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, CEO, OptiRisk Systems/Visiting Professor, UCL -

  • 09:00 -

    Welcome and Introduction

    Professor Gautam Mitra, CEO, OptiRisk Systems/Visiting Professor, UCL


    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 -

    Enhanced Trading Strategy using Sentiment and Technical Indicators

    Gautam Mitra, CEO, OptiRisk Systems/Visiting Professor, UCL, and Xiang Yu, OptiRisk Systems

    We compute daily trade schedules using a time series of historical equity price data and applying the powerful mathematical concept of Stochastic Dominance. In contrast to classical mean-variance method this approach improves the tail risk as well as the upside of the return. In our recent research we have introduced and combined market sentiment indicators and technical indicators to construct enhanced RSI and momentum filters. These filters restrict the choice of asset universe for trading. Consistent performance improvement achieved in back-testing vindicates our approach.


    Gautam Mitra

    Xiang Yu

  • 10:45 -


  • 11:15 -

    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

  • 11:45 -

    Application of Generative Adversarial Networks (GANs) in Algorithmic Trading

    Mohammad Yousuf Hussain, Senior Technology and Innovation Specialist, 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

  • 12:15 -

    Japanese Language News Classification for Investors

    Satoshi Shizume, Deputy Managing Director, 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

  • 12:45 -


  • 13:45 -

    Keynote 1 - Big Data Analysis in Finance Industry

    Lei Chen, Professor of Computer Science & Engineering, Hong Kong University of Science and Technology

    Big Data has made great impact in many application fields, together with advanced AI technology, it will change our world dramatically. In this talk, I will first start with several cases to motivate the importance of Big Data analysis in Finance Industry. Then, I will discuss the challenges in applying Big Data analysis. Finally, I will highlight some possible solutions to address those challenges.


    Lei Chen

  • 14:30 -

    Case Studies of How Moody’s Leverage on Technology to Resolve Corporate Pains

    Wendy Cheong, Moody’s Investors Service, Head of Hong Kong

    Moody’s is an acknowledged global leader in assessing credit risk, with more than 100 years of experience in the credit rating business. Our work is the product of our very diversified workforce and its resulting analyses and research. As technology has introduced both opportunity and disruption in many areas, we have willingly acknowledged and embraced technological innovation, seeing it as essential to reinforcing our relevance and importance to the credit landscape. In this presentation, we will provide two case studies, showing how we leverage on two different technologies to resolve inefficiencies and create capacity for higher valued-added work. The two technologies are Robotic Process Automation or “bots”, and Natural Language Processing and Generation.


    Wendy Cheong

  • 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

    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, Head of R&D Lab, Federal University of Alfenas

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

    Richard Peterson, CEO, MarketPsych

    In this talk Dr. Richard Peterson describes how media analytics are providing new insights into the origins and topping process of asset price bubbles.  Examples from price bubbles including the China Composite, cryptocurrencies, housing, and many others will be explored.  Recent mathematical models of bubble price action will be augmented with sentiment analysis.  Attendees will leave with new models for identifying and taking advantage of speculative manias and panics.


    Richard Peterson

  • 17:00 -


  • 17:30 -



Conference & Workshops

Rajib Ranjan Borah

CEO iRage, India

Humberto Brandão

Federal University of Alfenas

Ernie Chan

QTS Capital Management, LLC.

Lei Chen

Hong Kong University of Science and Technology

Wendy Cheong

Moody’s Investors Services

Mohammad Yousuf Hussain


Juho Kanniainen

Tampere University of Technology

Gautam Mitra

OptiRisk Systems/Visiting Professor, UCL

Richard Peterson


Johnson Poh

Singapore Management University

Satoshi Shizume

Financial Technology Research Institute

Arun Verma

Bloomberg LP

Xiang Yu

OptiRisk Systems

Gold Sponsor

Silver Sponsors


Knowledge Partners

Supporting Bodies


Media Partners



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6 March
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7 March

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8 March
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  • Standard Price - HK$ 3500/per workshop
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