Deep Learning in Finance Summit schedule

Petros Dellaportas is professor of statistical science at the University College London and faculty fellow of Alan Turing Institute, the UK's national centre for data science. His research area is Bayesian computational statistics with applications in finance. Some of his current research projects include feasible forecasting with Gaussian processes, modelling and forecasting jumps in multiple financial time series, high frequency trading algorithms and portfolio construction with macro views.

Trends for AI in Investments

According to World Economic Forum, 76% of banking CXOs agree that adopting AI will be critical to their organisation’s ability to differentiate in the market. In the meantime, we have seen 48% CAGR in AI investment through 2021; while the global AI investment has exceeded $50 billion influencing more than $1 trillion market up until today. Unlike past 'AI Springs', the science and practice of AI appears poised to continue an unprecedented multi-decade run of advancement. Key financial services opportunities enabled by AI ranging from conservative improvements to bold bets on new capabilities.

Dr Yuanyuan Liu joined AIG in 2013, and is currently leading the machine learning division within AIG’s Investments AI department. During the past 7 years, he has initiated and led multiple global projects such as SME loss-risk analysis, client lifetime value modelling, and opportunity map, etc. Most recently, he is working on AIG’s innovative R&D projects to apply edge-cutting deep learning algorithms in insurance and investment, using generative model, sequential model, and reinforcement learning. Yuanyuan’s team has published a series of papers in NeurIPS, ICML, AAAI, ICASSP for granular and accurate insurance pricing, equity volatility forecasting, efficient multi-mode data samplings etc. Yuanyuan graduated from the University of Oxford with a DPhil in Statistical Machine Learning and a MSc in Applied Statistics. Prior to that, he studied a Mathematics with Statistics major in the University of Bristol.

Deep Portfolio Theory

By applying deep learning (DL) to classic portfolio optimisation, we show how to capture (or ‘learn’) non-linear features which are invisible to classic portfolio theory. We develop a self-contained four step routine of encode, calibrate, validate, and verify to improve ex-post performance in index tracking and benchmark outperformance.

Jan Hendrik Witte is a numerical analyst who has developed a number of new numerical algorithms in the area of optimal stochastic control. Since leaving academia, Witte has been working as an FX quant in finance. Witte is generally interested in the areas of numerical mathematical finance, systematic trading, and portfolio optimisation. Together with GreyMaths, Witte is building deep learning technologies for the use in trading and investing.

Pushing the Limits of Recurrent Neural Networks

In predicting the movement of prices of correlated securities there are two vexing questions: reliability of predictions and model tuning including the number of layers to use in a deep learning model. We take a deeper dive into how to output only confident predictions in a dynamic fashion, and how to dynamically allocate the number of layers in each time and sequence. The results are discussed on financial market data from an investment firm. The audience will learn about state-of-the-art models and techniques, and we will share a bag-of-tricks to use.

Diego Klabjan is a professor at Northwestern University, Department of Industrial Engineering and Management Sciences. He is also Founding Director, Master of Science in Analytics, and the Deep Learning Lab. His expertise is focused on data science and deep learning with a concentration in finance, insurance, and healthcare. Professor Klabjan has led projects with large companies such as The Chicago Mercantile Exchange Group, Intel, General Motors and many other, and he is also assisting numerous start-ups with their analytics needs. He is also a founder of Opex Analytics.

Artificial Intelligence and Deep Learning for Decision Making with Large-scale Transactional Data

In this talk, I shall look at AI solutions over another type of signals, transactional data generated from our social and commercial world. A difference is that transactional data is usually heterogeneous, coming from diverse sources, and categorical in nature (representing attributes such as a person’s gender, marital status, hometown, career, or the types of movies they like). The correlations of those attributes are not known a priori. In MediaGamma, we have developed an audience decision engine (ADE) that can teach machines to make various predictions and then subsequently make optimal actions upon those predictions over terabytes transaction data. My talk will be focused on the ADE as an example to illustrate 1) how supervised and unsupervised learning can be developed to decipher the correlations among those attributes and use them to make various accurate predictions and forecasts, 2) how the knowledge can be transferred among those predictive tasks, and 3) how machines can pick up a signal and take an optimal decision over time against a pre-specific utility.

Dr. Jun Wang is Reader (Associated Professor) in Computer Science, University College London, and Director of MSc Web Science and Big Data Analytics. He is also Co-founder and Chief Scientist in MediaGamma, a UCL spin-out focusing on AI for intelligent audience decision making. His main research interests are in the areas of intelligent information systems, covering information retrieval, data mining, online advertising and deep learning. His team won the first global real-time bidding algorithm contest with 80+ participants worldwide. Jun has published over 100 research papers and is a winner of multiple “Best Paper” awards in information retrieval and data mining. He was a recipient of the Beyond Search – Semantic Computing and Internet Economics award sponsored by Microsoft Research and also received Yahoo! FREP Faculty award. MediaGamma has received the UCLB One-to- Watch award 2016.

