• 08:30

    REGISTRATION & LIGHT BREAKFAST

  • 09:15

    WELCOME

  • THE CHANGING FINANCIAL LANDSCAPE

  • 09:30
    Harshwardhan Prasad

    Recent Developments in Deep Learning in Finance

    Harshwardhan Prasad - VP - Quant Analytics - Morgan Stanley

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    Recent Developments in Deep Learning in Finance

    Harsh currently works with Morgan Stanley in Quant Analytics Group. He started his career as a programmer focussed on developing data driven algos in the areas of speech recognition, image processing and bioinformatics. He then moved to financial risk management and over the last 12 years has worked in various roles through the life cycle of models. In these roles, he has been continuously enthusiastic to applying machine learning in problems related to behavioural assumptions, data quality, recommender systems, model benchmarking and text analytics. His current role requires him reviewing all Machine Learning models used by the firm and providing direction to shaping AIML governance framework and strategy. He is also a visiting lecturer with universities and training institutions.

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  • 09:50
    Roshini Johri

    Evolution of AI in Finance: The Journey From POC to Deployment

    Roshini Johri - Senior Data Scientist - HSBC

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    Evolution of AI in Finance: The Journey From POC to Deployment

    Abstract: Financial industry as compared to other industries is very young when it comes to applying sophisticated machine learning techniques to their data problems. There are a huge number of challenges faced in global organisations that come in the form of technical debt, legacy code, outdated infrastructure, bad data practices, regulatory restrictions and so on. In such an environment, running a data driven machine learning algorithm that often comes with black box methods can prove to be really challenging. This talk will address some of the various challenges faced in the industry including explainability and how some of that can be mitigated more effectively. It will also address the distinction between roles for data scientists, machine learning engineers as well as analysts and how a more collaborative effort along the various stages of the pipeline will help drive projects from poc levels to deployment when running large scale projects in a global team.

    Roshini has a background in AI and electronics from Edinburgh university. She has more than eight years of experience in applying machine learning techniques to design scalable robust solutions in the fields of e-commerce, travel and finance. She has worked with modelling user behaviour, preditive models, recommendation systems, generative adversarial models with deep learning frameworks and is currently working on models in finance to assess risk. She is very interested in understanding how AI techniques can be applied in various industries to make them more efficient and accurate. She is also very passionate about encouraging more women to enter and lead in this field and runs the London chapter for women in machine learning and data science. In her free time she dabbles with creating artwork with neural style transfer and travel photography to show how easily AI can be integrated with day to day activities and enhance our creativity.

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  • 10:10

    Technological Transformation in Capital Markets

  • 10:30

    COFFEE

  • DEEP LEARNING TOOLS & TECHNIQUES IN FINANCE

  • 11:10
    Peter Jackson

    The Importance of Data Science to Hold Off the AI Winter in FS

    Peter Jackson - Director - Group Data Sciences - Legal & General

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    The Importance of Data Science to Hold Off the AI Winter in FS

    Data should sit at the heart of every organisation, it should drive decision making and transformation. The new buzz phrase after Big Data may be AI and commentators are already talking about the ‘AI winter’. Peter will explore how genuine data science can keep the chill at bay and create a long summer of data driven transformation.

    Peter is Director, Group Data Sciences at Legal and General. Previously Peter was Chief Data Officer at Southern Water and prior to that Head of Data at The Pensions Regulator (TPR), which regulates the pensions and automatic enrolment in the UK. Before joining TPR Peter spent 17 years providing data strategy consultancy across the not for profit sector, financial services and FMCG, working with large multi-national organisations and blue chip brands. Peter is a specialist in Data Strategy, Data Technologies, Master Data Management Strategies, Data Governance Frameworks, GDPR and Data Science Strategies. Peter is the co-author of ‘The Chief Data Officer’s Playbook’ published by Facet November 2017, and ‘Data-Driven Business Transformation’ published in March 2019 by Wiley. Peter is an international speaker on Data, Innovation and business transformation.

