BACKGROUND

Artificial Intelligence and Machine Learning (AI & ML) and Sentiment Analysis are said to “predict the future through analysing the past” – the Holy Grail of the finance sector. They can replicate cognitive decisions made by humans yet avoid the behavioural biases inherent in humans.

Processing news data and social media data and classifying (market) sentiment and how it impacts Financial Markets is a growing area of research. The field has recently progressed further with many new “alternative” data sources, such as email receipts, credit/debit card transactions, weather, geo-location, satellite data, Twitter, Micro-blogs and search engine results. AI & ML are gaining adoption in the financial services industry especially in the context of compliance, investment decisions and risk management.

This is a sophisticated conference that not only interrogates and explores the implications of AI & ML in the financial services industry but also goes on to identify the investment opportunities of sharing knowledge and exploiting IP in the finance domain.

  • Learn how you can benefit from the unprecedented progress in technological advances for yourself and your company
  • Find out about the impact of Alternative Data from multiple sources.
  • Benefit from the experience of world class presenters from the UK, US and India
  • Gain exclusive insights into pioneering projects in AI, Machine Learning & Sentiment Analysis in Finance
  • Programme includes the latest state-of-the-art research, practical applications and case studies
  • Enjoy excellent networking opportunities throughout the days with all participants, including presenters, investors and exhibitors.

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 credit hours. If you are a Certified ERP® or FRM®, please record this activity in your Credit Tracker.

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Code of Conduct & Diversity

UNICOM’s Code of Conduct & Views on Diversity

We at UNICOM strive to be a leading provider of knowledge to the business community and to engage the global business community as a specialised provider of knowledge. We strive to do this maintaining a culture of co-operation, commitment and trust. We want every UNICOM conference and training day to be a safe and productive environment for everyone – a place to share research and innovation and to build professional networks. To that end, we will enforce a code of conduct throughout all our events. We expect cooperation from all participants to help ensure a safe environment for everybody.

Our approach is that our events are dedicated to providing a harassment-free experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity or religion. We do not tolerate intimidation, stalking, harassing photography or recording, sustained disruption of sessions or events, and unwelcome physical contact or sexual attention. We do not tolerate harassment of conference participants in any form. Sexual language and imagery is not appropriate for any conference venue, including talks, workshops, Twitter and other online media. Event participants violating these rules may be sanctioned or expelled from the event without a refund at the discretion of the conference organisers. Please bring your concerns to the immediate attention of the event staff.

Diversity: In our endeavour to be the provider of knowledge to the business community, we understand that this depends on hearing from and listening to a variety of perspectives that come from people of all races, ethnicities, genders, ages, abilities, religions, sexual orientation, and military service. We welcome diverse speakers for all our events, we do not always fully achieve this goal, but it is an ongoing process.

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Programme

Introduction and Background to the Program
Innovations in Finance and harnessing of Technology have led to the inevitable combination, whereby, in the domain of finance Fintech has appeared as a portmanteau word. In the evolution of the BFSI sector Fintech today is established as well as disruptive of traditional order.
Fintech has disrupted all aspects of the finance industry—banking and capital markets, asset and wealth management, insurance, and funds transfer and payments. The lending and payments sector is anticipated to experience a high level of disruption with the emergence of online platforms such as peer-to-peer personal loans. The themes of this conference are about multiple aspects of Fintech.

There are three main themes for the day:

Theme 1: Start-ups and New Opportunities in Fintech
Digitization of services is becoming the norm and customers expect more flexibility and interactivity. Reduction in entry costs due to tech support has created space for qualified finance professionals to join or create new start-ups, which has expanded exponentially in India over the last decade.

Theme 2: AI, Alternative data and Quantitative Fund Management using Sentiment Analysis (Presented in two Sessions)
The rise of AI and Machine learning has disrupted, as well as enhanced many aspects of investment models and technologies. Equally applications of Alternative Data in Fintech are growing at a great pace; the question is how to discover new sources of alpha and create strategies and signals. It is also related to alternative data as the use of multiple data sources exploit their hidden coupling. Text analysis, Natural Language Processing and analysis of News as well as investor sentiment is well established. Bringing all these advances together new applications in trading, fund management and risk control continue to emerge. Under this generic theme many aspects will be presented and discussed by presenters and panelists in two sessions.

