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

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.

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

K S Somasundaram, Chief Enterprise Risk Officer, NSE

TBC

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.

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.

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

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.

Tejasvi Addagada, Director of Board – Marketing & Relations, IQ International

Clean Data is a crucial need to get an outcome from Machine Learning capabilities. Scale and diversity in data is also another important aspect. How accurate is the data to give a usable outcome – is a major question? This is related to Data Accuracy

What is easy to access – are the machine-learning services and algorithms, but data is still the prime constituent of AI. The basic predictive efficiency of AI models is defined by diversity, scale and quality of input data. This is associated with Data Coverage & Availability.

Most of the data with Information aggregators or large institutions is not consistent across systems and processes, while it is also not consistently formatted across the organization. These aspects form Structural Consistency & Semantic Consistency.

Vishvesh Chauhan, Founder & Managing Partner, Chase Alpha Asset Advisors

Using the Machine learning algorithm to design a quantitative trading system. Momentum trading is one of the highest used strategies in markets. We will try to showcase how we incorporate ML into your existing trading framework.

Utpal Chakraborty, Head, Artificial Intelligence, YES Bank

In the current decade, the world has achieved an enormous amount of technological advancement and skyrocketing progress in mass Digitization, Data Science and FinTech. In fact, we are currently living in the golden era of AI & FinTech.

With the advent of fast computing speed and low-cost storage for the enormous amount of data that is available for everyone, Artificial Intelligence has become paramount in our daily lives.

Vikram Pandya, Director FinTech, SP Jain School of Global Management

With advancements in Artificial Intelligence and Machine Learning, fabric of traditional automation is undergoing rapid changes. In this session we are going to understand techno-strategic considerations in automation lifecycle management and how to integrate emerging technologies to increase productivity, better customer targeting, revenue optimization and cost reduction using various case studies and hands-on examples.

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

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

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

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.

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

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.