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  • 11:30
    Angela Johnson

    AI in Banking on the Cloud

    Angela Johnson - Head of Risk - Lloyd's Banking Group

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    AI in Banking on the Cloud

    Angela will talk about the drive for Financial Services organisations to move to the cloud and how this may advantage technologies such as AI, Machine Learning, Big Data and Analytics. Angela will talk about how to set up a vision and strategy and drive through implementation.

    Prior to working at Lloyds, Angela worked At Deutsche Bank in the Cloud, Anti-Financial Crime Analytics, Data Quality and Finance Risk and Regulatory Reporting change and transformation teams. Before joining Deutsche Bank, Angela worked as a Senior Manager at EY across Asia Pacific and EMEIA within the Financial Services Organisation focusing on Corporate Banking and Capital Markets transformation and advisory programmes. Angela started her career at IBM in Australia working as a developer, tester, designer and then business analyst and project manager. Angela has an honours degree in Mechanical Engineering (Mechatronics / Robotics) and Computer Science from the University of Melbourne, Australia.

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  • 11:50
    Valeria Cortez Vaca Diez

    Detecting Discriminatory Outcomes in Classification Models

    Valeria Cortez Vaca Diez - Data Scientist - Lloyd's Banking Group

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    Detecting Discriminatory Outcomes in Classification Models

    Lending, policing, and hiring are some of the many areas where Machine Learning can harm disproportionately the most vulnerable groups in our society. This can lead to discrimination and long-lasting negative impact in society. It is therefore crucial to understand unfair treatment in AI to prevent automated discrimination at scale. The fundamental techniques to analyze and detect bias in Machine Learning decision can be explained through simple metrics applied to model outcomes. The aim of this presentation is to pass this knowledge to empower ML practitioners to challenge how Machine Learning is implemented.

    Valeria is a Data Scientist at Lloyds Banking Group, specializing in the design and development of scalable Machine Learning solutions for different business areas of LBG and their customers. Her current work at LBG focuses on building tools and processes to detect and mitigate bias in ML models. Before joining LBG, Valeria started her career in Cambridge researching on the economics of privacy at Microsoft Research and working for TAB, a Fintech startup. Valeria is a strong advocate of ethics and responsibility in AI as well as bringing more diversity into tech teams.

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  • 12:10

    LUNCH

  • CUSTOMER FOCUS

  • 13:00
    Marsal Gavalda

    Developing ML-Driven Customer-Facing Product Features

    Marsal Gavalda - Head of Machine Learning - Square

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    Developing ML-driven Customer-Facing Product Features

    As Machine Learning becomes a core component of any forward-looking company, how can we weave ML-driven functionality into the products and services we offer? This talk will explain the methodology we follow at Square when developing ML-driven customer-facing product features, which is based on paying close attention to four key and interdependent aspects: Design, Modeling, Engineering, and Analytics. Design is concerned about the usefulness and remarkability of the feature, and thus cares about the overall functionality, ease of use, and aesthetics of the experience. Modeling is concerned about the accuracy of the ML model, and thus cares about the training data, the features and performance of the model, and —crucially for a customer-facing product— how the application behaves in the face of the mistakes the model will inevitably make (false positives, false negatives, lack of predictions above a certain confidence). Engineering in turn is concerned about running the ML model at scale, and thus cares about the latency, throughput, and robustness of the inferencing service. Finally, Analytics is concerned about the adoption of the feature, and thus cares about the instrumentation to capture detailed usage, the definition of success metrics and dashboards, and the collection of feedback in a manner that the ML model can learn from, and thus keep improving over time. When all these aspects align, we can create remarkable ML-powered experiences that delight our customers.

