Schedule

08:30

REGISTRATION

09:15

WELCOME

David Samuel

David Samuel, RKR Epsilon & Prediction Machines

Compere

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.

Buttonlinkedin

RETAIL BANKING

09:30

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.

Buttonlinkedin

09:55

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.

Buttontwitter Buttonlinkedin

FINANCIAL MARKETS

10:20

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.

Buttonlinkedin

10:45

COFFEE

11:25

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.

Buttonlinkedin

11:50

Sujit Khanna

Sujit Khanna, Edelweiss Financial Services

Price Returns Prediction using Recurrent Neural Networks with Long Short Term Memory (LSTM) Units

Price Returns Prediction Using Recurrent Neural Networks With Long Short Term Memory (LSTM) Units

Traditionally RNNs have been used for text classification and sentiment analysis; in this paper, we extend the idea to price returns prediction. The primary objective of the models will be to accurately predict 5-minute price returns based on multiple price based features. In our network, we use stacked LSTMs instead of MLP architecture. LSTM units are preferred as its architecture helps in solving the vanishing gradient problem and enables in modeling the long-term dependency which is persistent in financial price series data.

Sujit works at Edelweiss securities as a Quantitative Researcher in the Algorithmic Trading Desk where he uses a host of quantitative techniques to create Alpha strategies. Sujit’s work majorly focuses on analyzing and applying ML algorithms for price prediction and environment modeling, which includes creating feature selection and dimensionality reduction models like RFE, Lasso, t-SNE, and PCA. Over the last couple of years, he has focused his attention on using Deep Learning Algorithms for Price predictions at Mid and High-Frequency intervals. Such algorithms included variants of LSTM RNNs, Deep generative Models, Adversarial, and Evolutionary algorithms. Prior to Edelweiss securities, Sujit was working with Morgan Stanley in the Quantitative and Derivatives Strategies team. Sujit has bachelor’s degree in Electronics and Telecommunication engineering from Mumbai University and an MBA from NMIMS.

Buttontwitter Buttonlinkedin

12:15

Joint Q&A with Edelweiss & HSBC

Sujit Khanna

Sujit Khanna, Edelweiss Financial Services

Panelist

Price Returns Prediction Using Recurrent Neural Networks With Long Short Term Memory (LSTM) Units

Traditionally RNNs have been used for text classification and sentiment analysis; in this paper, we extend the idea to price returns prediction. The primary objective of the models will be to accurately predict 5-minute price returns based on multiple price based features. In our network, we use stacked LSTMs instead of MLP architecture. LSTM units are preferred as its architecture helps in solving the vanishing gradient problem and enables in modeling the long-term dependency which is persistent in financial price series data.

Sujit works at Edelweiss securities as a Quantitative Researcher in the Algorithmic Trading Desk where he uses a host of quantitative techniques to create Alpha strategies. Sujit’s work majorly focuses on analyzing and applying ML algorithms for price prediction and environment modeling, which includes creating feature selection and dimensionality reduction models like RFE, Lasso, t-SNE, and PCA. Over the last couple of years, he has focused his attention on using Deep Learning Algorithms for Price predictions at Mid and High-Frequency intervals. Such algorithms included variants of LSTM RNNs, Deep generative Models, Adversarial, and Evolutionary algorithms. Prior to Edelweiss securities, Sujit was working with Morgan Stanley in the Quantitative and Derivatives Strategies team. Sujit has bachelor’s degree in Electronics and Telecommunication engineering from Mumbai University and an MBA from NMIMS.

Buttontwitter Buttonlinkedin

Sonam Srivastava

Sonam Srivastava, HSBC

Panelist

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.

Buttonlinkedin

12:40

LUNCH

13:45

Alexey Pogrebnyak

Alexey Pogrebnyak, Finstar

Predictive Scoring Models

Despite the fact that the topic of predictive scoring models building is often considered in articles and it was written not one book about it, many things remain out of sight. An attempt to consider some of interesting questions will be made in the presentation. It will be practical ways which help to answer on such questions as: how to estimate the maximum Gini before the start of the model building; how we could add non-maturing loans in likelihood error function; one additional way how we could estimate overfitting and how we can understand need we separate Fraud Prevention Model or not.

Alexey Pogrebnyak is a head of Decision Science department in financial startup Finstar which was launched in APAC two years ago. Alexey graduated from Moscow State University, with a specialization in high energy physics.