Theme 3: Security in Fintech
Increasing foothold of fintech poses great challenges in managing the digital identities of individuals and enterprises. Adopting a combination of latest technology and conventional security architectures to prevent cross-platform malware contamination and exploitation of sensitive data in dark market.

PROGRAMME UNDER DEVELOPMENT

DAY 1 (4 HRS)
DAY 2 (4 HRS)
DAY 3 & 4

10 MIN CHAIRMAN’S INTRODUCTION  Read More
GAUTAM MITRA – CEO, OPTIRISK

20 MIN KEYNOTE  Read More
DK Aggarwal – Chairman & MD, SMC Investments & Advisors Ltd

THEME – START-UPS AND NEW OPPORTUNITIES IN FINTECH

15 MIN TALK
RECENT TECH BREAKTHROUGHS IN DEEP LEARNING/MACHINE  
Read More
AJIT BALAKRISHNAN, CEO, REDIFF.COM, INDIA

AJIT BALAKRISHNAN, CEO, REDIFF.COM, INDIA

What can R and Big Data do to throw new light on classic marketing challenges such as online consumer market segmentation and product recommendations… I will present a few cases of such applications using large scale Indian data and also cast an eye on where Deep Learning could go next.

10 MIN TALK
PRACTICAL USE-CASES OF HOW MACHINE LEARNING AND SENTIMENT ANALYSIS CAN TRANSFORM BFSI SECTOR  
Read More
INDRANEEL FUKE, FOUNDER, CEO, SIMPLEWORKS BUSINESS SOLUTIONS PTE LTD

INDRANEEL FUKE, FOUNDER, CEO, SIMPLEWORKS BUSINESS SOLUTIONS PTE LTD

Going beyond the hype of AI/ML, this session will cover practical use-cases of how AI/ML and Sentiment Analysis can contribute to the digital transformation journey of the BFSI sector. It covers 1) uses cases of facial recognition, OCR scanning, Liveness Detection, fake ID detection, etc from Vision AI perspective, 2) use cases of leveraging of Sentiment Analysis of customer interactions through multiple channels towards cross-sell and up-sell opportunities 3) Leveraging ML for predictions such as next action recommender, next best product to buy, identifying insurance customers at policy renewal risk, etc.

10 MIN TALK
CAN ARTIFICIAL INTELLIGENCE HELP RETAIL INVESTORS BEHAVE RIGHT IN A VOLATILE SHARE MARKET? 
Read More
SOUGATA BASU, FOUNDER, CASHRICH

SOUGATA BASU, FOUNDER, CASHRICH

Most retail investors need handholding when navigating numerous investment options and dealing with market uncertainties. Human advisors have been helping investors since a long time. In this discussion, we shall explore whether artificial intelligence and machine learning can provide similar or better level of service when compared to human advisors.

25 MIN Q/A WITH SPEAKERS: START-UPS AND NEW OPPORTUNITIES IN FINTECH

10 MIN COFFEE BREAK

THEME – AI, ALTERNATIVE DATA AND QUANTITATIVE FUND MANAGEMENT USING SENTIMENT ANALYSIS (SESSION 1)

25 MIN TALK
QUANTAMENTAL FACTOR INVESTING USING ALTERNATIVE DATA AND MACHINE LEARNING 
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DR. ARUN VERMA, BLOOMBERG L.P, PH.D, SENIOR QUANTITATIVE RESEARCHER & QUANT SOLUTIONS TEAM LEAD

DR. ARUN VERMA, BLOOMBERG L.P, PH.D, SENIOR QUANTITATIVE RESEARCHER & QUANT SOLUTIONS TEAM LEAD

To gain an edge in the markets quantitative hedge fund managers require automated processing to quickly extract actionable information from unstructured and increasingly non-traditional sources of data. The nature of these “alternative data” sources presents challenges that are comfortably addressed through machine learning techniques. We illustrate use of AI and ML techniques that help extract derived signals that have significant alpha or risk premium and lead to profitable trading strategies.