    Marsal Gavalda is a senior R&D executive with deep expertise in speech, language, and machine learning technologies. Marsal currently heads the Commerce Platform Machine Learning team at Square, where he applies machine learning and automation for Square's overarching purpose of economic empowerment. Marsal holds a PhD in Language Technologies and a MS in Computational Linguistics, both from Carnegie Mellon University, and a BS in Computer Science from BarcelonaTech. Marsal is the author of over thirty technical and literary publications, thirteen issued patents, and is fluent in six languages.

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  • 13:20
    Eric Charton

    Using Deep Learning with Word Embeddings to improve Customer Satisfaction

    Eric Charton - Senior AI Director - National Bank of Canada

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    Using Deep Learning with Word Embeddings to improve Customer Satisfaction

    Understanding customer satisfaction in retail banking requires exploring and comprehending multiple sources of feedback, such as emails, social networks reviews, web feedback, bot interactions, as well as speech-to-text transcripts collected from call centers. Since such a vast amount of textual data can be difficult to leverage with traditional text mining techniques, deep learning and word embeddings can be used to automatically classify and label feedback, and then deeply analyze and understand their content. In this communication we explain how we leverage all those AI techniques to get an in-depth understanding of the opinions and needs of National Bank’s retail customers. We also show how we improve the performance levels of those AI tools using in-house algorithms and data resources to improve the overall capacity of natural language understanding.

    Eric Charton hold a Master in machine learning applied to voice recognition, and a Ph.D. in machine learning applied to Information extraction and natural language generation. He worked as scientist and research project coordinator in academic context in Europe (University of Avignon) and North America (CRIM, École Polytechnique de Montréal) before becoming head of search engine research and development at Yellow Pages Canada. Since March 2018, he is Senior AI Director at National Bank of Canada.

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  • PREDICTING TRENDS

  • 13:40
    Yunus Saatchi

    Deep Reinforcement Learning for Market Making

    Yunus Saatchi - Senior Machine Learning Scientist - Uber AI Labs

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    Deep Reinforcement Learning for Market Making

    A market maker or liquidity provider is a company or an individual that quotes both a buy and a sell price in a financial instrument, commodity or service held in inventory, hoping to make a profit on the bid-offer spread. A successful market-maker therefore has to have a very good model of both sides of the marketplace in order to be profitable. Reinforcement learning (RL) is very well suited to this task because it forces these models to be good for the purposes of profitability. In this talk I will explore different applications of RL in the act of marking making in traditional exchanges, and in more novel, emerging spatio-temporal two-sided marketplaces such as those observed in ride-sharing markets.

    Yunus did his PhD at the Machine Learning lab at the University of Cambridge, under the supervision of Carl Rasmussen and Zoubin Ghahramani, now chief scientist at Uber! His PhD was in scalable methods for a brand new type of Gaussian process known as the structured Gaussian process: a Gaussian process with a covariance structure chosen to make it scalable. After getting addicted to getting slow but awesome code run faster during his PhD, high-frequency trading seemed like a natural choice for Yunus, so he spent two years at Tower Research Capital, a New York-based quantitative hedge fund. He then switched gears (and countries) and joined one of the relatively older AI research labs in the Bay Area, namely Vicarious for another two years, where he worked on deep generative models and scalable sum-product networks. Getting the urge to apply some deep learning models in the wild, he joined comma.ai, a self-driving car startup in San Francisco, as Chief Machine Learning Officer. There he built an operational, self-driving system purely for the highway and congested highway traffic scenarios. Since then he has been a senior research scientist at Uber AI Labs, where he has implemented Bayesian optimization and reinforcement learning systems at Uber scale. He is also an advisor and investor in several ML startups across the globe.

