Background

sentiment-logoTechnology innovations meet greatest success in business when these are entirely ‘client focussed’. Developments in the retail sector, which is consumer-led, are addressing client demand for more personalised, faster and competitive services. Artificial Intelligence, Machine Learning and Sentiment Analysis are changing the way in which these services are offered. In particular Financial Organisations are creating and leveraging such innovation in the domain of wealth management. This trend is now being taken on board by multiple innovators: academia, start-ups, technology companies and financial market participants.

Sentiment Analysis (SA) is a technology that makes rapid assessment of the sentiments expressed in news releases as well as other media sources such as Twitter and blogs. This conference addresses and explains how to extract sentiment from the multiple sources of information and showcases the advances that have taken place in the field of financial innovation, as well as new applications in the domain of Retail and Consumer Marketing.

Event Format:

Day 1 Day 2
Consumer Markets Stream Consumer Markets – Research & Academic Focus
[Cnm – Res]
Consumer Markets – Industry Focus
[Cnm – Ind]
Financial Markets Stream Financial Markets – Industry Focus
[Fin – Ind]
Financial Markets – Research & Academic Focus
[Fin – Res]

 

Partner

The conference is being organised in collaboration with partner organisations that have considerable experience in the field and bring new insights and interested parties to the table. In Bangalore, the partner is the well-respected Indian Institute of Management (IIM) Calcutta which is home to the Financial Research & Trading Lab, Indian Institute of Management (IIM) Bangalore.

Location

After the great success of the London and Singapore events, we are now bringing the conference to Bangalore[Bengaluru]. Bangalore[Bengaluru] is often referred to as the “Silicon Valley of India” (or “IT capital of India”) because of its role as the nation’s leading IT exporter. So this makes it a highly appropriate venue with access to a large pool of qualified and enthusiastic professionals.

Topics to be covered include:

  • ♦ Pattern classifiers, 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
  • ♦ Applications in Financial Markets
  • ♦ Applications in Retail and Consumer Markets.

