Schedule

08:30

REGISTRATION

09:15

WELCOME

RETAIL BANKING

09:30

Deep Learning in Customer Analytics

09:50

Sonam Srivastava

Sonam Srivastava, HSBC

How Machine Learning is Transforming Finance

How Machine Learning is transforming Finance

Machine learning is slowly but surely transforming the way we trade, in this presentation I want to highlight how this transformation is taking place citing examples from personal experiences and beyond. I would then like to highlight the deep learning techniques and research I think are most relevant to training a model based on financial data and their significance. I would like to share some insight into understanding financial data and the value added by understanding the features. I would also talk about the thrill of using ML in finance – how a small optimization can bring about significant alpha! I would conclude by envisioning the trading rooms of the future based on the transformation.

Sonam works as a Quant Analyst for the HSBC Central Risk Book desk. She does short term stock return prediction and quantitative portfolio construction research for building optimal systematic trading strategies. Her work involves feature exploration and supervised learning using econometric time series models. Prior to this Sonam worked at the Algorithmic Trading Desk at Edelweiss for 4 years where she built high frequency execution and arbitrage algorithms for the brokerage house that traded a significant percentage to India’s total traded volume. Sonam has been involved in deep learning research since her graduation years in IIT Kanpur, which she graduated in 2010.

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10:10

Devendra Swami

Devendra Swami, Axis Bank Innovation Lab

Deep Learning in ATM Banking Security

Devendra has started his career in Axis Innovation Lab, the fintech centre of Axis Bank Ltd. where he closely works with the bank’s business teams and startup community from fintech sector to steer cutting edge innovation through collaboration. At Innovation lab, he is currently working with Uncanny Vision, a fintech startup with expertise in deep-learning to deploy computer vision and audio analytics in the field of banking surveillance systems. Devendra has completed his B.Tech & M.Tech in Civil Engineering from Indian Institute of Technology, Kanpur and has been an exchange student at Infrastructure Laboratory, University of Tokyo.

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10:30

Edouard D'archimbaud

Edouard D'archimbaud, BNP Paribas

From Unstructured Text to Knowledge Graphs: How to Leverage Dark Data

From unstructured text to knowledge graphs: how to leverage dark data

The banking world is full of dark data: unstructured information that does not live in orderly databases. As we move toward conversational applications, the ability to dig up dark data will become even more critical. While companies say they want a single view of the customer, what they really need is a multi-dimensional view so that every business stakeholder can look at a client’s information from the perspective that matters to them. Deep learning methods such as named entity recognition and co-reference resolution are used to find structure in unstructured data and populate knowledge databases and graphs.

Present Edouard d'Archimbaud is a data scientist in charge of the Data Science and Artificial Intelligence Lab for BNP Paribas CIB. He joined the group in 2016, having previously held different positions focusing on advanced technological research and operational projects in major banking institutions and hedge funds.

Education Edouard graduated from Ecole Polytechnique in Paris, with a specialisation in applied mathematics and computer science. He holds a M.Sc. in Machine Learning and Computer Vision from ENS Cachan.

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10:50

COFFEE

FINANCIAL MARKETS

11:30

Soner Teknikeller

Soner Teknikeller, Lion & WSU

Predicting Foreign Exchange Market Turbulence using Thick Neural Networks

Predicting Foreign Exchange Market Turbulence using Thick Neural Networks.

Foreign exchange markets are notoriously unpredictable and often expose international businesses and governments to potentially devastating risks. A case for deploying Thick Neural Networks (TNN) as a method to flag periods of exchange turbulence is presented. Following a brief introduction of the phenomena being modelled (exchange rate volatility) and the method deployed a walkthrough will be given on several (neural network) architecture considerations which enhance overall network predictive performance. A novel way for presenting network topology will be shared along with a method for selecting the best parameters whilst evaluating architectures (Teknikeller, 2014). Thick Neural Networks developed are presented with their out-of- sample turbulence flagging abilities and results are benchmarked against orthodox methods.

An inquisitive engineer turned pioneer economist. Awarded his doctorate (WSU, 2013) in developing novel macro-economic models through deploying Artificial Neural Networks and Machine Learning optimization. A professional committed to his corporate career (Lion) whilst lecturing and unit-coordinating masters and undergraduate units in Finance and Economics (Western Sydney University). Eagerly implementing refined techniques to address the sorts of complexities multinational businesses deal with in the areas of Pricing, Optimisation, Finance, Econometrics and Behavioural Economics. Outside of work he is an avid practitioner of Kendo and ambitious gardener who will go out of his way to find a live jazz band performance.