Mariano is the Managing Partner of Santander InnoVentures, a FinTech VC fund fully-owned by Grupo Santander. The fund invests capital and resources in start-ups that increase the value proposition for Santander customers globally, while creating value for the companies it invests in. Mariano was formerly the Associate Principal in the CIB and Risk Management practices for McKinsey & Co. in Madrid and New York, and a Research Technology Associate at global macro hedge fund Bridgewater Associates. He holds a BA in Computer Science and Philosophy from New York University, and conducted doctoral studies in Artificial Intelligence at Universitat Politecnica de Catalunya.

Basile Mayeur, Co-Founder and Data Scientist at Walnut Algorithms is a mathematician and data scientist by training. He received a Master Degree in Applied Mathematics and Computer Science from ENSIMAG in Grenoble, as well as a Master of Research in Machine Learning from Paris Sud University. He conducted doctoral research at CNRS-INRIA combining Deep Learning techniques to Reinforcement Learning. Today, Basile oversees Walnut Algorithms’ research efforts in implementing machine learning into systematic trading strategies together with Lionel Limery, Head of Trading. He was previously a technology consultant with Eggers Consulting.

Implementing Deep Learning Models: a quick overview

During this talk I will provide a quick introduction to Deep Learning, starting from its theoretical foundations to simple implementations. Then, I will focus on some languages and frameworks for developing commercial projects, with a particular emphasis on Tensorflow and Keras. In particular, I will explain how these frameworks work by simple examples and fragments of codes. To conclude, some tips for feeding deep networks, and evaluate its performance are presented. Therefore, beyond to present a general overview on Deep Learning and its mathematical structure, this talk aims to indicate the first steps for build up a (simple) model in Python.

Marco Alberto Javarone is a Senior Researcher at nChain LTD and Associate Researcher at the University of Kent. He holds two PhDs, one in Computer Engineering and one in Applied Mathematics. His research activity is focused on Statistical Physics and Information Theory with applications to Machine Learning (and Deep Learning), Blockchain Technologies, Evolutionary Game Theory and Complex Networks. Recently, he published a book on computational models for Evolutionary Game Theory (Springer). Passionate for Startups, he works as consultant in the field of Machine Learning and Data Science.

Ravi is a longtime editor at political, cultural and business publications with a global view. He is a writer and speaker on innovation, entrepreneurship, global trends in business and work, identity and technology currently working the technology, media and telecoms editor for the Financial Times.

Fin Bots - For Accounting, Banking and More

Bots are a major paradigm shift that is changing the way we use computers. Software becomes easier, simpler and more natural to use with a bot interface. This will particularly impact financial services - fin bots will transform financial services like accounting, banking, payroll, payments etc. Some of the leading bots in financial services have been built using the Gupshup bot platform. With its extensive experience working with finserv companies in US, Europe and Asia, the speaker from Gupshup will share insights on how bots are transforming financial services and how you can do the same.

Christine is responsible for the account management of the existing portfolio and growth of Gupshup in London. Her interest in bots stemmed from a project she worked on during her Masters' Degree which lead to a year long project building an early stage app utilisling conversational interfaces.

She has experience scaling people operations within hyper-growth start-ups in Europe, specifically within product & engineering teams. Christine grew up in London and has worked in clinical (hospital) and corporate sectors (banking) - which instilled her passion for understanding the complexities of diverse working environments. She has a Bachelors of Psychology and a Masters in Organisational Behaviour.

Soft-Skills and Individualisation by Machines: How Artificial Intelligence Is Changing the Game in Robo-Advice

Robo-advice is becoming an increasingly popular way to invest money. However, most robo-advisors are still neither real advisors nor do they unfold their full potential of deep learning capabilities that users would expect from a 'robo'. VisualVest is a German robo-advisor founded by Union Investment in 2015 and is looking for a way to include AI in its solution. In this talk, I will give an overview of how to concept and deploy AI in robo-advice. This will include use cases of deep learning in our solution, the crucial role of design for commercial success and the obstacles that occur for a fintech company. I will demonstrate how we address legal boundaries that limit the usage of deep neural networks for financial recommendations, how we determine proper algorithms for deep learning in our solution and what is important for a successful collaboration between teams of deep learning specialists, designers, finance experts and developers.

Alexander Del Toro Barba is a design and artificial intelligence evangelist. He has 7 years experience in global e-commerce for software companies before he joined Google Inc. in Dublin as part of the global marketing team for Google Apps for Work. In 2015 he relocated to Frankfurt and is now Head of Product & Marketing at VisualVest. Alex is also a PHD candidate in Economics of Deep Learning at the University of Münster and continues to be a pro-active supporter of sustainability, diversity and equality in businesses.

Automation and robots in finance: the good the bad and the ugly.

Automation is driving a rise of Robots that for now are relatively “simple machines” but will evolve quickly. This presentation is about the impacts these robots will have in the sphere of finance and touching upon the potential impact of robots reducing the size of financial work force. Ernest a Chatbot that is a Private Banker for all powered by Artificially Intelligence will be shown as one example of trends to come. The Good: What will be the productivity increase or at least the improvements to our lives? The Bad: In what way could this technology be used to actually make our lives worse? The Ugly: Will jobs go? Which ones?