Speakers

vivek-bajaj

Vivek Bajaj

Director, Kredent Ventures

ajit-balakrishnan

Ajit Balakrishnan

CEO, Rediff.com, India

Ashok Banerjee Speaker in Sentiment Analysis Conference

Ashok Banerjee

IIM Calcutta

kingshuk-banerjee

Kingshuk Banerjee

IBM Global Business Services

Prof Sankarshan Basu Sentiment Analysis in Bangalore 2017

Sankarshan Basu

Indian Institute of Management, Bangalore (IIMB), India

vidhu-beohar

Vidhu Beohar

Lead Analyst, Bank of America

svetlana-borovkova

Svetlana Borovkova

Vrije Universiteit Amsterdam, Netherlands

nishant-chandra

Nishant Chandra

R&D Scientist, AIG

gaurav-gaba

Gaurav Gaba

AVP at Societe Generale Global Solution Centre

arup-ganguly

Arup Ganguly

University of Pittsburgh, U.S.A

madhu-gopinathan

Madhu Gopinathan

Vice President, Data Science, MakeMyTrip

dummyimage

Anup Gunaseelan

Manager, LatentView Analytics

avadhoot-jathar

Avadhoot Jathar

Senior Statistician, Analytics Quotient

m-jeevananthan

M Jeevananthan

Thiagarajar School of Management

niraj-s-kakkad

Niraj S Kakkad

InvestAscent Wealth Advisors Pvt Ltd

arun-mallavarapu

Arun Mallavarapu

Fedo

enza-messina

Enza Messina

University Milano Bicocca

deepak-mishra

Deepak Mishra

Thomson Reuters

gautam-mitra

Gautam Mitra

OptiRisk Systems

pavinder-monga

Pavinder Monga

Citibank India

monika-ms

Monika MS

Thiagarajar School of Management

prithwiraj-mukherjee

Prithwiraj Mukherjee

Indian Institute of Management, Bangalore

vishesh-nigam

Vishesh Nigam

Concentrix

robin-paniker

Robin Panicker

SEQATO Software Solutions

Richard Peterson Sentiment Analysis in Bangalore 2017

Richard Peterson

MarketPsych, USA

varsha-ps

Varsha PS

Alliance University, Bangalore

korcha-teja-sai

Korcha Teja Sai

Sri Venkateswara University

kalya-lakshmi-sainath

Kalya Lakshmi Sainath

Lloyds Business School

kunal-saxena

Kunal Saxena

Alliance University, Bangalore

keshav-sehgal

Keshav Sehgal

Walmart Data Science Labs Bangalore

suman-singh

Suman Singh

Chief Analytics Officer, Zafin

gaurav-singh

Gaurav Singh

Founder, Verloop

Nitish Sinha Sentiment Analysis in Bangalore 2017

Nitish Sinha

Federal Reserve Board

shabbir-tayabali

Shabbir Tayabali

Senior Manager - Analytics, Oracle

Xiang Yu Sentiment Analysis in Bangalore 2017

Xiang Yu

OptiRisk Systems

Programme

  • 09:30 -
    gautam-mitra

    Welcome and Introduction – Professor Gautam Mitra, OptiRisk Systems

    Speakers:
    gautam-mitra

    Gautam Mitra

  • 09:35 -
    ajit-balakrishnan

    Keynote 1: The 21st Century Marketing Armory

    Ajit Balakrishnan, Chief Executive Officer, Rediff.com

    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.

    Speakers:
    ajit-balakrishnan

    Ajit Balakrishnan

  • 10:10 -
    Nitish Sinha Sentiment Analysis in Bangalore 2017

    Keynote 2: What’s the Story? A New Perspective on the Value of Economic Forecasts

    Nitish Sinha, Senior Economist, Federal Reserve Board USA

    In this talk I will discuss a co-authored work with Steve Sharpe that applies tools from the emerging literature on textual analysis to evaluate a key dimension of the information conveyed in the narratives that accompany Federal Reserve's greenbook forecasts. In particular, we quantify the degree of optimism versus pessimism embedded in the text, which we call the “tonality” of the text. We find that tonality has significant and often substantive directional predictive power for three key macroeconomic variables — namely unemployment, GDP growth, and inflation. Tonality also predicts forecast errors of Blue Chip forecasts. The analysis and conclusions set forth are those of the speaker and do not indicate concurrence by other members of the research staff or the Board of Governors.

    Speakers:
    Nitish Sinha Sentiment Analysis in Bangalore 2017

    Nitish Sinha

  • 10:50 -

    Coffee

  • 11:20 -
    Xiang Yu Sentiment Analysis in Bangalore 2017

    Beating Markowitz with Sentiment and Downside Risk Control

    Xiang Yu, Business Development Techno Executive, 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.

    Speakers:
    Xiang Yu Sentiment Analysis in Bangalore 2017

    Xiang Yu

  • 11:50 -
    Richard Peterson Sentiment Analysis in Bangalore 2017

    Keynote: Approaches to Market Forecasting with Media Sentiment Data

    Richard Peterson, CEO, MarketPsych Data, USA

    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.

    Speakers:
    Richard Peterson Sentiment Analysis in Bangalore 2017

    Richard Peterson

  • 12:30 -
    Ashok Banerjee Speaker in Sentiment Analysis Conference

    Predicting Corporate Default using Text

    Ashok Banerjee, Departmental Head of Finance and Control, Indian Institute of Management, Calcutta (IIMC), India

    Banks and financial institutions in emerging markets are saddled with a huge proportion of bad loans. Banking regulations require lenders to provide for troubled debt which adversely affects the profitability of banks. The capital market also reacts negatively to such write-offs of big ticket debts. Banks are, therefore, putting significant resources into developing early warning signals to arrest eventual default. The financial institutions use a wide range of default prediction models to estimate the loan loss. These models use data from financial statements and the market. The present study shows that such models fail to provide effective early warning signals. We use annual reports of companies to develop a default model which is predictive and hence has the capability of providing early warning signals. Using information from Directors' Reports, Audit Reports and notes to accounts, our model successfully discriminates the 'good' firms from the 'bad' ones.

