Artificial Intelligence and Machine Learning provide faster, more accurate assessment with enormous data processing powers, and consider a wide variety of factors which leads to better-informed and data-backed decisions. AI based outcomes are based on complex algorithms and sophisticated rules to help clients streamline and optimize processes ranging from digital adoption to personalized banking, fraud management, intelligent automation and NLP based customer satisfaction analysis.

Manisha Banthia has over 23 years of experience in Analytics in Financial Services and currently heads the Analytics CoE at Fiserv Global Services.

She has earlier worked in Infosys consulting team heading their analytics team and was instrumental in developing building analytics products, including a patent for ‘Customer Analytics for Enterprises’.During her stints at Oracle Financial Services Software (earlier iflex solutions) she built analytics solutions for Citibank (Asia Pacific), and US clients like American Stock Exchange and Country Financial.

Manisha has also worked on Marketing Analytics, Financial Performance and Fraud and Risk Analytics during her tenure at MetricStream and National Stock Exchange of India.

Manisha is based out of Bangalore and heads analytics teams in India, Costa Rica and US for Fiserv.

In e-commerce , customer experience can be divided into broadly 3 parts - pre purchase, purchase and post purchase. Drivers of good customer experience vary across these 3 parts of the customer journey. At Myntra , we heavily use data based insights and advanced ML models to identify what product features, merchandise selection and service propositions optimise customer experience while helping the business meet it's financial goals. Some examples include search personalisation, catalogue optimisation using computer vision, automated return approval for "trusted" customers etc. The presentation would cover some examples of projects that have been undertaken at Myntra and also talk about what metrics should be tracked to ensure your company is meeting the customer experience goals.

Natural Language Processing has been the new chip in the block of AI. There has been a significant progress in the domain of language understanding using Deep learning technology. This has given rise to plethora of applications which uses language , written or spoken form, as one of the key medium of communication. Language understanding, especially understanding the context or the big picture, was one of the key challenges.

Thanks to the progress of Deep Learning computing, more specifically evolution of RNN network, this is now falling in line and has been a tremendous success. Automated translation, now almost in real time, Google Duplex AI voice assistant placing a call for seat reservation in Restaurant is a reality. Amazon Alexa has almost become a household butler.

This summer, Tokyo Olympic 2020 will see lot of innovative voice enabled solution to bridge the language barrier. NLP has a tremendous potential as key AI technology and this technology will impact almost all types of business and value added services. It will unblock a key bottleneck in the area of process automation.

In this talk we will explore the evolution of NLP technology, core building blocks and computing backend with some example use cases. We will also talk about few uses cases we have implemented in Tata Elxsi - for example , Virtual sales assistant and Virtual Car Assistant.

Biswajit Biswas is Chief Data Scientist @ Tata Elxsi heading the AI Center of Excellence responsible for driving innovation in Digital systems for Communication, Media and Transportation business verticals . He has over 22+ years of Industry experience.

He is very well versed in AI and Deep learning , especially, in the area of computer vision, language and text processing . Before heading Data Science team, he was responsible for Wireless Communication, Multimedia Systems, Digital signal processing , Image, Video and Audio coding, Machine Vision System. He has hands on with developing algorithms in C/C++, Python, TensorFlow, R and devotes significant time in coding, coaching team in various development activities. He also holds a patent for innovation in V2X related technology and has filed Patents for few other innovations in the area of Predictive Machine learning.

He is a Certified Data Scientist from Massachusetts Institute of Technology (MIT, US) Media Lab , Masters from BITS and BE from Jadavpur University Calcutta.

In the modern world with its myriad of decision points, recommender systems are ubiquitous to save time and effort of customers. Recommender systems have been popularized through the Netflix competition held started in 2006. Today it is used by Amazon, Spotify, LinkedIn etc to name a few. This session would cover the highlights of how to build a recommender system starting from overview of data to various machine learning methods that can be used.

