• 08:30

    DOORS OPEN

  • 09:15

    WELCOME

  • THE CHANGING FINANCIAL LANDSCAPE

  • 09:30
    Roshini Johri

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

    Roshini Johri - Senior Data Scientist - HSBC

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    Large Scale Machine Learning in Production

    Delivering insights on big data means large scale machine learning projects. This starts from being obsessive about data quantity, data quality, data refresh to the core of machine learning which is modelling and testing. This process has most certainly outgrown the notebook era which should be restricted to eda and poc only. This talk briefly takes a look at the components of a production system such as model tracking, concept drift, model training, etc and why that is important. We will also dive into some of the available machine learning workflows with cloud integration and share resources on how to get started.

    Key Takeaways:

    • Different types of Machine Learning workflows • Basic components of a production environment for machine learning • Scaling to production via cloud

    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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  • 09:50
    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

    This talk aims to provide a literature survey of published use cases and research papers on use of machine learning in finance and how it is helping re-focus the financial sector to its fundamental purpose. The discussion will in particular focus on recent developments in deep learning applications and put a spotlight on some of the relevant research in deep learning and reinforcement learning. Other aspects like generating synthetic data, text analytics, transfer learning and explainability of deep learning models will also be discussed. This talk will conclude with update on evolving regulatory landscape and some ethical questions about use of these models.

    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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  • DEEP LEARNING TOOLS & TECHNIQUES IN FINANCE

  • 10:10

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

  • 10:30

    COFFEE

  • 11:10
    Helen Byrne

    Accelerate your AI Financial Modelling with IPU

    Helen Byrne - Field Applications Engineer - Graphcore

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    Accelerate your AI Financial Modelling with IPU

    In the finance sector, the potential for innovation with advanced machine intelligence is significant. But often, new and complex models are not being fully leveraged due to latency issues and compute restraints. Enter the IPU – a completely new processing architecture designed for machine intelligence, capable of running advanced financial models up to 26x faster. Helen Byrne from Graphcore explains how the IPU’s unique architecture can power such incredible breakthroughs – and what this means for the future of finance and trading.

    Key Takeaways: • Innovation in financial modelling is possible with AI • We need a new processor (the IPU) to do this • We can achieve 26X speedup on real models.

    Helen joined Graphcore in July 2018 as an AI Research Engineer focussed on distributed learning in large-scale machines, before moving to her current role working on customer applications on Graphcore IPUs. She has a BSc in Mathematics and a Master’s degree in Artificial Intelligence. Before joining Graphcore, she was a Maths teacher and worked at an Investment Banking FinTech.

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  • 11:30
    Angela Johnson

    AI in Banking on the Cloud

    Angela Johnson - Head of Risk - Lloyds 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.

    Key Takeaways: • Rationale for cloud • Challenges in moving to the cloud  • Get rights for getting to and thriving in the cloud.

    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

    Detecting Discriminatory Outcomes in Classification Models

  • 12:10

    LUNCH

  • 13:00

    Quantum Neural Networks

  • CUSTOMER FOCUS

  • 13:20

    Developing ML-Driven Customer-Facing Product Features

  • 13:40
    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.

    Key Takeaways: • Industrial applications • State of the art classification • Understanding of DL embedding limits

    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

  • 14:00
    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 Data and 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. Key Takeaways: • AI is flourishing in the payments sector across a wide variety of applications • Use of transaction data delivers tangible benefits to consumers, businesses and society • Visa takes an ethical, responsible approach to data and AI, placing the consumer at the heart of what we do.

    Jessica is a Senior Director in Visa’s Global Strategic Initiatives team leading on Data and Artificial Intelligence. Her work focuses on AI (policy, regulation and ethics); privacy, data protection and data sharing; and consumer empowerment. She has over ten years’ corporate affairs experience across highly regulated, technology-driven sectors including digital and cyber, data and AI, telecoms, cleantech and fintech. During this time, Jessica has advised political parties, businesses (start-up to large scale global enterprises), consultancies, trade bodies, think tanks and NGOs. Jessica sits on the Board of TechUK, as well as data and AI advisory boards and committees at UKFinance, the All Party Parliamentary Group for AI, and the World Federation of Advertisers. She is an enthusiastic member of campaigning group ‘Women Leading in AI’, which advocates for ethics, diversity and inclusion in AI. She previously trained as 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
    Rahul Singhal

    AI-Enabled Data Extraction Using Horizontal AI

    Rahul Singhal - Chief Product Leader - Innodata

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    Lessons Learned in applying AI To Extract Data Points From Derivative Contracts

    Innodata has built a world class solution that allows Legal department/Treasury and Portfolio managers to understand key data points and covenants from executed contracts. Rahul will discuss how the solution was created and lessons learned on building a world class AI system and some best practices.

    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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  • 16:05
    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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  • 16:25

    PANEL: The Rising Impact of RegTech

  • 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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  • 09:45

    Deep Learning for Bankruptcy Prediction

  • TRADING

  • 10:05

    Deep Learning for Cross-Border Finance: International Trade

  • 10:25

    Algorithmic Trading Advancement with Deep Learning

  • 10:45

    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

    Investment AI: Motivations + Next Gen AI: CL

  • 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

    Towards Privacy-Compliant Data Science

  • 13:50

    The Use of AI to Assist Data Governance

  • 14:10

    Detecting Manipulated Cryptocurrency Trades

  • 14:30

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

  • Ronan Brennan

    Panelist:

    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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  • Ashish Ranjan Jha

    Panelist:

    Ashish Ranjan Jha - Machine Learning Engineer - Revolut

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    Ashish Ranjan Jha received his Bachelors degree in Electrical Engineering from IIT Roorkee (India), Masters degree in Computer Science from EPFL (Switzerland) and an MBA degree from Quantic School of Business (Washington). He has received distinction in all 3 of his degrees. He has worked for large technology companies like Oracle, Sony as well as the more recent tech unicorns such as Revolut, mostly focussed around Artificial Intelligence. He currently works as a Machine Learning Engineer.

    Ashish has several years of working experience and specialisation in the field of Machine Learning, and Python is his go-to tool. He has worked on a range of products and projects from developing an app that uses sensor data to predict the mode of transport, detecting fraud in car damage insurance claims, to developing computer vision solutions to automate KYC processes such as Identity Document verification. Besides being an author, machine learning engineer, data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around Machine Learning.

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  • 15:00

    END OF SUMMIT

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