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  • 14:00
    Tomas Navarro

    How Space-Based Technology can Hedge Financial Strategies

    Tomas Navarro - Space Segment Engineer - European Space Agency

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    How Space-Based Technology can Hedge Financial Strategies

    Artificial Intelligence is on the verge of transforming man-controlled spacecraft into cognitive machines that will soon drastically change the way we used to do space exploration. However, state-of-the-art AI technology is not only used by the European Space Agency (ESA) to improve planetary landing, exoplanet hunting or to increase autonomy in spacecrafts and robots. ESA’s Business Applications and Space Solutions supports FinTech companies to boost their technological capabilities by making use of space assets and technologies, therefore helping to develop breaking-through financial strategies and services. Examples or these include prediction and detection of stock market anomalies using ESA’s most advanced AI-based algorithm for spacecraft anomaly detection or support hedge funds and investment firms mapping energy and metal commodities activity or additionally evaluating credit risk through AI-enhanced geoinformation based on ESA’s Earth Observation Satellites. In this talk we will go through some of the key AI technologies being used by ESA in communications, operations and space exploration and explain how Fintech industry can engage with ESA to make use of them. We will also give an overview of the work performed by some of the brightest Fintech companies already using space technologies, sponsored by ESA’s Business Applications and Space Solutions.

    Tomas Navarro is a Space Segment Engineer at ESA (European Space Agency) working on the Advanced Research in Telecommunications Systems (ARTES) programme. Thomas coordinates Artificial Intelligence activities for Space applications. Before joining ESA, Tomas worked at INMARSAT in satellite operations and as a R&D engineer for satellite & payload optimisation systems, focusing on genetic and machine learning algorithms used in operational geostationary communications satellites. Tomas is also the co-inventor of an Artificial Intelligence-powered Space architecture able to improve response times in systems based on a centralised topology. His invention is planned to be used in Space systems to distribute intelligence across a multi-node network, aiming at optimising resources while reducing spacecraft control time. Tomas works also with ESA Business Applications to understand how to apply this architecture to financial systems in order to reduce financial transaction times and optimise flow of information across different nodes.

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  • PAYMENT SYSTEMS

  • 14:20
    Jessica Lennard

    Putting the Customer at the Heart of AI in Payments

    Jessica Lennard - Senior Director, Global Strategic Data & AI Initiatives - Visa Data Science Lab

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    Putting the Customer at the Heart of AI in Payments

    Payment transactions provide one the largest, richest data sources on the planet. As the world’s leading payments provider, Visa is driving advances in data science and AI for the benefit of customers, the businesses that serve them, and society more broadly. As well as discussing some of the key use cases, this session will also look at what businesses need to do to empower consumers and engender public trust in these new technologies and use cases of data.

    Jessica is a Senior Director of Data and AI Initiatives in Visa’s Global Strategy team. Her work covers data privacy, AI ethics and related policy, regulation and business development. Her previous roles at Visa include Director of External Affairs for Visa’s Data Science Lab, and Head of UK Regulation and Public Affairs. She has over ten years’ corporate affairs experience including lobbying, communications and reputation management. She has worked for political parties, businesses (start-up to FTSE 100), consultancies, trade bodies, think tanks and NGOs. Her particular expertise is highly regulated, technology-driven sectors including digital, data and AI, telecoms, energy and fintech. She was previously a capital markets solicitor at Linklaters, having completed an undergraduate degree in Law from Oxford University and a Masters degree in Political Theory from the LSE.

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  • 14:40

    COFFEE

  • DETECTING FRAUD

  • 15:25
    Maciej Mazur

    Fraud Detection in 2020 - Bad Guys Perspective

    Maciej Mazur - Chief Data Scientist - PGS Software

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    Fraud Detection in 2020 - Bad Guys Perspective

    AI is evolving rapidly these days, and together with it are our fraud detection systems. I want to show you what is the current state of the art approach to fraud detection, how are such systems implemented, and what are key differentiators to look at when choosing a solution for your business (AML, credit card frauds and insurance). Next we will focus on credit card frauds, but not from a payment provider or a bank perspective but a criminal. Learn more on what are the newest technology trends for card frauds, how bad guys build their infrastructure and how they cheat and manipulate your million dollars black boxes that are supposed to keep you safe.