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11:50

Sandro Cavallari

Sandro Cavallari, Nanyang Technological University

Aspect Extraction for Opinion Mining with a Deep Convolutional Neural Network

Aspect extraction for opinion mining with a deep convolutional neural network

In this presentation, we present the first deep learning approach to aspect extraction in opinion mining. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text. A deep convolutional neural network has been used to tag each word in opinionated sentences as either aspect or non-aspect word. We also developed a set of linguistic patterns for the same purpose and combined them with the neural network. The resulting ensemble classifier, coupled with a word-embedding model for sentiment analysis, allowed our approach to obtain significantly better accuracy than state-of-the-art methods.

Sandro Cavallari received his BEng in Telecommunication Engineering in 2012 and his MEng in Computer Science in 2015 both from the University of Trento. After finalizing his thesis at the ADSC of Singapore in collaboration with the University of Illinois at Urbana Champaign, he has been awarded the prestigious SINGA scholarship and started his PhD at Nanyang Technological University in 2015 under the supervision of Dr Cambria. His research areas focus on the application of machine learning and natural language processing technique to perform stock market prediction.

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Soujanya Poria

Soujanya Poria, Nanyang Technological University

Joint Presentation

Aspect extraction for opinion mining with a deep convolutional neural network

In this presentation, we present the first deep learning approach to aspect extraction in opinion mining. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text. A deep convolutional neural network has been used to tag each word in opinionated sentences as either aspect or non-aspect word. We also developed a set of linguistic patterns for the same purpose and combined them with the neural network. The resulting ensemble classifier, coupled with a word-embedding model for sentiment analysis, allowed our approach to obtain significantly better accuracy than state-of-the-art methods.

Soujanya Poria received his BEng in Computer Science from Jadavpur University, India, in 2013. In the same year, he received the best undergraduate thesis and researcher award from Jadavpur University and was awarded Gold Plated Silver medal from the University and Tata Consultancy Service for his final year project during his undergraduate course. Soon after, Soujanya joined Nanyang Technological University as a research engineer in the School of Electrical and Electronics Engineering and, later in 2015, he joined NTU Temasek Laboratories, where he is now conducting research on aspect-based sentiment analysis in multiple domains and different modalities. In parallel with his research activities in Singapore, Soujanya is also in the process of finalizing his PhD studies in Computing Science and Mathematics at the University of Stirling, UK.

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12:10

Seth Huang

Seth Huang, Shanghai Advanced Institute of Finance MF at SJTU

Achieving Superior Volatility Prediction with Deep learning

Achieving Superior Volatility Prediction with Deep Learning

We employ Deep Learning to construct an information set to predict the weekly volatility changes from 2010 to 2016 for all stocks in Standard and Poor’s 500 index based on individual stock, market information and treasury note information. Using deep neural network, we train 50000 models and find the binary prediction results and achieved over 70% accuracy for about 500 stocks, month-on- month, for 52 periods from 2011 to 2016. It indicates that deep learning and related machine learning methodologies can be helpful in risk management and in economic stress tests when considering asset allocation.

Prof. Seth H. Huang is an AI researcher/ quantitative trader focusing on developing proprietary methodologies for trading. He is a Taiwanese American and received his PhD in Economics at Cornell University. He is currently also a Director at Shanghai Advanced Institute of Finance, Shanghai Jiao Tong University. His research field focuses on the Deep Learning application in risk management and quantitative trading in global financial assets. Before Shanghai, he lived in Seoul as a finance professor and researcher. He is a partner and adviser for Jumpgate Technologies, a quantitative, big-data-driven hedge fund. He is also a founder of Aris Capital Group, a research and futures trading firm.

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12:30

Shivaram Ramegowda

Shivaram Ramegowda, Societe Generale

Deep Autoencoders in Financial Markets

Deep Autoencoders in Financial Markets

I will be talking about applications of few deep learning algorithms in finance. First, I will talk about autoencoders for dimensionality reduction in relative value strategy. I will illustrate how it is different from conventional methods such as PCA or factor models. I will also address its limitations and things to keep in mind while building autoencoders for dimensionality reduction on financial data.