    Speakers:
    Ashok Banerjee Speaker in Sentiment Analysis Conference

    Ashok Banerjee

  • 13:00 -

    Lunch

  • 14:00 -
    enza-messina

    Keynote: Deep Learning and Ensemble Methods for sentiment analysis

    Enza Messina, Professor in Operations Research, University of Milano-Bicocca, Italy

    We show how deep learning and ensemble methods can successfully address challenging problems arising in sentiment analysis such as irony detection or domain adaptation. In particular, we propose an unsupervised framework for domain-independent irony detection built upon an existing probabilistic topic model initially introduced for sentiment analysis purposes. Moreover, in order to improve its generalization abilities, we apply Word Embeddings to obtain domain-aware ironic orientation of words. The acquisition of cross-domain high level feature representations through word embeddings combined with the generalization capability of ensemble methods can also be used for addressing the problem of domain adaptation also in the scenario where the testing target domain is completely unlabeled.

    Speakers:
    enza-messina

    Enza Messina

  • 14:40 -
    deepak-mishra

    Driving Customer Retention & Growth using Machine Learning on Media

    Deepak Mishra, Head of Customer Analytics & Platform - Asia, Thomson Reuters

    Speakers:
    deepak-mishra

    Deepak Mishra

  • 15:10 -

    Tea

  • 15:40 -
    niraj-s-kakkad
    vivek-bajaj

    Special Session:

    TBA
    Praloy Mujumder, CEO, Disseminare Consulting Pvt. Ltd.

    TBA
    Prateek Agrawal, Director, IVY Professional School

    Modern Technology for Financial Research
    Vivek Bajaj, Director, Kredent Ventures

    Financial markets offer tremendous data points that needs regular coding & decoding. The whole industry thrives on the probability of predicting the future based on the past data points. In today's fast moving world with shorter attention time span, a participant needs a strong tool to comprehend data faster and effectively. Stockedge is doing something interesting for Indian equity market participants. With over 150000+ downloads in just 6 months and 4.7 rating by over 3000 users, the tool is empowering Indian investors in true sense.

    AI – Impact on Indian Financial Markets – Mutual Funds
    Niraj S Kakkad, AVP – Private Wealth Advisory, InvestAscent Wealth Advisors Pvt Ltd

    Artificial intelligence (AI) is thinking capabilities built into machines which help in logical reasoning and problem solving. AI is an imminent change in the Indian financial markets, advisors must embrace it. AI can help comprehend a client’s expectations from investments by helping with cognitive deductions of risk appetite, deriving portfolio weightage, fund selection etc. I intend to present a Critical SWOT analysis of the impact of AI on Indian Financial Markets - focused on client advisor relationship.

    Speakers:
    niraj-s-kakkad

    Niraj S Kakkad

    vivek-bajaj

    Vivek Bajaj

  • 17:15 -

    Close

  • 09:30 -
    Ashok Banerjee Speaker in Sentiment Analysis Conference

    Attention and Sentiment

    Ashok Banerjee, Departmental Head of Finance and Control, Indian Institute of Management, Calcutta (IIMC), India

    Studies at the Finance Lab of IIM Calcutta show that the effect of any news on the market depends on the attention of investors. If the attention of investors were somewhere else, even news carrying strong positive/negative sentiment would go unnoticed or would be penalized. This is particularly true in the case of major non-market attention grabbing events. In other words, attention overwhelms the effect of sentiment. Processing any attention-grabbing event requires effort. If that effort is directed towards some particular information, people are too lazy to put inthe extra effort to process any other information at the same time, no matter how much sentiment that information might carry.

    Speakers:
    Ashok Banerjee Speaker in Sentiment Analysis Conference

    Ashok Banerjee

  • 10:00 -
    arup-ganguly

    Textual Disclosure in SEC Filings and Litigation Risk

    Arup Ganguly, PhD Candidate, Katz Graduate School of Business, University of Pittsburgh, USA

    Prior theoretical and empirical studies in both finance and accounting are split on the relation between information disclosure and litigation risk. I argue that more information is disclosed in the form of textual data (non-numerical form) through SEC filings that has not been analyzed in most of the extant studies. Using hand-collected data on federal civil securities class action lawsuits for the period 1996-2014 and widely used techniques in natural language processing and propensity score matched sample to address endogeneity concerns to some extent, I study whether the degree of information disclosure through texts in SEC filings (10-Ks and 10-Qs), the readability of such disclosures, and the sentiments generated through the choice of words, are associated with the incidence of securities class action lawsuits. I find results that are consistent with the theoretical view that argues that greater disclosure is often perceived as ex post misleading, precipitating securities class action litigations. Moreover, I find that despite the boiler plate nature of SEC filings, both readability of the text used and sentiments generated through the selection of words have a significant predictive power in explaining the likelihood of being sued by shareholders in class actions. Lastly, I document how managers alter their behavior with respect to textual disclosures pre- versus post-litigation using a standard difference-in-differences (DiD) framework. These results are robust to the use of different empirical specifications, controls and various measures of textual disclosure, readability and sentiments.