Buttonlinkedin

14:05

Joel Guglietta

Joel Guglietta, Graticule

ML for Financial Markets: A Tool, Not a Magic Recipe

The usage of machine learning techniques for the prediction of financial time series has been used investigated for a (very) long time. Both in the academic and fund management words, generative methods such as Switching Autoregressive Hidden Markov and changepoint models are generally found to be unsuccessful at predicting daily (and higher frequency) prices from a wide range of asset classes. However, committees of discriminative techniques (such as Support Vector Machines or Relevance Vector Machines and Neural Networks) are found to give some interesting results. It however remains that there is no such thing as a magic recipe and that using machine learning model for financial markets faces enormous challenge due to the very nature of financial markets, i.e. an ecology of learning agents continuously creating information and having different

Joel Guglietta has been working as a quantitative strategist & portfolio manager for hedge funds and investment banks (Brevan Howard, BTIM, Graticule, HSBC) for more than 12 years. His expertise is in quantitative models for asset allocation, portfolio construction & management using a wide range of technics of which machine learning techniques and genetic algorithms.

Buttonlinkedin

INSURTECH

14:25

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.

Buttonlinkedin

TRADING

14:45

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.

Buttonlinkedin

15:05

COFFEE

15:40

Ilija Ilievski

Ilija Ilievski, National University of Singapore

Deep Reinforcement Learning for Optimal Order Placement in a Limit Order Book

Deep Reinforcement Learning for Optimal Order Placement in a Limit Order Book

Financial trading is essentially a search problem. The buy-side agent must find a counterpart sell-side agent willing to trade the financial asset at the set quantity and price. The virtual space where the agents execute their trading actions is called limit-order book. We present a deep reinforcement learning algorithm for optimizing the execution of limit-order actions to find an optimal order placement. The reinforcement learning agent utilizes historical limit-order data to learn to an optimal compromise between fast order completion but with higher costs and slow, riskier order completion but with lower costs. We also give a technological overview of the system and discuss the challenges and potential future work.

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.

Buttonlinkedin

16:05

Lipa Roitman

Lipa Roitman, I Know First

From Forecast to Trading System: Reinforcement Learning vs Supervised Learning

The I Know First forecasting algorithm models the markets as non-stationary chaotic systems with fractal properties. Features: Multi-representation – wide and deep supervised learning, data munging to expose important features. Generalization through explicit regularization. Plurality of forecasts. Principal Component Analysis results in near orthogonal inputs. Genetic adaptable algorithm. Can Reinforcement Learning system learn to trade the market? We will report here our results of using Reinforcement Learning to create a trading system based on the I Know First forecasts.

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.

Buttontwitter Buttonlinkedin

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.

Buttontwitter Buttonlinkedin

16:30

PANEL: How Will the Fintech Sector in Asia by Affected by AI?

Rasmi Ranjan Mohapatra

Rasmi Ranjan Mohapatra, OCBC

Panelist

Rasmi is a former computational biology researcher, software programmer, data scientist transformed to a Technologist. Currently one of key members of OCBC Bank’s FinTech & Innovation Group: The Open Vault, where he tries to filter noise from hype. He enjoys connecting diverse dots of any use case and bring an idea to implementation. In his free time, he likes to play around with Raspberry Pi based Home Automation System. His current areas of interest include: blockchain, chatbots, core banking 2.0 and last mile supply chain management using IoT.

Buttonlinkedin

Varun Mittal

Varun Mittal, EY

Panelist

Varun Mittal is the ASEAN Fintech Lead at EY covering emerging technology and business models in financial services across ASEAN. Previously Varun was the first marketing and sales employee at helloPay (acquired by ANT Financial, Alibaba Group), led payments for Samsung in ASEAN and developed regional mobile payment solutions at Singtel Group. Prior his work in payments, Mittal worked at Microsoft and Gemalto. He holds an MBA from the National University of Singapore. Varun is also part of founding team of Singapore Fintech Association, working closely with startups, educational institutions, investors and regulators across ASEAN.

Buttontwitter Buttonlinkedin

Gabriel Lundberg

Gabriel Lundberg, SeedPlus

Moderator

Gabriel Lundberg is an operating partner at SeedPlus, a venture capital fund focused on operational involvement where applied machine learning is a fundamental pillar of the investment philosophy. A reformed engineering physicist with graduate studies in adaptive systems, Gabriel previously spent six years at Spotify as a product manager. When not dealing with product strategy or user experience design, he still claims to be able to write code in a pinch.

Buttontwitter Buttonlinkedin

17:00

CONVERSATION & DRINKS

08:30

DOORS OPEN

09:15

WELCOME

STARTUP STAGE

09:30

Jie Fu

Jie Fu, National University of Singapore

Who Says You Can Trust Deep Learning Now?