Data science and Machine learning (DS & ML) platforms are now quite popular and there are more than a dozen good platforms available in the market from reputed vendors. What are DS & ML platforms and what are the advantages of having one in your enterprise. How do you decide whether you need one? Do you need it now or should you wait? What are the capabilities that you should be looking for in your Data science platform?. Learn in this session

Vinod Khader has around 20 years experience in Software Development and is an Associate Director at IBM Software Labs in the Data and AI division. In the current role, he leads the development of Watson Machine Learning Platform on IBM's public and private cloud platforms with teams across the globe. Watson Machine Learning platform help Data Scientists, Data Engineers and App Developers in managing the end to end Machine Learning and Model management life cycle starting from Training, Evaluation, Deployment and Scoring (prediction).

Most of them think that AI/ML is a complete black box and only accuracies matter, which is not the case in industry level scenarios or any Machine Learning problem statements.

Interpretation of ML models is highly important and how do we interpret that and tweak it to get industry level stats is the thing which will be covered in this session

Since we have only 30 minutes for the presentation, I am planning to take some of the below use cases from Banking.

(1) Real Time Offer Recommendation System. Recommend real time merchant offers for customers based on his current geo location on Bank app. Give customer offers based on cluster of Merchants in the area he is visiting. These recommendations will be based on their propensity and interest segments.

(2) Leads Optimization using Machine Learning. Recommendation of a right product for a customer through right channel based on propensity, profitability, risk and utility of a product.

(3) Credit Card Attrition model. Machine Learning models to identify the customers who are likely to move out from bank beforehand so the campaign team can devise proper marketing strategies.

(4) Anti Money Laundering. Machine Learning models to predict the customers who are engaging in Anti Money Laundering.

(5) ATM cash Optimization. Machine Learning Models to predict the ATM replenishment, frequency and the cash.

(6) Face recognition for Branch Customers. Identification of preferred & AML watchlist customers who walk into branch. Use of deep learning models to identify customers (Preferred & AML watchlist) walking in into the bank.

One complete demo on Visual Question Answering with step by step explanation using Tensorflow or Pytorch Assistant.

Building products that scale from mobile apps, backend architectures, ERP system to IoT based analytics. I am a regular speaker at tech events & conferences including TEDx as well as frequent guest lecturer at engineering colleges. Apart from that I have won 17 hackathons till now and participated in 50+ hackathons.

Specialties:

Quick learner, Building business that works, Hiring & Building the team from scratch, Managing Products and teams, Interacting with all stake holders, Exploring latest technologies and bringing into products

Working on end-to-end application design and development on cloud - AWS, Azure & IBM Softlayer, writing mobile sdk, Raspberry pi, arduino, Augmented Reality, Virtual Reality based applications.

AutoML, as the name suggests, is the process of automating the process of applying machine learning to real-world scenario.

When applying machine learning models, people generally do data pre-processing, feature engineering, feature extraction and, feature selection. After all this, one can select the best algorithm and fine tune the parameters in order to obtain the best/optimized results. AutoML is a series of concepts and techniques that used to automate these processes.

There are many service providers for AutoML like Amazon SageMaker, Azure Machine Learning AutoML, Google Cloud AutoML etc. There exists many AutoML framework like TPOT, Auto Sklearn etc. AutoML helped a lot in terms of reducing human efforts.

A dynamic result oriented professional with blend of Data Science, Analytics Consulting, Business Analytics and project & people management experience comprising of 15 + years from project scoping to entire execution, in several successful shared services organizations in India in technology, CPG & retail sector.

o Has global experience in-terms of developing and implementing marketing, communication, Marketing Mix Modeling, 360-degree unified view of customers, different types of predictive modeling (Churn, next most logical product etc.), and business solutions across different domains.

Also has rich experience in formulating and implementing light listening, deep listening, loyalty analysis, competitive landscape analysis & sentiment prediction models using Social media data for different brands/domains. I also have worked in Marketing Media spend as well as channel optimization.