This session will cover the following topics:

♦ The broad application of machine learning in finance
♦ Extracting sentiment from textual data such as news stories and social media content using machine learning algorithms
♦ Construction of scoring models and factors from complex data sets such as supply chain graph, options (implied volatility skew, term structure), Geolocational datasets and ESG (Environmental, Social and Governance)
♦ Robust portfolio construction using multi-factor models by blending in factors derived from alternative data with the traditional factors such as fama-french five-factor model.

25 MIN TALK EQUITY TRADING STRATEGY USING SENTIMENT AND TECHNICAL INDICATORS Read More
GAUTAM MITRA, CEO, OPTIRISK SYSTEMS/VISITING PROFESSOR, UCL

GAUTAM MITRA, CEO, OPTIRISK SYSTEMS/VISITING PROFESSOR, UCL

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.

15 MIN TALK PREDICTING DISTRESS FROM THE TEXT OF CORPORATE FILINGS Read More
ASHOK BANERJEE, DEPARTMENTAL HEAD OF FINANCE AND CONTROL, INDIAN INSTITUTE OF MANAGEMENT, CALCUTTA (IIMC), INDIA

ASHOK BANERJEE, DEPARTMENTAL HEAD OF FINANCE AND CONTROL, INDIAN INSTITUTE OF MANAGEMENT, CALCUTTA (IIMC), INDIA

  • Qualitative information present in corporate annual reports
  • Data of 800 companies registered and operating in India
  • A text-based analytical model that evaluates three sections of an annual report
  • Directors Report, Audit Report and Notes to Accounts.
  • Predicts a credit default event 3-4 years before it happens

30 MIN PANEL & Q/A: AI, ALTERNATIVE DATA AND QUANTITATIVE FUND MANAGEMENT USING SENTIMENT ANALYSIS (SESSION 1)

45 MIN (FOR NETWORKING AND 1-MIN TALKS BY VENDORS)

THEME – SECURITY IN FINTECH

20 MIN TALK
NSE KNOWLEDGE HUB- AI FIRST LEARNING EXPERIENCE PLATFORM FOR BFSI  
Read More
ABHILASH MISRA – CEO, NSE ACADEMY

ABHILASH MISRA – CEO, NSE ACADEMY

‘The NSE Knowledge Hub’, is a unique Artificial Intelligence (AI) powered learning eco-system to assist the BFSI sector in enhancing skills for their employees and helping academic institutions in preparing future ready talent skilled for the financial services industry.

NSE Knowledge Hub aims to bring world class content closer to learners in a personalized and community learning environment. It allows aggregation, curation, creation and targeting of content which is both learner centric and learner driven. The learning platform is powered by content aggregated from many internal, external, and premium sources, and enhanced by collaborative knowledge sharing from users. With Artificial Intelligence (AI) and Machine Learning (MI) capabilities, the platform provides a comprehensive user-wise report with recommendation on learning opportunities towards skill gap or as a part of progression matrix.

Further, a Learning Experience Platform (LXP), an in-built application on NSE Knowledge Hub platform, functions as a curation and content aggregation layer between an organization’s internal digital learning assets, the vast amount of external content available on the Internet, and user generated content. Enterprises can upskill workforce and enhance employee performance through collaborative innovation. Learners can learn current skills and be future-ready through content/certifications that complements their academic curriculum.

NSE Knowledge Hub delivers a unique learning experience for users and enhanced levels of performance for organizations that produces dramatic business and economic impact and value.