    Speakers:
    arup-ganguly

    Arup Ganguly

  • 10:30 -

    Coffee

  • 11:00 -
    dummyimage
    gaurav-singh
    kingshuk-banerjee
    pavinder-monga

    PLENARY Panel -– 90 minutes

    Panel Members:

    1. Kingshuk Banerjee, IBM Global Business Services;
    2. Pavinder Monga, Citi;
    3. Anup Gunaseelan, Manager, LatentView Analytics;
    4. Gaurav Singh, Founder, Verloop;
    5. Moderator: TBD

    Speakers:
    dummyimage

    Anup Gunaseelan

    gaurav-singh

    Gaurav Singh

    kingshuk-banerjee

    Kingshuk Banerjee

    pavinder-monga

    Pavinder Monga

  • 12:30 -

    Lunch

  • 13:30 -
    svetlana-borovkova

    Media Sentiment, Systemic Risk and New Investment Factors

    Svetlana Borovkova, Associate Professor of Quantitative Finance, VrijeUniversiteit Amsterdam, Netherlands

    This talk shows how we use media sentiment to measure risk in the global financial system. I introduce a new measure of systemic risk called SenSR (for Sentiment-based Systemic Risk) and demonstrate that this measure gives an early warning about financial system distress. I then discuss perceived financial networks, which we build using media attention directed to banks, and whose characteristics can also help us understand systemic risk. Finally, I address the construction of sentiment-based indicators, similar toSenSR, for other industries and their use as new factors for investment purposes.

    Speakers:
    svetlana-borovkova

    Svetlana Borovkova

  • 14:15 -

    An AdaBoost-Random Forest Approach for Stock Market Forecasting (TBC)

    Ved Prakash Upadhyayet al

  • 14:45 -
    m-jeevananthan
    monika-ms

    An Experimental Analysis on One Step Ahead Forecasting of Intraday Values

    M. Jeevananthan and Monika MS, Thiagarajar School of Management

    Forecasting stock market is difficult in nature because of its non-stationary and complex nature. Various researchers have predicted the stock market movement and prices for different markets using statistical and other models. Different stock markets may use different forecasting method for forecasting because of its fluctuating nature. In this study six different techniques: Multivariate Adaptive Regression Splines (MARSplines), Back Propagation Neural Network (BPNN), Support Vector Regression (SVR), Auto Regression (AR), Simple Moving Average (SMA) and Average method have been used to forecast the one-step ahead intraday stock indices of developed and developing nations namely NASDAQ Composite Index (NCI), United States (US); Financial Times Stock Exchange 100 (FTSE100), United Kingdom (UK); NIFTY 50 (NIFTY), India and Shanghai Stock Exchange Composite Index (SSE), China. With this predicted value a comparative study has been done in finding the best method of forecasting in all these four markets.

    Speakers:
    m-jeevananthan

    M Jeevananthan

    monika-ms

    Monika MS

  • 15:15 -

    Tea

  • 15:45 -
    keshav-sehgal

    Deep Learning for Stock Prediction

    Keshav Sehgal, Walmart Data Science Labs, Bangalore

    Deep neural networks in the recent years have successfully solved challenging problems from various domains. Prediction of stock price movements in financial markets is one of the challenging problems attempted by researchers. There have been limited attempts to use Deep Learning in the context of stock movement predictions. The current paper probes into using deep learning techniques to predict the daily price movements of equity stocks. Multi-layer perceptron and Recurrent Neural Networks were trained on price data of equities. The paper discussed detailed methodology of training and testing process. Both MLP and RNN model performed weak learners when assessed over test data. The results were consistently above the baseline accuracy.