Niall has a Electronic Engineering Masters from Nottingham University and an MBA from Oxford University. His career spans Technology Finance and Entrepreneurship. He has worked for big brands such as Barclays Global Investors and BlackRock and in the Consulting sphere for Deloitte and Alpha Financial Markets Consulting.

One Algorithm to Secure Them All

AimBrain aims to secure consumer mobile devices and applications by developing algorithms that learn the user’s biometric model. The core technology is fuelled by the recent advances in deep learning. In the talk we’ll discuss what are the key parts of deep learning that enable us to build our platform. One key ingredient is the ability of deep nets to learn features and form representations while being trained under objectives that fit the task, rather than fitting the task to the algorithm. Composing appropriate objectives allows us to learn global latent factors that are well behaved locally, alleviating the database imbalances. The effectiveness of deep learning permits us to unify AimBrain’s products under a single learning paradigm which in turn allows us to focus our research and simplify production deployments.

Alesis Novik is the CTO and co-founder of AimBrain. With the vision to provide the market with a smart and transparent biometric experience, Alesis has helped to guide AimBrain to its unique position as the world's only multi-module, biometric security solution. Alesis holds degrees from Vilnius University and the University of Edinburgh. During the course of his academic career, Alesis participated in Google’s Summer of Code Program. More recently, Alesis has spent time at CERN and at Level E Capital. Additionally, Alesis has completed three years of PhD work.

Deep Learning in Trading

There are many potential applications of deep learning in financial services, Alpaca has addressed some of them and has been working specifically on the deep learning in trading. In this talk, we will talk about our technique and insights found in our experiments and productions.

Hitoshi Harada has worked in the database industry and community for ten years before Alpaca, and has a large amount of experience in data science, machine learning and image processing for industrial applications. He recently talked at NVIDIA's GTC 2016 about Alpaca's deep learning technology. GitHub: umitanuki

The Use of Blockchain in Finance

Blockchain has the potential to disrupt multiple industries and make processes, more democratic, secure, transparent, and efficient. Entrepreneurs, startup companies’ investors, global organizations and governments have all identified Blockchain as a disruptive opportunity to change the current paradigm.

Banks are not characterized for being neither agile nor fast when embracing new technologies due to their legacy system. However, times are changing and new technologies are being offered. In order to adapt to new times and shifting to more scalable systems and interconnected world, banks will have to adapt to new technologies and embrace changes easily. But, is this shift feasible?

Josep Grau is a digital innovation project manager on the strategic development and innovation department in CaixaBank. Josep has a wide knowledge base and experience on innovation in banking and financial institutions. As a Fintech expert, he has experience in payments, lending, security and others, but particularly one of his major areas of interest is Blockchain and any of its areas of impact.

Josep did his bachelor on electrical engineering and a master in telecommunication engineering in Polytechnic University of Catalonia. Due to his motivation on having a positive impact on the world and willing to contribute to create sustainable value for the society, Josep moved to Sweden in order to do a Master on Entrepreneurship and Innovation Management by KTH Royal Institute in Stockholm, where he wrote his master thesis on “Strategic innovation in financial sector: Blockchain and the case of Spanish banks”

How Deep Learning can Revolutionise Document Management in the Finance and Real Estate Industry

LEVERTON uses Deep Learning techniques to solve problems of Optical Character Recognition (OCR) and Information Extraction, this helps to turn unstructured data into structured data. The technologies are being applied to corporate documents in order to extract cash-flow and legally relevant data points to support easier and faster decision making (in more than 20 languages). Structured data can then be migrated to various applications, including ERP systems (SAP).

Dan is Product Management Director at LEVERTON. Having a degree in computer science and with his strong passion for product management – Dan revolutionizes how individuals work with data and documents.

Before LEVERTON, Dan spent six years with ABBYY Europe in Munich. As a Director of Service, Dan successfully helped in building the European market and was involved in > 20 international data extraction projects.

Research to Products: Machine & Human Intelligence in Finance

Artificial intelligence and deep learning in finance has gained traction in the past years. This talk will cover our work in the field of machine learning applied to distress events, networks and news. We look into machine learning for systemic risk identification and distress signalling by measuring excessive increases in micro and macro-financial imbalances, network analytics to account for the interconnectedness of financial markets and deep learning textual data for event extraction with a focus on bank distress in the news.

Peter is an Associate Professor of Economics at Hanken School of Economics (Helsinki, Finland), and Director of RiskLab Finland. Currently, he is a research associate with the Systemic Risk Center at London School of Economics (LSE) and IWH Halle Institute for Economic, as well as a board member of the IEEE Analytics and Risk Technical Committee and the IEEE Computational Finance and Economics Technical Committee. He is also an Associate Editor of Journal of Network Theory in Finance and Intelligent Systems in Accounting, Finance & Management. Peter received his PhD (Econ) from the Department of IT, Åbo Akademi University, and has also studied at LSE, Stockholm School of Economics and Stockholm University. He has been a Financial Stability Expert and advised several central banks, such as the European Central Bank. Peter’s book Mapping Financial Stability was published by Springer in May 2014.