I will also talk about Generative Adversarial Networks (GAN) models and how they can be used to generate synthetic stock market returns. I will talk about GANs ability to match the fat tail distributions very accurately. I will address the issue of the autocorrelation of returns and how we can use GANs to capture it.

I am cross asset quantitative analyst at Societe Generale and I specialize in machine learning. I have been building quantitative models to forecast/analyze markets from last 10 years. Prior to Societe Generale, I was working as HFT trader at Edelweiss Securities, Mumbai. I use time series analysis, machine learning (deep learning), stochastic calculus, factor analysis and other mathematical techniques to build my models.

I have bachelor’s degree in Electronics and communication from RVCE, Bangalore and master’s degree in Control and Automation from IIT Delhi. I have several hobbies. I like building robots with computer vision, IOT devices and I have used use machine learning techniques to build models to forecast rainfall, analyze genomics data etc.

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12:50

LUNCH

INSURTECH

14:00

Siddhant Tiwari

Siddhant Tiwari, AXA Data Innovation Lab

Transforming Insurance through Machine Learning

Transforming Insurance through Machine Learning

Machine Learning and AI is playing crucial in transforming traditional insurers. It is helping insurers to deliver high value to its customers by simplifying products, standardizing underwriting, designing on-demand pricing solutions (telematics), and identifying fraudulent claims etc. In the presentation, I would share application of machine learning in insurance, insights about dealing with finance data , feature engineering and highlight the usability of machine learning models on real business problems.

Siddhant is an analytics professional working as a Data Scientist at AXA Data Innovation Lab, Singapore. His current role involves executing data science projects to support AXA entities in sales and marketing activities such as opportunity sizing, cross-sell and up-sell, product recommendations, quantifying risk, retention and fraud using advanced machine learning techniques. Previously, at ZS Associates, he consulted many Fortune 500 pharmaceutical clients in devising sales and marketing strategies to improve brand positioning in the market using data analytics. He holds a Bachelors of Engineering (Hons.) in Electrical and Electronics from BITS Pilani, India and MSc in Business Analytics from National University of Singapore.

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14:20

Cyber Insurance and Deep Learning Methods

14:40

PANEL: Insurance Claims Analysis using Deep Learning

15:20

COFFEE

TRADING

16:00

Yue Zhang

Yue Zhang, Singapore University of Technology and Design

Deep learning for Stock Market Prediction

Deep learning for stock market prediction

In this talk I share with you two of our recent research projects leveraging unstructured data for predicting stock market movements. In the first project, we use open information extraction techniques to extract event structure out of news titles, representing the results using deep learning, and building neural network models to match them with S&P 500 company returns. In the second project, we extend the efforts further, by using deep learning models to directly learn representations of news abstracts, from which we predict cumulative abnormal returns of S&P companies over 3 days. The results are promising, beating sentiment baselines significantly without using time series data.

Yue Zhang is currently an assistant professor at Singapore University of Technology and Design. Before joining SUTD in July 2012, he worked as a postdoctoral research associate in University of Cambridge, UK. Yue Zhang received his DPhil and MSc degrees from University of Oxford, UK, and his BEng degree from Tsinghua University, China. His research interests include natural language processing, machine learning and artificial Intelligence. He has been working on statistical parsing, parsing, text synthesis, machine translation, sentiment analysis and stock market analysis intensively. Yue Zhang serves as the reviewer for top journals such as Computational Linguistics, Transaction of Association of Computational Linguistics and Journal of Artificial Intelligence Research. He is also PC member for conferences such as ACL, COLING, EMNLP, NAACL, EACL, AAAI and IJCAI. Recently, he was the area chairs of COLING 2014, NAACL 2015, EMNLP 2015, ACL 2017 and EMNLP2017.

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16:20

Ilija Ilievski

Ilija Ilievski, National University of Singapore

Deep Reinforcement Learning

Ph.D. candidate in Deep Learning, M.Eng. in Software Engineering for Machine Learning.

He is Interested in: Multimodal Deep Learning; Non-convex Optimization; (Visual) Question Answering; Natural Language Processing and Generation.