    Speakers:
    keshav-sehgal

    Keshav Sehgal

  • 16:15 -
    Prof Sankarshan Basu Sentiment Analysis in Bangalore 2017

    Technology and Finance – Advantages and Pitfalls

    Sankarshan Basu, Professor in the Finance and Accounting Area at the Indian Institute of Management, Bangalore (IIMB), India

    Technology has significantly altered the way the world has known finance over the years. While this has brought significant benefits to the system and the economies as a whole, it has thrown up several challenges as well – some having quite disastrous consequences. This talk will dwell upon the benefits that technology has provided to the financial sector in general and the banking sector in particular at the same time highlighting the pitfalls that have sprung up in the process. Part of the talk also looks at how technology can be used in a dynamic environment context to address some of the issues related to the pitfalls and more particularly what, if any, measures can be taken to reduce the pitfalls in the future.

    Speakers:
    Prof Sankarshan Basu Sentiment Analysis in Bangalore 2017

    Sankarshan Basu

  • 16:45 -

    Summing up; Close of conference

  • 09:35 -
    gautam-mitra

    Welcome and Introduction – Professor Gautam Mitra, OptiRisk Systems

    Speakers:
    gautam-mitra

    Gautam Mitra

  • 09:35 -
    ajit-balakrishnan

    Keynote 1: The 21st Century Marketing Armory

    Ajit Balakrishnan, Chief Executive Officer, Rediff.com

    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.

    Speakers:
    ajit-balakrishnan

    Ajit Balakrishnan

  • 10:10 -
    Nitish Sinha Sentiment Analysis in Bangalore 2017

    Keynote 2: What’s the Story? A New Perspective on the Value of Economic Forecasts

    Nitish Sinha, Senior Economist, Federal Reserve Board USA

    In this talk I will discuss a co-authored work with Steve Sharpe that applies tools from the emerging literature on textual analysis to evaluate a key dimension of the information conveyed in the narratives that accompany Federal Reserve's greenbook forecasts. In particular, we quantify the degree of optimism versus pessimism embedded in the text, which we call the “tonality” of the text. We find that tonality has significant and often substantive directional predictive power for three key macroeconomic variables — namely unemployment, GDP growth, and inflation. Tonality also predicts forecast errors of Blue Chip forecasts. The analysis and conclusions set forth are those of the speaker and do not indicate concurrence by other members of the research staff or the Board of Governors.

    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.

    Speakers:
    Nitish Sinha Sentiment Analysis in Bangalore 2017

    Nitish Sinha

  • 10:50 -

    Coffee

  • 11:20 -
    prithwiraj-mukherjee

    Comparing the BG-NBD and Markov Chain Methods to Model Non-Contractual Customer Churn

    Prithwiraj Mukherjee, Indian Institute of Management, Bangalore

    Modelling customer churn is important for retailers, especially when dropout is unobserved. We compare two popular methods used by managers – the BG-NBD model (Fader, Hardie and Lee 2005) that uses recency and frequency as inputs, and Markov Chain models incorporating threshold recency as a dropout assumption. We compare these approaches on parameters like accuracy and computational load across multiple data sets.

    Speakers:
    prithwiraj-mukherjee

    Prithwiraj Mukherjee

  • 12:00 -
    avadhoot-jathar

    Retailers and Resellers: Implications for Pricing

    Avadhoot Jathar, Senior Statistician, Analytics Quotient

    Demand faced by organized retailers in emerging markets comes from both households and resellers. Retailer’s price promotions must not be left to leave money on the table. Descriptively, undirected classification highlights uptake by resellers, and we further present this unique context of demand aggregation. Our analysis suggests deals with quantity ceilings can be a useful pricing tool for retailer’s category management.

    Speakers:
    avadhoot-jathar

    Avadhoot Jathar

  • 12:30 -

    Open Position

  • 13:00 -

    Lunch

  • 14:00 -

    A Tale of Consumer Perception Analytics (TBC)

    Mokhalles M. Medhi

  • 14:30 -
    kunal-saxena
    varsha-ps

    AI, Machine Learning and Sentiment Analysis Applied to Consumer Markets

    Kunal Saxena, Associate Professor of Marketing, and Varsha PS, Assistant Professor, Alliance University, Bangalore

    This presentation gives a brief outline of the history and applications of Artificial Intelligence (AI) in numerous areas. It presents some real-world examples of artificial intelligence in consumer markets, such as marketing/advertising applications with significant traction and burgeoning future applications for AI in marketing / advertising.