15 MIN TALK
AI, MACHINE LEARNING AND FRAUD PREVENTION  
Read More
AVEZ SAYED – CRO, SBI GEN INSURANCE

AVEZ SAYED – CRO, SBI GEN INSURANCE

THEME – AI, ALTERNATIVE DATA AND QUANTITATIVE FUND MANAGEMENT USING SENTIMENT ANALYSIS (SESSION 2)

20 MIN TALK
RECENT TECHNOLOGY TRENDS IN DEEP LEARNING FOR FSI
Read More
SUNDARA RAMALINGAM N, HEAD – DEEP LEARNING PRACTICE, NVIDIA GRAPHICS PVT LTD, INDIA

SUNDARA RAMALINGAM N, HEAD – DEEP LEARNING PRACTICE, NVIDIA GRAPHICS PVT LTD, INDIA

The talk will cover the latest technology developments in the field of Artificial Intelligence, with focus on Deep Learning for FSI. It will touch upon how Global FSI companies are adopting AI and Deep Learning for their existing workflows like Risk analytics, Targeted customer campaigns, Document processing, Customer Churn prediction etc.

All aspects of the AI computing ecosystem including Frameworks, advancements in Training & Deployment etc., will be covered during the talk.

20 MIN TALK
ALTERNATE DATA BASED UNDERWRITING FOR LOANS 
Read More
SONAL KAPOOR, HEAD, CONSUMER LENDING BUSINESS, FLIPKART

SONAL KAPOOR, HEAD, CONSUMER LENDING BUSINESS, FLIPKART

Alternative data has come into the spotlight in financial services, and it presages a significant shift in credit availability for unbanked and underbanked consumers. There are about 70 million credit-invisible consumers in India who lack sufficient traditional credit data. Alternative data is the future of financial inclusion, enabling lenders to extend credit to consumers who have been credit-invisible using next-generation data sources to power both traditional and alternative credit models.

What is alternative data? It includes payment history for electricity, gas and telecom bills, rent payments, repayments to payday lenders, and information such as employment history and educational background. Although alternative data has proved to be valuable and insightful for making lending decisions, until recently, it has not been possible for it to play a meaningful role in credit scoring.

15 MIN TALK
UTILIZATION OF MARKET NEWS FOR IMPROVING THE DECISION MAKING PROCESS IN FINANCIAL SECTOR
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CHANDRASEKHAR SUBRAMANYAM, SR PROFESSOR AND DIRECTOR BUSINESS ANALYTICS CENTER, IFIM BUSINESS SCHOOL

CHANDRASEKHAR SUBRAMANYAM, SR PROFESSOR AND DIRECTOR BUSINESS ANALYTICS CENTER, IFIM BUSINESS SCHOOL

With the popularity of Digital Media large volume of Information about the Company,Industry are available from various sources some paid and unpaid like Bloomberg,Revenpack,News Agencies , Analyst reports , Company Web sites , Blogs , etc. All this is not used for decision making. Majority of these data is in the form of Text , Viedeo , Images etc.. They are also collectively known as unstructured data as they do not comply with standard definition of Data base. Sentiment Analysis is a type of Unstructured data analysis. It is a combination of Natural Language Processing , Statistics & Machine Learning to identify and extract subjective information from text.

This information can be effectively utilised to improve the decision making by extracting sentiment from large volume of such data and combined with other Machine Learning techniques such as Decision Trees, Support Vector Machines and many more techniques. Two use cases will demonstrate how this can be implemented to predict the Rating Transition of Financial instruments and predicting the Enterprise value of companies using real life data. Also talks about some of the challenges involved in data Integration

10 MIN COMFORT BREAK

20 MIN TALK
DISCOVERING INTELLIGENCE IN UNSTRUCTURED CONTENT 
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DAN JOLDZIC, ALEXANDRIA TECHNOLOGY

DAN JOLDZIC, ALEXANDRIA TECHNOLOGY

♦ Technology borrowed from the domain of DNA identification
♦ Design to identify cause and effect in large datasets
♦ Analysis of immense quantity of genomic information
♦ This AI&ML base technology applied to financial analytics

20 MIN TALK
CURRENCY AND FOREX FORECASTING WITH MEDIA SENTIMENT DATA  
Read More
ANTHONY LUCIANI, QUANTITATIVE RESEARCHER, MARKETPYSCH

ANTHONY LUCIANI, QUANTITATIVE RESEARCHER, MARKETPYSCH

In this talk Anthony Luciani 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