    As Chief Data Scientist at PGS Software, Maciej is the technical lead of the data team and implements ML-based solutions for clients around the globe. In his 10 years of IT-experience, he’s worked for major players like Nokia and HPE, developing complex optimisation algorithms even before the term Data Science was coined.

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  • REGULATION & GOVERNANCE

  • 15:45
    Manuel Proissl

    Towards Algorithmic Assurance of Governing Machine Learning Systems at Scale

    Manuel Proissl - Head of Predictive Analytics in Banking Products - UBS

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    Towards Algorithmic Assurance of Governing Machine Learning Systems at Scale

    Over the past years a vast amount of research and guidelines have been published with the aim to pave the way towards 'governance frameworks' of machine learning systems affecting consumers, particularly around adversarial robustness, model transparency, privacy conservation, algorithmic fairness and ethical principles. This presentation focuses on a set of techniques that have shown potential and presumably practical relevance in financial services. Furthermore, the talk attempts to also shed light on opportunities and challenges of embedding third-party APIs that have been developed/trained by global communities.

    Manuel is currently Head of Predictive Analytics in Banking Products at UBS. Previously, he's been a senior advisor and machine learning cloud platform lead at Ernst & Young, developed numerous AI-driven business solutions for global organizations, and held managing roles in cross-border audit & advisory engagements and leading international research collaborations with contributions to AI research, Cognitive Control Systems and Particle Physics.

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  • 16:05
    Rahul Singhal

    AI-Enabled Data Extraction Using Horizontal AI

    Rahul Singhal - Chief Product Leader - Innodata

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    AI-Enabled Data Extraction Using Horizontal AI

    Data points from complex contracts including ISDAs, GMRA, etc. can be vital in guiding real-time decisions in an ever changing regulatory environment. Innodata’s Chief Product Officer, Rahul Singhal, will demonstrate how to extract the most critical data points across documents and how it will save time and improve productivity for legal, operations, and front-office.

    Rahul Singhal leads product, technology and business development initiatives for Innodata, a data and content transformation company. Prior to joining Innodata, Rahul was Chief Product Officer at Equals 3, an AI marketing platform which won several accolades including Gartner Cool Vendor. Before that, Rahul spent 12 years at IBM, the last three of which he spent leading the product portfolio for the Watson Platform which included a collection of APIs for vision, speech, data and language.

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  • 16:25

    PANEL: What are the Key Considerations for the Future of Implementing AI & Deep Learning into the Financial Sector?

  • Ronan Brennan

    Panellist:

    Ronan Brennan - Strategy and Innovation Manager - Natwest

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    Ronan is a Strategy and Innovation Manager at NatWest, focusing on solutions for Personal Banking customers. He has a deep interest in the development of safe and fair Artificial Intelligence; and has designed model governance policy frameworks for deploying safe, ethical, Artificial Intelligence & Machine Learning tools in customer propositions. Prior to working at NatWest, he studied at the University of Southampton, completing an MSc Dissertation exploring the potential for widespread adoption of AI & ML technologies to impact inequality within and between countries using the UN Sustainable Development Goals framework.

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  • 17:00

    CONVERSATION & DRINKS

  • 08:30

    DOORS OPEN

  • 09:15

    WELCOME

  • START UP SESSION

  • 09:25
    Chandini Jain

    AI for the Buyside: Deep Learning for Factor Research

    Chandini Jain - Founder - Auquan

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    Women in FinTech Summit - AI: Can Machines Truly Make Smarter Investment Decisions Without Any Human Input?

    Quantitative and semi-quant funds have consistently outperformed discretionary funds over the last few years. Does this mean the age of human intuition based investing is over? Can machines truly replace humans? We will explore the case for data driven decision making in investment management, the benefits and pitfalls of fully automated investing and why the human touch is still important. AI in finance, when done right, is not a replacement for human trader but a powerful tool that traders can combine with their own skill to massively augment their decision making and boost profitability.

