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.
Machine learning and sentiment analysis are specific techniques that are applied in AI. These techniques are maturing and rapidly proving their value within businesses. 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.
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 14 GARP CPD credit hours. If you are a Certified Financial Risk Manager (FRM®), please record this activity in your Credit Tracker.
Topics Covered Include:
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.
Ashok Banerjee, Departmental Head of Finance and Control, Indian Institute of Management, Calcutta (IIMC), India
Using the qualitative information present in corporate annual reports of companies registered and operating in India, we have systematically evaluated the language and content of annual reports, particularly text and observed that the negative sentiments in the qualitative information starts increasing approx. 3-4 years before the year of credit default event and remains very high after the credit default event. The finding is based on a text-based analytical model that evaluates three sections of a corporate annual report- Directors Report, Audit Report and Notes to Accounts.
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.
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; this presentation gives an overview of new developments that have brought both challenges and benefits to the complexities of the Indian financial system.
Prithwiraj Mukherjee, Indian Institute of Management, Bangalore
Seeding, or providing free samples of a product, is a popular way of speeding up sales by marketers. Seeding also has its limitations, as a free sample could be expensive, as well as represent a lost sale. Previous work on seeding of markets with new products has typically focused on seeding at time t=0, based on assumed knowledge of diffusion model parameters. We build on this work to explore the possibility of seeding later on, either because the diffusion parameters may not be properly known, or to investigate if multiple seeds could be more beneficial than a single seed. We also present results from agent-based simulations to identify whom to target on social networks.
Humberto Brandão, Data scientist
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.
Krishma Singla, Managing Consultant - Data Science and Cognitive Computing, Watson Cognitive Solutions, IBM Global Business Services
Through use case examples, Krishma sums up the journey of Artificial and Augmented Intelligence to date, with particular focus on finance. She describes new developments in fintech and other related areas that demonstrate how the finance industry is evolving with AI and other modern technologies
Anders Bally, CEO and Founder, Sentifi
In the early 90’s, the majority of financial market participants used news mainly from services like Bloomberg and Reuters to inform themselves. 20 years later, they still do. During the same period, our society went through a communication paradigm shift. Today more than 2 billion people walk around with mobile devices and communicate what they see and think on social media. These billions of voices, when structured, can generate insights which can help investors make better investment decisions. This presentation will touch on how Sentifi structures and delivers these insights, providing an information advantage for media platforms globally.
Dr. Vijay Srinivas Agneeswaran, Senior Director of Technology, SapientRazorfish
Recommendation systems are all around us. Ecommerce companies like Amazon recommend goods that we are likely to buy based on our past behavior. Netflix suggests what videos we should watch. Pandora even builds personalized music streams, based on what we are likely to listen to. Almost every website has a recommendation system based on user browsing history, past purchases, past searches, and preferences.
It turns out most existing recommendation systems are based on three paradigms: collaborative filtering (CF) and its variants, content-based recommendation engines, and hybrid recommendation engines that combine content-based and CF or exploit more information about users in content-based recommendation. This presentation gives a start-of-art view of building deep learning-based recommendation and learning-to-rank systems using TensorFlow, including model management and scaling.
Nishant Chandra, Data Science Leader, AIG Science
Deep learning has created a revolution in the natural language processing domain 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 generic open source tools are available in machine learning, including deep learning, which can be customized for natural language processing. This presentation will help the audience to go beyond generic NLP problem solving by leveraging deep learning, customizing it for their industry. Specifically, they’ll learn that:
♦ Sentiment doesn’t have to be positive, negative or neutral but it can be extracted from the conversation
♦ Summarization doesn’t have to be entire document but only certain context
♦ Text classification doesn’t have to be exactly text/phrase/spelling based but can also include variation of acronym and synonym
♦ NLP can be applied broadly, and complex use cases can be built through intelligent iteration on simple examples.
Hari Sankaranarayanan, Director – Engineering, Amadeus Software Labs India Pvt Ltd.
Travel planning is always interesting and exciting yet matching everyone taste, preferences and approach for travel planning is a challenge. We have lot of chatbots in the marketplace now and the question is how contextual they are. Can they understand the traveler, location, interest and converse naturally like a real travel agents. In this session we will discuss the contextual issues that can make the bot more interesting or uninteresting. We will review some of the existing airline bots and how they use NLP, disambiguation and ontology of travel domain to the full use. We will discuss the trends, advancements and gaps on travel domain chatbots.
Dr.G.Sathis Kumar, Great Lakes Institute of Management
Use of Big Data and analytics for public policy is no longer a theoretical debate but is now in the early stages of a practical implementation. In a country like India, with its 1.3 billion people spawning enormous amounts of data every day, there is a unique opportunity to use Big Data Analytics to control the data behemoth and tame it for the country’s benefit. The presentation will investigate the deeper into various issues around the role of Big Data in Government schemes and projects like the Digital India, the UID Scheme (Aadhar), Electronic Transactions Aggregation and Analysis Layer (e-TALL) and the Smart Cities Mission.
Shweta Ramesh, senior data scientist at Mad Street Den
With the online retail industry constantly evolving to meet customer needs, being able to run controlled experiments quickly has become imperative. While the standard A/B tests are still the preferred way, alternatives that are faster and practical for industry use are being explored. These include multi-armed bandit based A/B tests, impact measurement through structured equation modeling, using synthetic controls for experiments, etc. In this talk, we compare and evaluate bandit based A/B tests against standard A/B tests with results from real-world simulations; and discuss models to measure impact when it is not feasible to hold out a control group.