15 MIN TALK
SENTIMENT ANALYSIS FOR TRADING 
Read More
ISHAN SHAH, HEAD FOR CONTENT DEVELOPMENT, QUANTINSTI

ISHAN SHAH, HEAD FOR CONTENT DEVELOPMENT, QUANTINSTI

This workshop will demonstrate the cutting-edge natural language processing research in financial markets. This unique workshop will help you devise new trading strategies using Twitter, news sentiment data. Roadblocks and how to overcome them while working with unstructured data. And how long is the impact of the sentiments on the assets prices. You will learn to predict the market trend by quantifying market sentiments

35 MIN Q/A SESSION: AI, ALTERNATIVE DATA AND QUANTITATIVE FUND MANAGEMENT USING SENTIMENT ANALYSIS (SESSION 2)

15 MIN VALEDICTORY SESSION

35 MIN (FOR NETWORKING AND 1-MIN TALKS BY VENDORS)

MINING NEWS TOPIC CODES WITH SENTIMENT Read More
IVAILO DIMOV, QUANT RESEARCH SOLUTIONS, CTO OFFICE, BLOOMBERG L.P.

IVAILO DIMOV, QUANT RESEARCH SOLUTIONS, CTO OFFICE, BLOOMBERG L.P.

Stories on the Bloomberg newsfeed are tagged with “topic codes” containing information about their origin, subject matter, or other characteristics. One might expect that sentiment analysis of news stories may be enhanced by taking into account these topic codes, but the sheer number of topic codes is an obstacle to doing so systematically.

In this talk, we present evidence that some groups of topic codes are indeed associated with stronger sentiment impact on stock prices than others, and discuss a method to condense the mass of topic codes by identifying and retrieving latent factors which may be interpreted as broad themes shared by groups of topic codes.

NEWS SENTIMENT ANALYSIS IN EM SOVEREIGN DEBT RESEARCH, INVESTMENT AND RISK MANAGEMENT   Read More
JACOB GELFAND, CFA, DIRECTOR OF QUANTITATIVE STRATEGY AND RESEARCH, INVESTMENT RISK MANAGEMENT AND KAMILLA KASYMOVA, ASSOCIATE, QUANTITATIVE RESEARCH AND ANALYTICS, NORTHWESTERN MUTUAL

JACOB GELFAND, CFA, DIRECTOR OF QUANTITATIVE STRATEGY AND RESEARCH, INVESTMENT RISK MANAGEMENT AND KAMILLA KASYMOVA, ASSOCIATE, QUANTITATIVE RESEARCH AND ANALYTICS, NORTHWESTERN MUTUAL

We present a framework for ex-ante analysis of the USD denominated EM Sovereign spreads and respective currencies based on the flow of news in global and local media. The public domain GDELT data is currently being used, but the framework is agnostic to data source, and can be adopted to any data source. We will discuss use cases pertaining to selected countries and spearhead the discussion about predictive qualities of the produced analytics.

APPLYING MACHINE LEARNING TO SYSTEMATIC INVESTMENT STRATEGIES Read More
ANTOINE FRECHES, SENIOR VP, FICC TRADING, HAITONG INTERNATIONAL SECURITIES

ANTOINE FRECHES, SENIOR VP, FICC TRADING, HAITONG INTERNATIONAL SECURITIES

♦ What are the challenges linked to using machine learning techniques to design a systematic investment strategy linked to commodity futures?
♦ What data is relevant and of practical use to attempt to forecast the behaviour of a forward curve?
♦ Can sequence models (RNNs, LSTMs) apply to the noisy data of commodity markets?
♦ How complex can the overall model be for optimal performance and interpretability?

GOING NATIVE WITH JAPANESE NEWS ANALYSIS Read More
DAN JOLDZIC, ALEXANDRIA TECHNOLOGY

DAN JOLDZIC, ALEXANDRIA TECHNOLOGY

Local source, native publishers may offer an information advantage compared to publications in English. Translation services have typically been sub-optimal for character-based languages, but machine learning allows for classification in the native form, which can lead to significant alpha in forward periods.