    AI in Finance Summit - AI for the buyside: Deep Learning for Factor Research

    Problems in the financial markets are different from typical deep learning applications since the emphasis is not on replicating tasks that humans already do well. Humans have no innate ability to solve these problems that involve large data sets and complex data interactions that currently are difficult to specify in a purely economic model. Applying deep learning methods to these problems can detect and exploit interactions in the data that are, at least currently, invisible to any existing financial economic theory.

    We will demonstrate an example of how we can combine the latest statistical techniques with deep learning methods to compute any relationship between thousands of raw indicators and asset prices, no matter how complex and non-linear. These insights can be used to create new empirical representations of fundamental factors which lead to long-term over-performance. This also enables a better understanding of portfolio exposure to different risk factors, allowing asset managers to create high performing, well diversified portfolios. We will compare the performance of these methods to the linear factor models of traditional financial economics and the ad hoc methods of statistical arbitrage and other quantitative asset management techniques.

    Chandini Jain is the CEO and founder of Auquan. She has 7+ years of global experience in finance with Deutsche Bank in Mumbai/New York and as a derivatives trader with Optiver in Chicago and Amsterdam. At Optiver, she traded volatility arbitrage strategies and was involved first hand in making the shift from discretionary to automated trading. Since 2017, she has been working on Auquan, an early stage fintech startup employing new and cutting edge ML and Deep Learning techniques to solve financial prediction problems for hedge funds and asset managers.

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  • 09:40

    Deep Learning for Bankruptcy Prediction

  • 09:55

    Using Deep Learning to Detect and Prevent Financial Fraud

  • TRADING

  • 10:10

    Deep Learning for Cross-Border Finance: International Trade

  • 10:30

    Algorithmic Trading Advancement with Deep Learning

  • 10:50

    COFFEE

  • INVESTMENTS

  • 11:30
    Yuanyuan Liu

    Trends for AI in Investments

    Yuanyuan Liu - Director, Statistical Machine Learning - AIG

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    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.

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  • 11:50
    Dan Philps

    Investment AI: Motivations + Next Gen AI: CL

    Dan Philps - Head - Rothko Investment Strategies

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    Investment AI: Motivations + Next Gen AI: CL

    This presentation addresses the motivations and risks for using AI to drive investment decisions and, secondly, drills down into a next generation AI approach; Continual Learning (CL). Few industries are more ripe for disruption than equities investment management in 2020. Crowded 1990s-era factor quant models are still in demand, while the recent explosion in high-quality data, coupled with the technology to make sense of it, AI, has opened up new alpha opportunities. However, AI comes with its own problems, chief among them outright complexity. Few AI approaches applied to investment, balance inferential-power and complexity well. However, CL may offer an approach. I introduce CL and how it aims to allow machines to accumulate knowledge over time and use this knowledge to make better investment decisions in the future.

    Dan Philps, CFA, is head of Rothko Investment Strategies and is an artificial intelligence (AI) researcher. He has 20 over years of quantitative investment experience. Prior to Rothko, he was a senior portfolio manager at Mondrian Investment Partners. Before 1998, Philps worked at a number of investment banks, specializing in trading and risk models. He has a BSc (Hons) from King’s College London, is a CFA charterholder, a member of CFA Society of the UK, holds a post graduate research role at London University, and is a member of the AAAI

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  • 12:10
    Sonam Srivastava

    Portfolio Management Using Deep Reinforcement Learning

    Sonam Srivastava - Founder - Wright Research

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    Portfolio Management Using Deep Reinforcement Learning

    Prediction using supervised learning algorithms for financial time series modelling is hard and converting predictions into actions requires additional naive layer of logic. In this talk I present a financial-model-free Reinforcement Learning framework to provide a deep machine learning solution to the portfolio management problem. A deep neural network is used for generating signals based on historical price data, these signals are fed to a portfolio memory layer that optimizes transaction costs, the deep reinforcement learning agent trains itself using an exhaustive reward function based on long term and short term market performance. This problem is implemented on the cryptocurrency markets and compared with other well known portfolio management frameworks like trend following, mean reversion and equal weighting scheme. I show that the model outperforms the other frameworks but performance is very much dependent on the overall market trend in the long only setup.