    Speakers:
    kunal-saxena

    Kunal Saxena

    varsha-ps

    Varsha PS

  • 15:00 -
    nishant-chandra

    Natural Language Understanding in the Era of Deep Learning: Technology, Tools and Tips

    Nishant Chandra, R&D Scientist, AIG

    The latest advances in natural language understanding has created a massive paradigm shift in dealing with text related data problems. Deep learning has created a revolution in the NLU space and corporations are leveraging it in various ways. The technology barrier is significantly reduced with open source technologies that are easy to configure and use. Several open source tools are available in the machine learning domain for traditional natural language processing to deep learning. Helpful implementation tips will be provided along with evaluating the technologies and tools.

    Speakers:
    nishant-chandra

    Nishant Chandra

  • 15:30 -

    Tea

  • 16:00 -
    kalya-lakshmi-sainath
    korcha-teja-sai

    A study on Sentimental Analysis for Better Consumer Markets with reference to Modern Technology

    Kalya Lakshmi Sainath, Lloyds Business School and Korcha Teja Sai, Sri Venkateswara University

    This study supports sentiment analysis as additional research technique for accumulating and analysing recorded data on the internet. Sentiment analysis is a data excavating technique that steadily gauges textual content using machine learning techniques. As a research method in marketing, sentiment analysis provides a proficient and real valuation of consumer views in real time. It allows data collection and analysis from a huge sample without interferences, impediments and time delays.The paper concludes with the challenges marketeers can face when using this practice in their research work.

    Speakers:
    kalya-lakshmi-sainath

    Kalya Lakshmi Sainath

    korcha-teja-sai

    Korcha Teja Sai

  • 16:30 -

    Open Position

  • 17:15 -

    Close

  • 09:30 -
    madhu-gopinathan

    From Keywords to Concepts for Sentiment Analysis

    Madhu Gopinathan, Vice President, Data Science, MakeMyTrip

    The algorithms for analyzing sentiment ranges from using simple wordlists with positive and negative words to employing our brains for deep understanding of natural language text. This talk focuses on how we can go beyond mere word level analysis to concept level analysis and then extracting relations between concepts for improving sentiment analysis.

    Speakers:
    madhu-gopinathan

    Madhu Gopinathan

  • 10:00 -
    gaurav-gaba

    Risk Management and Financial Crime Compliance (FCC) using AI & Machine Learning

    Gaurav Gaba, AVP at Société Générale Global Solution Centre

    This presentation covers the Genesis, Conventional Approach and Change to Transformational Risk Management Practices via Artificial Intelligence with machine learning and sentiment analysis capabilities. It further gives a view of the future where the focus lies on pure quality, efficiency and practices built on knowledge which is a foot wide but a mile deep.

    Speakers:
    gaurav-gaba

    Gaurav Gaba

  • 10:30 -

    Coffee

  • 11:00 -
    dummyimage
    gaurav-singh
    kingshuk-banerjee
    pavinder-monga

    PLENARY Panel -– 90 minutes

    Panel Members:

    1. Kingshuk Banerjee, IBM Global Business Services;
    2. Pavinder Monga, Citi;
    3. Anup Gunaseelan, Manager, LatentView Analytics;
    4. Gaurav Singh, Founder, Verloop;
    5. Moderator: TBD

    Speakers:
    dummyimage

    Anup Gunaseelan

    gaurav-singh

    Gaurav Singh

    kingshuk-banerjee

    Kingshuk Banerjee

    pavinder-monga

    Pavinder Monga

  • 12:30 -

    Lunch

  • 13:30 -
    robin-paniker

    Applying AI to Complement Decision Making

    Robin Panicker, CEO, SEQATO Software Solutions

    In this presentation, we discuss how we can use artificial intelligence and predictive analysis to provide accurate insights that will help consumers in optimised decision making. Machines cannot beat the experience and expertise of a human brain. At the same time, humans cannot beat the speed and perseverance of an automated algorithm. Combining the two leads to production of accurate, optimal and best of the breed solutions. We will talk about how some complicated use-cases in financial and healthcare domain can be cracked easily with the help of AI and ML.

