• 09:00

    WELCOME & OPENING REMARKS

  • AI IN FINANCE VIRTUAL SUMMIT

  • PLENARY SESSION

  • 09:10
    Javier Perez

    The Growth of AI Open-Source Software in Unexpected Platforms

    Javier Perez - Open Source Programe Strategist - IBM

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    The Growth of AI Open-Source Software in Unexpected Platforms

    Today Open Source Software (OSS) is more prevalent than in any other era and continues to grow with the latest technologies from AI and Data Science to Blockchain and Autonomous Vehicles. In this session, we are going to review AI open-source in unexpected platforms. Specifically, we are going to cover OSS in the modern mainframe, the platform used by most financial services organizations, including now fintech startups every large financial institution.

    Tensorflow, Python, Spark, and many other widely used OSS have become the building blocks of all AI and ML applications. Open-source is addressing the major trends in the Financial industry: Modernization with AI and big data, regulatory compliance, and DevOps.
    Open Source Software for mainframes is neither widely known nor something new. This session is going to present information on how open source is done for mainframes and how to port existing software to a modern platform available in all Linux distributions.

    Takeaways: • Learn about available open source software in AI • Learn about the platform of choice for AI in Financial institutions • Learn how to continue the growth of the open-source ecosystem for AI

    Bio: Javier Perez leads the Open Source Program for the IBM Z and LinuxONE ecosystem at IBM. Javier has been in the Open Source, Cloud, SaaS, and Mobile industries for 20+ years. He has been working directly with Open Source Software (OSS) for over 10 years, more recently leading product strategy of the Software Composition Analysis product line at Veracode. Prior to Veracode, Javier was at Axway leading a successful open source project, Appcelerator, and at Red Hat where he was Director of Product Management driving the OpenShift-based Mobile Application Platform offering for developers and enterprises including containerized applications. Javier has had the opportunity to speak at webinars and conferences all over the world covering open source, security, cloud, and application development topics. Javier has held leadership positions in Product Management and Sales Engineering for different startups, leading successful product exits and product integrations post-acquisition. Javier holds an honors degree in Computer Systems and an MBA.

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  • 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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  • DEEP LEARNING TOOLS & TECHNIQUES IN FINANCE

  • 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
    Valeria Cortez Vaca Diez

    Detecting Discriminatory Outcomes in Classification Models

    Valeria Cortez Vaca Diez - Senior Data Scientist - Monzo

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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 recently joined Monzo Bank as Senior Data Scientist. Previously, she worked on the development of Machine Learning solutions for different business areas of Lloyds Banking Group and their customers. During this time, she focused on building tools and processes to detect and mitigate bias in Machine Learning 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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  • 10:50

    COFFEE

  • 11:30
    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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  • 11:50
    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.

    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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  • CUSTOMER FOCUS

  • 12:10
    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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  • 12:30
    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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  • 12:50

    Networking & Discussion

  • 09:00

    WELCOME & OPENING REMARKS

  • PREVENTING FRAUD

  • 09:10
    Rafał Jasiński

    How Cloud and ML Ops Enable to Detect Frauds Better

    Rafał Jasiński - Senior Business Analyst and Service Owner - PGS Software

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    How Cloud and ML Ops Enable to Detect Frauds Better

    Where there are transactions, there is the potential for fraudulent behavior – and in the digital landscape, it can be all too easy for software weaknesses to be exploited. Recent technological advances have made possible effective fraud detection methods that can prevent that from happening! To make the most of them, you need to use that technology the right way.

    Join our session to find out: - How Cloud and AI can help you detect fraud - What the benefits of a custom fraud detection solution are - How you can develop a fraud detection solution in just few weeks.

    Rafał Jasiński is a Senior Business Analyst and Service Owner of PGS Software’s Data Services. In his 10 years of IT industry experience, he has worked on diverse projects involving manufacturing, telecommunications and healthcare with a focus on maximizing value through AI and Machine Learning solutions. A proponent of Digital Twin technology and Agile working methodologies, he sees the solution of modern business challenges in empowerment through technology. My main driver in life is curiosity. I strive to acquire new knowledge, gain new experience and learn new skills. IT is a field where I have plenty of opportunities to do just that, learn. I always encourage people around me to do the same, explore new ideas, have courage to try it in the real-world situations. The worst thing that can happen is they will learn something.

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  • AI APPLICATIONS IN FINANCE

  • 09:30

    Next Generation NLP

  • 09:50
    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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  • 10:10

    PANEL: Tackling AI Explainability, Interpretability & Transparency in Finance

  • 10:55

    COFFEE

  • 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
    Sophie Chen

    From Wall Street to Main Street: The Challenges and Opportunities for Leveraging Lessons from Financial Market

    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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  • 12:10
    Pearl Lieberman

    Stop Operating Your Models in the Dark! Lessons Learned From the Field

    Pearl Lieberman - Head of Product Marketing - Superwise.ai

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    Stop Operating Your Models in the Dark! Lessons Learned From the Field

    As AI is becoming ubiquitous, machine learning practitioners are faced with a new challenge: the day after production. As ML systems are inherently data-dependent, trying to ensure their proper behaviour “in production” can be thorny: from drifts to bias or data quality issues, through missing labels.

    In this session, we will share best practices to monitor AI in production in the financial sector, and maximize the value of your AI program for all stakeholders.

    Pearl Lieberman is the head of product marketing at superwise.ai, the startup devoted to assuring the health of AI models in production. With over 10 years experience translating sophisticated technology into valuable business benefits for the financial sector, Pearl has a track record of bringing together teams across the enterprise to advance and scale innovation.

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  • GOVERNANCE IN FINANCE

  • 12:20
    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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  • 12:40

    Networking & Discussion

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