• 09:00



  • 09:10

    Recent Developments in Deep Learning in Finance

  • 09:30

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


  • 09:50

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

  • 10:10
    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.

    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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  • 10:30


  • 11:00
    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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  • 11:20

    Developing ML-Driven Customer-Facing Product Features

  • 11:40

    Using Deep Learning with Word Embeddings to improve Customer Satisfaction

  • 12:00



  • 13:00
    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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  • 13:20
    Ali Raza

    Financial Fraud Prevention via Machine Learning

    Ali Raza - Machine Learning Engineer - Bank of America

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    Financial Fraud Prevention via Machine Learning

    Modern technologies such as EMV (the chip card) have greatly reduced fraudulent card transactions at brick and mortar stores. Unfortunately, online credit card fraud remains prevalent and is projected to cost consumer over $32 billion by 2020 [1]. In this session we will discuss a how data science is used at Bank of America to reduce the risk of fraudulent transactions. In particular, we will explore a machine learning model that uses customer behavior history to detect suspicious logins in real time for over 20 million logins a day.

    Ali Raza is a Machine Learning Engineer at Bank of America where he works within the consumer protection department. Prior to Bank of America, Ali was as a Software Engineer at Cerner Corporation working on building Electronic Health Records for the medical industry. Ali holds a bachelor’s degree in Computer Science from the University of Missouri.

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  • 13:40
    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.

  • 14:00
    Sophie Chen

    Smart Index Management With AI

    Sophie Chen - Data Scientist (Machine Intelligence Lab) - Nasdaq

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    Smart Index Management With AI

    The financial service industry has been asking the same question for the last decade: how to provide the same level of performance with a much lower cost. In response to this, the global capital markets witnessed an aggressive growth of the passive investment. Its market share is more than doubled in the past 10 years. Our team will explain the challenges of smart index management, and illustrate our application of advanced analytics, machine learning, and optimization for smart portfolio construction.

    Shihui (Sophie) Chen is a Data Scientist at NASDAQ’s Machine Intelligence Lab, a group dedicated to leveraging AI to improve financial markets and solutions. Her previous projects covered alternative data research, risk management, portfolio construction, and optimization. She holds a Masters of Finance degree from MIT.

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  • 14:20


  • 14:50

    Algorithmic Trading Advancement with Deep Learning


  • 15:10

    Towards Algorithmic Assurance of Governing Machine Learning Systems at Scale

  • 15:30

    PANEL: Tackling AI Explainability, Interpretability & Transparency in Finance

  • 16:00


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