She Have proven record of accomplishments of setting up new processes & procedures & nurturing the fresh talent into specialist. She

Most of them think that AI/ML is a complete black box and only accuracies matter, which is not the case in industry level scenarios or any Machine Learning problem statements.

Interpretation of ML models is highly important and how do we interpret that and tweak it to get industry level stats is the thing which will be covered in this session

How is TVS motors deploying AI and ML enabled solutions to improve customer experience

Anand is part of senior leadership team at TVS motors. He leads the data engineering, data science & BI capabilities for customer facing functions like sales, marketing, distribution network, Digital, parts, services etc. In his current role, his goal is to help end-customers get a world class TVSM experience, help channel partners become profitable and help TVS drive sales growth and market share. He intends to do this by enabling faster and better decision making with actionable AI and world-class data management.

In business areas where there are decisions involved that have social implications and we have seen biases in human decisions or data skewed undesirably towards a particular category, it is important that our ML/AI algorithms are tuned to eliminate any such bias that are bound to creep in automatically due to historical data. How to we identify those scenarios and eliminate biases effectively from our Machine Learning algorithms.

Manas heads the Wealth Technology Business Unit for ANZ Bank in Bengaluru, a man with diverse interests apart from his role in Technology Delivery – a passionate champion of Innovation, he has helped institute an Innovation Framework in ANZ Wealth Globally including an Innovation Contest that is saving hundreds of thousands of dollars. Manas is also the Chair of the ICT-Academia Expert Committee of Bangalore Chamber of Industry and Commerce (BCIC), where he is presently leading the organisation in a number of Industry-Academia initiatives.

While enterprises are increasingly adopting AI, building a scalable AI platform is becoming very important. Such next-gen platform should be able to handle large scale data, bring diverse algorithms, support different infrastructure for AI model deployment. Finally, it should cater to a large audience that can get implement diverse AI use cases. In this talk, we will focus on key principles for building such platforms.

Dr. Rahul Ghosh is a Research Director at American Express AI Labs. In his current role, Rahul manages AI Products team to build next generation ML/AI products, platforms and capabilities. Prior to joining in Amex, he worked at Xerox Research, IBM Software Group and IBM T. J. Watson Research Center. Rahul received his PhD from Duke University, USA. He is co-author 30+ peer reviewed research and co-inventor of 40+ US patents (granted/pending).

Service Desks requires good strategy and innovation to improve customer service and to support business goals. Cisco TAC is constantly looking at ways to provide customers with the information they need via a self-service capability in an attempt to reduce the number of support requests that are opened. This is frequently referred to as case avoidance. A significant amount of information is lost while exchanging mail, chats or over case notes.

The impediment to progress is the inability to derive actionable insights from this unstructured text since most of the prioritize corrective action is buried in these notes. Using advanced ML techniques along with text mining solution, the objective is to extract actionable insights for TAC that can be used to drive down Service Request volume. The proposed solution comprises of text parser and automated case deflection Conversational NLP solution which can provide an immediate solution using historic data analysis"

Vivek is currently a data scientist @Cisco. He has 7+ years of work experience across E-commerce, mortgage, Retails and CPG domains. He is an extensive NLP researcher, he has a pragmatic approach in text mining, NLP, Machine learning and social media analytics. Prior to Cisco, he has also worked across organizations like Xerox, BRIDGEi2i and Altisource.

Introducing latest development in NLP systems which enhances the usability and reliability of Conversational AI powered applications, especially in Customer Support systems like Smart IVRs and AI Chatbots. Also, how to go about adding support for Vernacular languages to increase customer engagement.

Nishant is an experienced Big data stack developer working at Microsoft Azure Storage Platform team. He has been instrumental in research for developing Vernacular Language Understanding Infrastructure for Indian languages using latest advancements in NLU domain.