BLOWING BUBBLES: QUANTIFYING HOW NEWS, SOCIAL MEDIA, AND CONTAGION EFFECTS DRIVE SPECULATIVE MANIAS Read More
RICHARD PETERSON, MANAGING DIRECTOR, MARKETPSYCH

RICHARD PETERSON, MANAGING DIRECTOR, 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.

CORRELATION INFLUENCE NETWORKS FOR SENTIMENT ANALYSIS IN EUROPEAN SOVEREIGN BONDS Read More
PETER SCHWENDNER, PROFESSOR, ZHAW SCHOOL OF MANAGEMENT AND LAW

PETER SCHWENDNER, PROFESSOR, ZHAW SCHOOL OF MANAGEMENT AND LAW

European sovereign bonds are especially sensitive to the political news flow. Consistent to the current sentiment, market makers adjust factor models in their quotation systems to be prepared for short-term market reactions in the most liquid instruments. We present a correlation influence network case study to make the signs of these factor betas transparent using intraday data analysis. This shows the sentiment of the most active market participants.

APPLICATION OF GENERATIVE ADVERSARIAL NETWORKS (GANS) IN ALGORITHMIC TRADING  Read More
MOHAMMAD YOUSUF HUSSAIN, SENIOR TECH AND INNOVATION SPECIALIST, JASMINE22

MOHAMMAD YOUSUF HUSSAIN, SENIOR TECH AND INNOVATION SPECIALIST, JASMINE22

♦ Overview of techniques explored for intelligent forecasting
♦ Lessons learnt – mistakes that can be avoided when using ML for forecasting
♦ Extracting signals from alternative data and integrating them into trading strategies

Speakers

Tejasvi Addagada

Director of Board – Marketing & Relations, IQ International

DK AGGARWAL

Chairman & MD, SMC Investments & Advisors Ltd

Ajit Balakrishnan

CEO, Rediff.com, India

Ashok Banerjee

IIM Calcutta

Anurag Bansal

Director, SMC Global Securities Limited

Sougata Basu

Founder, CashRich

Rajib Ranjan Borah

CEO iRage, India

Utpal Chakraborty

Head, Artificial Intelligence, Yes Bank

Vishvesh Chauhan

Founder & Managing Partner, Chase Alpha Asset Advisors

Amol Dethe

Editor, ETBFSI & ETCFO (By The Economic Times)


IVAILO DIMOV

Quant Research Solutions, CTO Office, Bloomberg L.P.


ANTOINE FRECHES

Senior VP – FICC Trading, Haitong International Securities

Indraneel Fuke

Founder, CEO, Simpleworks Business Solutions Pte Ltd (www.simplecrm.com)

JACOB GELFAND

CFA, Director of Quantitative Strategy and Research, Investment Risk Management

MOHAMMAD YOUSUF HUSSAIN

Alexandria Technology

Dan Joldzic

Alexandria Technology

Sonal Kapoor

Head, Consumer lending Business, Flipkart

KAMILLA KASYMOVA

Associate, Quantitative Research and Analytics, Northwestern Mutual

Anthony Luciani

Quantitative Researcher, MarketPysch

Abhilash Misra

CEO of NSE Academy Ltd.

Gautam Mitra

CEO, OptiRisk Systems/Visiting Professor, UCL

Vikram Pandya

Director FinTech, SP Jain School of Global Management

RICHARD PETERSON

Managing Director, MarketPsych

Sundara Ramalingam N

Head – Deep Learning Practice, NVIDIA Graphics Pvt Ltd, India

AVEZ SAYED

Chief Risk Officer, Heading Risk Management, Information & Cyber Security at SBI General Insurance

PETER SCHWENDNER

Professor, ZHAW School of Management and Law

Ishan Shah

Head for Content Development, QuantInsti

Chandrasekhar Subramanyam

Sr Professor and Director Business Analytics Center, IFIM Business School

Dr. Arun Verma

Bloomberg L.P, Ph.D, Senior Quantitative Researcher & Quant Solutions Team Lead
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