    Sonam Srivastava is a quantitative trading professional more than 8 years of professional experience in systematic portfolio management and quantitative trading. She is a IIT Kanpur graduate with a Masters in Financial Engineering from Worldquant University. She has worked as a Portfolio Manager at Qplum, applying machine learning & artificial intelligence to automate investment decision making. She has also worked at HSBC as a Quant Researcher and Edelweiss as an Algorithmic Trader. She is an avid researcher in the field of Quantitative Finance and a Registered Investment Advisor.

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  • 12:30

    LUNCH

  • PRIVACY & SECURITY

  • 13:30
    Omari S. Felix

    Towards Privacy-Compliant Data Science

    Omari S. Felix - DevOps Engineer - Capital One

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    Forecasting Risk using CICD and Machine Learning

    The software development process does not have a singular formula for success. There are many factors that influence this process such as code management, quality analysis, testing strategies to name a few. Pinpointing the features that correlate to the success of a deployment are subjective to a developer’s personal style of software engineering and testing. I will be presenting a project that implemented a machine learning component to analyze build features and gauge them against a pool of build records from a project and organization perspective.

    Omari S. Felix, a DevOps Engineer at Capital One where he implements DevOps tools and guide teams on utilizing techniques designed to improve their software development and delivery. A graduate from Virginia State University and attended N.C. A&T State University. He has experience in multiple technology spaces include mobile development, automation, and machine learning to name a few.

  • 13:50
    Greg Mason

    The Use of AI to Assist Data Governance.

    Greg Mason - Co-Founder & Head of Technology - Forensic Risk Alliance

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    The use of AI to assist Data Governance.

    The past half dozen years have seen a clear emergence of conflicting global trends affecting data privacy protection. These all place strict limitations on the transfer of data across national borders, with increasingly severe criminal penalties and civil fines for violations, making cross-border eDiscovery particularly challenging. This presentation will outline a ground breaking solution to help address these issues.

    Greg Mason is a founding partner of FRA and head of its eDiscovery and IT divisions. His expertise lies in database architecture and programming, software design, mass data analysis, data mining, and data forensics for the purposes of investigations, disputes and litigation.

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  • 14:10
    Mesut Tastan

    Detecting Manipulated Cryptocurrency Trades

    Mesut Tastan - Senior Lecturer in Finance - Westminster Business School

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    Detecting Manipulated Cryptocurrency Trades

    Cryptocurrencies are new, volatile and unpredictable. Due to lack of regulation, market participants are subject to manipulated trades, called Pump-Dump schemes. Thus, detection of such trades are quite important for market makers and traders. In this study, we use deep and machine learning methodologies to classify and detect manipulated cryptocurrency trades on main crypto exchanges like Binance. We also compare predictive power for several models. Our contribution is three folded. First, we begin with identifying the systematic and idiosyncratic components of manipulated trades. In order to dissect systematic movements, we control for variations in Bitcoin price. Second, we look at the coin specific factors such as regime shifts in volume, price that drive the trades in the short run. Finally, unlike existing studies, we focus on more refined time frequencies and horizons.

    Mesut is a Senior Lecturer and Course Leader at Finance at Westminster Business School. He holds PhD in Finance (Cass Business School) and MSc in Finance (University of Texas). He taught at London School of Economics (LSE) for several years. He is also founder of SNS Analytics which develops and implements algorithmic trading mostly in cryptocurrencies. Mesut is an expert and avid speaker for various topics in data science, textual/sentiment analysis, and FinTech. He has a big appetite for automation.

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  • 14:30

    PANEL: The Next Decade of Deep Learning in Finance Predictions

  • 15:00

    END OF SUMMIT

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