    Speakers:
    robin-paniker

    Robin Panicker

  • 14:00 -
    shabbir-tayabali

    Consumer Market and Decision Trees

    Shabbir Tayabali, Senior Manager - Analytics, Oracle

    Decision Tree is a proven and favorite tool among decision makers when the decision making process is complex (includes multiple variables, multiple alternatives and multiple dependencies). Today technologies have empowered customers in various ways thereby impacting their purchase process and the very fundamental purchase funnel. On the other hand, technological advancements have also resulted in various sources of information that suppliers can tap into to understand customer better. However, for this, they have to use various ML algorithms and the one they will always need to understand the customer’s mind is the Decision Tree. In collaboration with other machine learning algorithms, Decision Tree can give a deeper view into the minds of customers that other algorithms cannot.

    Speakers:
    shabbir-tayabali

    Shabbir Tayabali

  • 14:30 -
    vidhu-beohar

    Auto Discovery of Product Features from Customer’s Feedback

    Vidhu Beohar, Lead Analyst, Bank of America

    Today most of the organizations which offer products and services to their customers, face a severe challenge to provide the best services and features. At the same time, customers may not like some of the offered features and services. This results in a negative sentiment for the organization. In order to keep the customer happy and provide them with the features and services that they are looking for, organizations are dependent on market surveys and customer’s feedback. Often these feedbacks are unstructured and very large in volume, which makes it difficult to discover any subtle suggestions. In this paper, we shall discuss different Deep Text Analytics approaches for auto discovery of the features embedded deep in customers’ feedback.

    Speakers:
    vidhu-beohar

    Vidhu Beohar

  • 15:00 -

    Tea

  • 15:30 -
    arun-mallavarapu

    Healthcare AI: Challenges & Opportunities

    Arun Mallavarapu, Co-founder,Fedo

    Healthcare industry has historically been a laggard in adopting new technologies. So, why would the fate of AI/ML be any different? In a healthcare setting, a big bang approach to refine traditional practices is bound to fail. This ultraconservatism in the healthcare industry is not just due to inertia but is driven by the primary motive to “do no harm”. In addition, the challenges of implementing AI/ML algorithms are compounded due to issues such as data privacy, lack of datasets and EHR integration. Lot of new initiatives have started to foster progress in this area. One such example is healthcare.ai – a platform that facilitates model building for applications in the healthcare space. The adoption however, remains low.

    Our presentation will delve deeper into some of these issues and demonstrate with use cases how adoption of AI/ML could be improved within the healthcare industry. One such use case is the use of pattern recognition / classification models such as using random forest, SVM and logistic regressions for predicting the health risks of an individual.

    Speakers:
    arun-mallavarapu

    Arun Mallavarapu

  • 16:00 -
    suman-singh

    Role of Machine learning and Big Data Technology on Digital Platform for Retail Bank To Improve Customers’ Experience and Grow Profitability

    Suman Singh, Chief Analytics Officer, Zafin

    Banks are increasingly under pressure to provide a more personalized and improved customer experience. With eroding revenue streams, intensifying competition, and ever-increasing customer expectations, financial institutions need to explore a new way of doing business. Advanced statistical methods and machine learning algorithms can help banks to become smarter and more diligent in dealing with their customers. Measuring customer relationship, evaluating the customer journey and recommending right bundle of product & services at the right price in real time through technology-enabled digital platforms, will be the vital enablers to improve the customers’ banking experience.

    Speakers:
    suman-singh

    Suman Singh

  • 16:30 -
    vishesh-nigam

    Conversational Bots Gratifying Customer’s Engagement

    Vishesh Nigam, GM - Global Strategic Assets Area, Concentrix

    Autonomous interactive technologies are helping enterprises to solve their Business Problems and transforming an experience into conversational, personalized, and instantly gratifying engagement for customers and workforce. This new technology goes under different names, e.g. Conversational Bots, ChatBot, Virtual Customer Assistance(VCA) and Virtual Interaction etc.

    This presentation addresses current requirements, as organisations seek to understand which is the right omni-channel tool to extend concierge-like services; how to create the best customer experience by selecting the most appropriate tool, as well as to best deploy Natural Language Processing, Information retrieval and continuous improvement through artificial intelligence and sentiment analytics.

    Speakers:
    vishesh-nigam

    Vishesh Nigam

  • 17:00 -

    Summing up; Close of conference

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Tickets

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  • IIM Bangalore, Bannerghatta Road, Bilekahalli, Bengaluru, Karnataka 560076
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