on to identify the regional business and investment opportunities.

Additionally, 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 at this conference. Participants will be presented with real insights on how they can exploit these technological advances for themselves and their companies.

Attend this event and earn GARP/CPD credit hours.

UNICOM has registered this program with GARP for Continuing Professional Development (CPD) credits. Attending this program qualifies for 7 GARP CPD credit hours. If you are a Certified Financial Risk Manager (FRM®), please record this activity in your Credit Tracker.

Topics Covered Include:

  • AI Experience and Fintech – Disrupting our world
  • Trends & Opportunities: a regional ecosystem to meet the global needs, M&A Hotspots, Where is China!
  • Wealth Management, Family office, expectations of the modern HNWIs and global institutions
  • 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 like to stimulate your brain cells!
  • Excellent networking opportunities throughout the days with all participants, including presenters, investors and exhibitors.


  • Programme Chair: Professor Gautam Mitra, OptiRisk Systems/UCL, UK -

  • 09:00 -

    Welcome and Introduction

    Professor Gautam Mitra, OptiRisk Systems/UCL, UK


    Gautam Mitra

  • 09:15 -

    Extracting Embedded Alpha in Social & News Data Using Statistical Arbitrage Techniques

    Arun Verma, Quantitative Researcher, Bloomberg LP

    ♦ Extracting actionable information in the high volume, time-sensitive environment of news and social media stories
    ♦ Using machine learning to address the unstructured nature of textual information
    ♦ Techniques for identifying relevant news stories and tweets for individual stock tickers and assigning them sentiment scores
    ♦ Demonstrating that using sentiment scores in your trading strategy ultimately helps in achieving higher risk-adjusted returns


    Arun Verma

  • 10:00 -

    Title TBA

    Wendy Cheong, Moody’s Investors Services


    Wendy Cheong

  • 10:30 -


  • 11:00 -

    Natural Language Processing for Event Extraction

    Johnson Poh, Head, Data Science, DBS Bank / Adjunct Faculty, Singapore Management University

    Much information is embedded within the large volumes of unstructured data that we so often neglect in the implementation of business analytics. How do we seamlessly classify and extract key ideas with automation? In this presentation, we explore the open source tools, algorithms and services that relevant for the design of a reference architecture to surface underlying insights from texture descriptions.


    Johnson Poh

  • 11:30 -

    Application of Generative Adversarial Networks (GANs) in Algorithmic

    Mohammad Yousuf Hussain, Senior Technology and Innovation Specialist at HSBC

    As digital innovation and cognitive solutions gain more traction, there is a need to create greater awareness and familiarity with the latest technology trends amongst ourselves. Generative Adversarial Networks (GANs) seems to be advancing well through their hype cycle and are entering the phase of widespread deployment.

    In this session, the presenter will provide an overview of the GANs framework and highlight their explain ability through the concepts of game theory, enabling the discussion to move towards the application of GANs in algorithmic trading. The main use case would be about independent behaviour modelling of the market participants, construction of objective functions and suitable optimisation techniques.


    Mohammad Yousuf Hussain

  • 12:00 -

    Japanese Language News Classification for Investors

    Satoshi Shizume, Financial Technology Research Institute

    In this presentation, I am going to introduce the Japanese language news analyzer ‘News Dolphin’, an innovative tool that classifies Nikkei news for hedge funds and investment managers. It analyzes sentiments (positive or negative) by using both rule base and machine learning. News Dolphin generates various classification outputs such as referred companies, keywords, and events for each article. I will present this unique news analyzer including details of its engine, back test of its sentiments and its use for investors.


    Satoshi Shizume

  • 12:30 -


  • 13:30 -

    Keynote 1 - Towards Empathetic Human-Robot Interactions

    Professor Pascale Fung, Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong

    “Sorry I didn’t hear you” may be the first empathetic utterance by a commercial machine. As people increasingly interact with voice and gesture controlled machines, they expect the machines to recognize different emotions, and understand other high-level communication features such as humor, sarcasm and intention. To make such communication possible, the machines need an empathy module - a software system that can extract emotions from human speech and behavior and accordingly decide the correct response. This talk presents our work in the areas of deep learning of emotion and sentiment recognition, as well as humor recognition, using signal processing techniques, sentiment analysis and machine learning. It gives an overview of the future direction of android development and how it can help improve people's lives.


    Pascale Fung

  • 14:15 -

    Daily Trade Signals using Sentiment Analysis and Stochastic Dominance for Downside Risk Control

    Xiang Yu, OptiRisk Systems and Gautam Mitra, CEO/Director, OptiRisk Systems/UCL, UK

    We have created an innovative and dynamic trading strategy for equities, with a particular focus on controlling downside risk. The mathematical concept behind the approach is called stochastic dominance, where investment decisions are based on distributions rather than moments. A major contribution of news sentiment is in the prediction of future distributions. Regression analysis on news sentiment and regime switching models are employed to digest market moods and account for changing market situations.


    Gautam Mitra

    Xiang Yu

  • 15:00 -


  • 15:30 -

    Big Data Problems and Techniques in Finance

    Juho Kanniainen, Professor of Financial Engineering, Tampere University of Technology, Laboratory of Industrial and Information Management, Finland

    Nowadays, available datasets are so large and complex that such "Big Data" is becoming difficult to process with the current data management tools and methods. This data could provide valuable information to design trading algorithms, manage risks, and supervise markets. At the same time, financial research has been quite slow to embrace the data revolution. This talk elaborates the opportunities and challenges of using data science methods and large data sets in finance-related industries and research.


    Juho Kanniainen

  • 16:00 -

    Why algorithmic trading in the real world is so different to academic experiments

    Humberto Brandão, Federal University of Alfenas (Brazil)

    It is not difficult to find academic papers showing how to make money easily using algorithmic trading, which includes graphs, statistical tests, etc. However, in real markets, the majority of them cannot be replicated. In this presentation, I will discuss some reasons for this problem and try to explain how to improve validation processes before applying an algotrader in real stock exchanges.


    Humberto Brandão

  • 16:30 -

    Keynote 2 - Approaches to Market Forecasting with Media Sentiment Data

    Richard Peterson, MarketPsych

    Dr. Peterson will describe the unique characteristics of media sentiment data and approaches to financial price prediction with this data. The basics of media sentiment data, various modeling approaches, and their results (including live trading results) will be described in this talk. Viewers will gain an understanding of real-world modeling tips and techniques when dealing with noisy and inconsistent data such as media sentiment streams.


    Richard Peterson

  • 17:00 -


  • 17:30 -



Conference & Workshops

Rajib Ranjan Borah

CEO iRage, India

Humberto Brandão

Federal University of Alfenas (Brazil)

Ernie Chan

QTS Capital Management, LLC.

Wendy Cheong

Moody’s Investors Service

Pascale Fung

Hong Kong University of Science and Technology

Mohammad Yousuf Hussain


Juho Kanniainen

Tampere University of Technology

Andrea Madotto

Human Language Technology Center (HLTC)

Gautam Mitra

OptiRisk Systems

Richard Peterson

MarketPsych, USA

Johnson Poh

DBS Bank

Jayadeep Shitole

OptiRisk Systems

Satoshi Shizume

Financial Technology Research Institute

Arun Verma


Chien-Sheng Wu

Human Language Technology Center (HLTC)

Xiang Yu


Gold Sponsor

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Supporting Bodies


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6 March
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7 March

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8 March
Price per workshop

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  • Standard Price - HK$ 3500/per workshop
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  • The Salisbury Hong Kong 41 Salisbury Road, Tsim Sha Tsui, Kowloon Hong Kong
  • +852 5808 9126
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