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16:40

Lipa Roitman

Lipa Roitman, I Know First

The Game of Stock Market. Deep Reinforcement Learning Trading System

With over 35 years of research in artificial intelligence and machine learning, Dr. Lipa Roitman has developed algorithms to predict chaotic event s in financial markets. M.Sc. in chemistry and organic chemistry from Novosibirsk University, Ph.D. in organic and physical organic chemistry, and photochemistry from the Weizmann Institute of Science, and a post doctorate in polymer chemistry from the University of Akron, and has a background in mathematics and statistics, Past work: R&D Chemist for Rohm and Haas Corp. USA. Co-Founder and CTO of I Know First Ltd., the company has used Dr. Roitman’s algorithm to predict markets and supply big data solutions for institutional and large private investors since 2010. Responsible for the design, development, characterization, specification, and implementation of algorithmic trading solutions. Other responsibilities include development of quantitative trading strategies, predictive analytics, and development of quantitative trading strategies, algorithmic trading applications, and Big-Data solutions for hedge funds and institutional clients.

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Yaron Golgher

Yaron Golgher, I Know First

Joint Presentation

Yaron Golgher is the Co-Founder and CEO of I Know First Ltd. I Know First is a FinTech company that brings science and math to the financial world by providing daily investment forecasts based on an advanced self-learning algorithm. The algorithm utilizes artificial intelligence and machine learning techniques through which I Know First is able to analyze, model and predict the stock market. Mr. Golgher Holds a Bachelor of Science (B.Sc.) in Industrial Engineering from Tel-Aviv University and an EMBA from Ben Gurion University. Yaron Golgher has over 18 years of experience in managing and leading consulting projects in industrial companies, services, finance, infrastructure and transportation organizations.

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17:00

CONVERSATION & DRINKS

08:30

REGISTRATION

09:15

WELCOME

STARTUP STAGE

09:30

Scott Treloar

Scott Treloar, Noviscient

Applying Autoencoding in Finance

Applying Autoencoding in Finance

Autoencoders are neural netowrks that are trained to replicate input using less information. In this presentation we explore the use of autoencoding to learn about latent factors that drive security returns. We extend this approach to create a dynamic autoencoding model. We apply these approaches to both simulated and actual financial return data sets and present the results.

Scott has 20 years of financial markets experience with investment banks and hedge funds. He is currently working with Noviscient (www.noviscient.com), a technology-led, research and proprietary trading firm based in Singapore. Noviscient applies statistical and machine learning technologies to investment management challenges. Prior to this he was Chief Risk Officer and Portfolio Manager at Vulpes Investment Management. Scott also spent eight years with Deutsche Bank in Singapore heading a quantitative team covering modeling and valuation risk for the Bank’s Asian trading businesses. And before that he worked in venture capital with Macquarie Bank in Sydney. Scott has a BEng and an MBA from the University of Melbourne, a Master of Quantitative Finance from the University of Technology, Sydney and he is completing a PhD in Finance with EDHEC. Scott has been coding in Python for around eight years and working with machine learning techniques for five years.

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09:45

David Samuel

David Samuel, RKR Epsilon & Prediction Machines

Learning to Trade with Q-RL and DQNs

Learning to Trade with Q-RL and DQNs

Price changes in financial products are largely random, representing an efficient market, but are often supplemented by salient features that provide additional structure which can be exploited for trading profits. Experienced traders are skilled at identifying such features and deploying profitable exploits. We present some methods for learning such exploits using Q- function based reinforcement learning and DQNs that are trained on simulation models for markets which progress through levels of realism with data provided by generative models that mimic both the randomness and salient features of the actual markets.

David Samuel is a veteran of the financial markets, working in trading and quantitative research for a number of investment banks and proprietary trading firms. He has a PhD in theoretical physics and published research in a number of top academic journals. David has been a lecturer for the MSc in Mathematical Finance at Oxford University and collaborated on the application of machine learning to trading with researchers at Cambridge University. His recent interest has been on the application of reinforcement learning and deep learning methods to trading. David is co-founder of Prediction Machines which develops algorithms for predicting and trading in commercial transactions markets.

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10:00

Multidimensional Intelligence & Financial Trading

10:15

Pattern Recognition & Mobile Banking

10:30

Deep Learning in Insurance

10:45

COFFEE

FINANCIAL RISK

11:30

Ratnakar Pandey

Ratnakar Pandey, Kabbage

Artificial Neural Network (ANN) based Fraud Detection & Prevention in FinTech Industry

Artificial Neural Network (ANN) based Fraud Detection & Prevention in FinTech Industry

FinTech has disrupted the financial services industry in many ways such as democratization of lending via peer to peer (P2P) lending, accessibility of capital to underserved or unserved segments, digitization of entire loan processing. In addition, decision making has been brought down from days to within few mins!