Who Says You Can Trust Deep Learning Now?

Despite widespread adoption, end-to-end gradient-based deep learning models remain mostly black-boxes. On the other hand, human cognition is believed to be able to integrate the connectionist and symbolic paradigms. With a symbolic component, common sense priors can be built into the system. This can help reduce the learning workload and facilitating common sense reasoning, thus leading to a more transparent and trustable learning system.

Ph.D. candidate in Deep Learning at National University of Singapore. Semi-wild machine learning researcher with his human-friendly big AI dream. He is expected to graduate in May 2017 from National University of Singapore (NUS), and was fortunately under the supervision of Tat-Seng Chua and Huan Xu, also closely working with Jiashi Feng and Kian Hsiang Low. He received a Research Master's degree in Computer Science (First Class Honours) with scholarship from University of Otago, New Zealand, luckily advised by Brendan McCane and Lubica Benuskova. He is interested in machine learning. More specifically, his research is focused on deep learning, probabilistic reasoning, reinforcement learning and neural abstract machines. He is especially excited about reducing the gap between theoretical and practical algorithms in a principled and efficient manner.

Buttontwitter Buttonlinkedin

09:50

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.

Buttontwitter Buttonlinkedin

10:10

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.

Buttonlinkedin

10:30

Jackie Tan

Jackie Tan, fundMyLife

Convolutional Neural Networks

Connecting Customers and Financial Advisers using Deep Learning

The customer engagement process in insurance has been largely unchanged for a very long time. Despite the gradual transition from offline to online platforms by insurance companies, there remains plenty of opportunity to tackle customer engagement and segmentation process. fundMyLife is transforming these processes using deep learning, empowering customers to engage advisers online anonymously. For this talk, we will be presenting how we apply ML algorithms in our chat platform to parse financial planning questions. We employ a metaclassifier which comprises of a convolutional neural network to read plain text, classifying both users and the topics they ask on our platform to optimize the matching between advisers and users.

Jackie is the co-founder and CEO of fundMyLife, a platform that connects users to the right financial advisers based on their financial planning questions. His PhD in Nanyang Technological University involved 3D model reconstruction of nanoscale biomolecules using 2D images via k-means clustering and maximum likelihood models. Jackie of all trades, he has published papers on game theory and devised an algorithm that predicts drug sensitivity in cancer patients. He is also a prize-winning entrepreneur who built app solutions for organizations such as P&G, QuintilesIMS, and National Design Council Singapore.

Buttonlinkedin

Wesley Goi

Wesley Goi, fundMyLife

Joint Presentation

Connecting Customers and Financial Advisers using Deep Learning

The customer engagement process in insurance has been largely unchanged for a very long time. Despite the gradual transition from offline to online platforms by insurance companies, there remains plenty of opportunity to tackle customer engagement and segmentation process. fundMyLife is transforming these processes using deep learning, empowering customers to engage advisers online anonymously. For this talk, we will be presenting how we apply ML algorithms in our chat platform to parse financial planning questions. We employ a metaclassifier which comprises of a convolutional neural network to read plain text, classifying both users and the topics they ask on our platform to optimize the matching between advisers and users.

Wesley is formally trained as a data specialist in the National University of Singapore, leveraging big data techniques to decipher the metagenome of ecosystems for significant biological processes. He has contributed packages for big data analytics and spoken on graph database management systems. Wesley also developed ML algorithms in high frequency trading for a fintech startup. A self-taught full-stack developer, he spends his time developing fundMyLife’s web application and performing the underlying data analytics as the CTO of fundMyLife.

Buttonlinkedin

10:50

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.

Buttonlinkedin

11:55

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.

Buttonlinkedin

12:20

LUNCH

ARTIFICIAL INTELLIGENCE

13:30

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

Buttonlinkedin

13:55

David Low

David Low, Pand.ai

Building Smart Chatbots with Deep Learning: From Retrieval-Based to Generative Models

In this talk, David Low will be sharing various approaches of building an Artificial Intelligence Chatbot with Deep Learning techniques. Word embedding, Long short-term memory (LSTM) and Sequence-to-Sequence models will be introduced. At the same time, he will present a comparison between the approaches and share a few tips on training deep neural nets optimally. He will also talk about the challenges to build, launch and maintain a chatbot, especially in the financial service industry.

