• 08:30

    REGISTRATION & LIGHT BREAKFAST

  • 09:15

    WELCOME

  • THE CHANGING FINANCIAL LANDSCAPE

  • 09:30

    The Fourth Industrial Revolution & Finance

  • 09:50
    Manuel Proissl

    On the Human Element in Building Trustworthy Consumer-Centric AI Products in Banking

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

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    On the Human Element in Building Trustworthy Consumer-Centric AI Products in Banking

    As the demand for personalized AI-empowered assistants along the value chain in banking is growing, the reliability and interpretability of their underlying models become paramount to build trust and sustainable value for consumers. This presentation focuses on human-augmented training of domain-specific neural networks and discusses their use cases and recent advances in methods to address model transparency, adversarial robustness, algorithmic bias and fairness in the context of the current state and perspectives on the evolution of the global regulatory landscape around AI.

    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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  • 10:10

    The Impact of Deep Learning on Economics

  • 10:30

    COFFEE

  • DEEP LEARNING TOOLS & TECHNIQUES IN FINANCE

  • 11:10
    Angela Johnson

    AI in Banking on the Cloud

    Angela Johnson - Head of Security Risk & Compliance - Deutsche Bank

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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.

    Angela is in Deutsche Bank’s Cloud Centre of Excellence. Within this team she is the head of Security, Risk and Compliance. She is responsible for managing all security, risk and compliance with respect to the cloud. At Deutsche Bank, Angela has led teams including Anti-Financial Crime Technology Analytics and Data Quality Colleague Rollout. 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. Angela is on the board of the London Women’s Forum and the Serpentine Swimming Club.

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  • 11:30
    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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  • 11:50
    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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  • 12:10

    LUNCH

  • FINANCIAL INCLUSION

  • 13:10

    Alternative Lending: Deep Learning, Loans and Credit

  • CUSTOMER FOCUS

  • 13:30
    Neal Lathia

    Using Deep Learning to Support Customer Operations

    Neal Lathia - Machine Learning Lead - Monzo

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    Using Deep Learning to Support Customer Operations

    An insight into how Monzo is using deep learning to empower all the customer support, across the bank's various departments.

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  • 13:50
    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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  • 14:10
    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

  • 14:30

    Accelerating Chat Bots with Deep Learning

  • 14:50
    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

    Tomas Navarro is 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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  • 15:10

    COFFEE

  • PAYMENT SYSTEMS

  • 15:45
    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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  • 16:05

    Deep Learning in The Mobile Banking Revolution

  • 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 - RBS

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    Ronan is a Strategy and Innovation Manager at RBS, 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 RBS, 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

  • 09:30
    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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  • 09:45

    Deep Learning for Bankruptcy Prediction

  • 10:00

    Using Deep Learning to Detect and Prevent Financial Fraud

  • 10:15

    Emerging Technology for Financial Forecasting in Enterprise

  • 10:30

    COFFEE

  • TRADING

  • 11:05

    Deep Learning for Cross-Border Finance: International Trade

  • 11:25

    Algorithmic Trading Advancement with Deep Learning

  • INVESTMENTS

  • 11:45
    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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  • 12:10
    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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  • 12:30

    LUNCH

  • 13:30
    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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  • PRIVACY & SECURITY

  • 13:50
    Greg Mason

    The Ese 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

RE•WORK London AI Finance Summit

RE•WORK London AI Finance Summit

31 - 01 April 2020

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