However, with faster/agile decision making and digitization of the loan process, the FinTech players are more exposed to fraud as compared to a bank. FinTech players are also hit some of the unique fraudulent activities such as “stacking” or “Ponzi scheme”.

Although statistical techniques such as Elastic Net, Gradient Boosting Tree have traditionally done a good job in identifying potential fraud cases, but with the ever evolving modus operandi of fraudsters we need to deploy self-learning/ deep learning algorithms such as ANN to stay one step ahead of fraudsters!

Ratnakar Pandey has 15+ years of experience in data science and deep learning fields. Currently he is heading the India Analytics and Machine Learning teams for Kabbage Inc where he is leading machine and deep learning models development activity across customer life cycle, from acquisition to customer engagement to fraud prevention to risk based underwriting policy development.

Before joining Kabbage, Ratnakar was part of the data science leadership team in Citigroup, Target, Texas Instruments, and few startups in India. Ratnakar holds an MBA from ISB Hyderabad, MS from University of Arkansas, and BTech from HBTI Kanpur.

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11:50

Financial Risk Assessment using Deep Learning

12:10

Edmond Juin Fung Chin

Edmond Juin Fung Chin, NTUC Income

Fighting Fraudulent Insurance Claims with Deep Learning

Fighting Fraudulent Claims with Deep Learning

Fraudulent Insurance Claim is a very prevalent issue in the insurance Industry. Its existence drives up the premium an insured has to pay for a particular policy. The common practice in handling such issue is very costly and inefficient. Fortunately, In the era of big data, advancement in technologies and deep learning methodology have enable us to overcome this by studying patterns in the past, along with texts and images, to come up with accurate prediction and estimation.

Currently a data analyst in Information Management Department, NTUC Income, Edmond has worked on numerous projects that use statistical modelling and machine learning algorithms in tackling business issues. His current field of interest includes market analytics, forecasting and recommender system. He holds a Bsc in mathematics and economics from Nanyang Technological University and is completing his MTech studies in Enterprise Business Analytics at National University of Singapore and Institute of System Science.

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12:30

LUNCH

ARTIFICIAL INTELLIGENCE

13:40

PIerre-Yves Peton

PIerre-Yves Peton, Commerzbank

Deep Learning, the bridge between Artificial Intelligence and Big Data Framework

Deep learning, the bridge between artificial intelligence and big data framework

The demands of investment funds and banks on high frequency trading and streaming analytics have pushed them to develop their own frameworks similar to Sparks (SecDb at GS, Athena at JP etc.) These framework originally designed for intensive financial models can now be used for much broader ‘data science’ and ‘machine learning’ problems whether in the trading/compliance/risk areas etc. My presentation will cover technical aspects of my previous experience in teaching undergrad student neural networks at University (MATLAB), My work experience in distributed computing and how the current theory and infrastructure have triggered a recent boom in Machine Learning.

Graduate from Ecole des Mines de Paris In Computer Science and Mathematics, Pierre-Yves has been enthusiastic about applied mathematics and machine learning ever since the start of his career over 10 years ago. As a JAVA and C# developer in different financial institutions, he had to the opportunity to work on multiple projects mostly around High Performance Computing, pricing and latency-sensitive applications. Today, he’s working at Commerzbank on a framework to stream real-time analytics

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14:00

Future Applications of Chatbot Technology

14:20

Chetna Goyal

Chetna Goyal, BlackRock

PANEL: How will Artificial Intelligence impact Financial Services?

Chetna is working as an analyst, part of the two year graduate programme at BlackRock where she is part of the Index/ETF business. In that capacity she is responsible for the design and implementation of index strategies across investment themes, geographies and asset classes. Alongside her role she is working as a researching student under Professor Kwoh at Nanyang Technological University (NTU). Her current research study is in deep neural networks, feature selection using signal processing focused on large data sets in the electroencephalogram (EEG) domain with an emphasis on stimulus recognition and mapping. She graduated from NTU with a B.Eng. in Computer Science Engineering with a focus on Artificial Intelligence in August 2015. She has previously worked in Bank of America Merrill Lynch as an intern on their technology team and been selected as student ambassador by Microsoft and Mozilla Firefox to recognize her technical caliber. She has also won numerous hackathons in the region and is extremely passionate about the impact technology especially in the field of intelligent machines can have on businesses.

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15:00

END OF SUMMIT

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