David Low is currently the Co-founder and Chief Data Scientist at Pand.ai. He represented Singapore and National University of Singapore (NUS) in Data Science Game'16 at France and clinched top spot among Asia and America teams. Recently David has been invited as a guest lecturer by NUS to conduct masterclasses on applied Machine Learning and Deep Learning topics. Prior to Pand.ai, he was a Data Scientist with Infocomm Development Authority (IDA) of Singapore. As a pastime activity, he competed on Kaggle and achieved Top 0.2% worldwide ranking.

Buttonlinkedin

14:20

PANEL: How will Artificial Intelligence impact Financial Services?

Chetna Goyal

Chetna Goyal, NTU

Panelist

I want to highlight the impact digitization is having on globalization and on the benefits that can be achieved through improved reference data, analytics and application of machine intelligence in developing more efficient ecosystems that create new opportunities. My personal research is a focused study on electroencephalogram (EEG) signals which aims to help paralytic patients through the development of a Brain Computer Interface (BCI) and exploration of stimulus recognition and mapping by the application of deep neural networks, genetic algorithms and feature extraction through signal processing techniques. Chetna graduated from Nanyang Technological University in August 2015 with a major in Computer Science Engineering. She has been pursuing her research interest in collaboration with Professor Kwoh at Nanyang Technological University. Her work at BlackRock and Bank of America Merrill Lynch have given her an insight into the financial world and she believes that the impact intelligent machines can have on the markets are endless. She has also been selected as student ambassador by Microsoft and Mozilla Firefox, and has also won numerous hackathons in the region.

Buttonlinkedin

Sujit Khanna

Sujit Khanna, Edelweiss Financial Services

Panelist

Price Returns Prediction Using Recurrent Neural Networks With Long Short Term Memory (LSTM) Units

Traditionally RNNs have been used for text classification and sentiment analysis; in this paper, we extend the idea to price returns prediction. The primary objective of the models will be to accurately predict 5-minute price returns based on multiple price based features. In our network, we use stacked LSTMs instead of MLP architecture. LSTM units are preferred as its architecture helps in solving the vanishing gradient problem and enables in modeling the long-term dependency which is persistent in financial price series data.

Sujit works at Edelweiss securities as a Quantitative Researcher in the Algorithmic Trading Desk where he uses a host of quantitative techniques to create Alpha strategies. Sujit’s work majorly focuses on analyzing and applying ML algorithms for price prediction and environment modeling, which includes creating feature selection and dimensionality reduction models like RFE, Lasso, t-SNE, and PCA. Over the last couple of years, he has focused his attention on using Deep Learning Algorithms for Price predictions at Mid and High-Frequency intervals. Such algorithms included variants of LSTM RNNs, Deep generative Models, Adversarial, and Evolutionary algorithms. Prior to Edelweiss securities, Sujit was working with Morgan Stanley in the Quantitative and Derivatives Strategies team. Sujit has bachelor’s degree in Electronics and Telecommunication engineering from Mumbai University and an MBA from NMIMS.

Buttontwitter Buttonlinkedin

Richard Waddington

Richard Waddington, Sherpa Funds Technology

Panelist

Richard is the CEO of Sherpa Funds Technology, a Singapore based company that provides Asset Managers with Intelligence Augmentation (I.A.) software and decision frameworks, which result in better returns, more consistent portfolio characteristics and more efficient workflow. Previously Richard worked in London and New York on the sell-side as a derivatives trader and trading business head, and ran a financial services-focused technology consultancy in Tokyo. Richard studied Physics and Engineering at Cambridge University, and is a keen endurance sports participant.

Buttontwitter Buttonlinkedin

Julian Low

Julian Low, Vertex Ventures

Moderator

Julian is an Executive Director (Investment) at Vertex Ventures SE Asia & India - part of a global venture fund network, under the auspices of Vertex Holdings. Vertex Holdings is a member of Temasek Holdings and focuses on venture capital investment opportunities in the information technology and healthcare markets and has a global network that includes China, India, Israel, Singapore, Taiwan and the US. Prior to Vertex, Julian co-founded which was acquired by Zendesk (NYSE: ZEN). He also spearheaded investments at Red Dot Ventures, a seed stage incubator as Entrepreneur-in-Residence, and managed Paywhere.com which was acquired by Philippine Telco PLDT (NYSE: PHI). Julian graduated from the University of Singapore with a BSc in Biomedical Sciences.

Buttontwitter

15:00

END OF SUMMIT

Connect

Be Sociable

  • Twitter
  • Facebook
  • Linkedin
  • Youtube
  • Flickr
  • Lanyrd
  • Instagram
  • Google plus
  • Medium