• 09:00




  • 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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  • 09:30
    Mark Weber

    Teacher, Tool, and Sidekick: Designing AI Software Applications in High Compliance Domains

    Mark Weber - Strategy & Operations Lead, Applied Research Scientist - MIT-IBM Watson AI Lab

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    Teacher, Tool, and Sidekick: Designing AI Software Applications in High Compliance Domains

    Modern machine learning methods have shown experimental promise for forensic activities such as relational anomaly detection in anti-money laundering (AML). Yet model performance is only one factor. High compliance domains require substantive attention to interpretability and explainability, where the former supports the task itself (e.g. investigation) and the latter enables the translation of insight into action (e.g. enforcement). In this talk, Mark Weber of the MIT-IBM Watson AI Lab will share a new user-oriented framework for the front-end development of machine learning tools that serve such domains. The Teacher, Tool, and Sidekick (TTS) framework is as follows: (1) first, an application must teach the user how the model itself works; (2) next, the application must help the user examine the world; (3) finally, the model must provide actionable explanatory power enabling the user to engage the world. To illustrate, Mark will present a prototype instance of a new application for anti-money laundering investigations.

    Mark Weber is an applied research scientist and strategy & operations lead at the MIT-IBM Watson AI Lab, an academic-industry partnership for advanced AI research. He also leads the lab’s business program with member companies, working to connect dots across disciplines to bridge fundamental research to real-world impact. Mark’s current works include graph deep learning for anti-money laundering and b_verify, a blockchain-based protocol for verifiable records in agricultural finance, pollution monitoring, and other use-cases. Prior to IBM Research, Mark was a graduate researcher at the MIT Media Lab’s Digital Currency Initiative and a fellow at the MIT Legatum Center for Development & Entrepreneurship while he earned his MBA in finance from MIT Sloan. Classically trained in Notre Dame's intensive "great books" program, Mark spent the first chapters of his career focused on political economy and development. He produced three documentary films on these subjects, most notably the critically acclaimed film Poverty, Inc. Mark's recreational joys include ultramarathon trail running, reading, and experiencing new cultures.

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  • 09:50
    Colin Ware

    Horizon Scanning – Solutions Required to Understand and Interpret Regulations

    Colin Ware - Regulatory Product Manager - BNY Mellon

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    Horizon Scanning – Solutions Required to Understand and Interpret Regulations

    There is an increasing need to know what and when regulatory changes are coming, and how they may impact the industry. Market pressures are changing, and regulators are prioritizing the regulation of evolving business models. Firms are struggling to understand, prioritize and comply with the growing volume of regulations and reporting requirements. It is critical that financial firms increase their ability to: • Monitor and track complex regulations, updates and interpretations globally • Measure and assess the impact of regulatory changes on the business

    Colin has recently joined BNY Mellon as Regulatory Product Manager. He is responsible for ensuring that all products within Asset Servicing are compliant with upcoming regulations and serve our client’s needs to meet the regulatory requirements, including MIFID II, GDPR, MMFR. He represents the company on a number of RegTech Award panels and industry Event Advisory Boards, and helps to co-ordinate the BNY Mellon Tax & Regulatory Client Forum. This industry-leading event includes a number of RegTech panels and the creation of a regular BNY Mellon RegTech Forum. Prior to joining BNY Mellon, Colin spent 4 years working at Barclays as Industry & Regulatory Engagement Lead for Data Management, working with RegTech and vendor solution providers to assess solutions to meet the upcoming Regulatory challenges.Previously, he was Global Product Manager for Omgeo CTM responsible for developing the product to meet all client and regulatory requirements. He has also spent time at a number of leading Investment Bank institutions, including Deutsche Bank, Nomura, and Goldman Sachs, across a broad range of Data Management, Client Service and project management roles.

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  • 10:10

    Easing the Delivery of Regulation in Government & Policy Making Bodies


  • 10:30
    Igor Sumkovski

    AI and Machine Learning solutions for fighting Financial Crime – FIs (banks) and RegTech Firms Partnering for the Future

    Igor Sumkovski - Senior Manager Financial Crime Compliance Advisory - Santander

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    AI and Machine Learning solutions for fighting Financial Crime – FIs (banks) and RegTech firms partnering for the future This presentation will cover: - Where AI solutions are most helpful in solve regulatory compliance, with specific focus on financial crime related legal & regulatory obligations? - The role of RegTech in reducing inefficiency/ ineffective Financial Crime processes. - Barriers and obstacles to using AI. Overcoming challenges.
    - Bringing FIs (banks) and Regtech firms closer, better understanding of each other’s perspectives and finding common ground. - Future focus - keeping up with AI and Financial Crime risks in an evolving regulatory landscape.

    As a Senior Financial Crime Manager for Santander, Igor is responsible for the implementation and delivery of an effective Financial Crime risk management strategy and policy framework. He obtained his Master’s Degree in Financial Regulation, International Corporate Governance and Economic Law at the Institute of Advanced Legal Studies, University of London and he is ICA qualified financial crime professional with proven BAU and project management experience, gained within some of the largest UK and EU banks. Igor has a strong anti-financial crime background and comprehensive understanding of the external environment in respect to regulators, industry practice and benchmarking, strong knowledge of the UK/EU financial crime compliance legal and regulatory frameworks. He has genuine interest in AI and Machine Learning, committed to continuously exploring opportunities to use new technologies for greater effectiveness in combating financial crime.

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  • 10:50



  • 11:30
    Soumya Kalra

    Evaluating Our Defenses with a Data Science Approach

    Soumya Kalra - Senior Quantitative Risk Specialist - Federal Reserve Bank of San Francisco

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    Soumya Kalra is currently a Senior Quant Risk Specialist in Banking Supervision at the Federal Reserve Bank of San Francisco. Previously, she worked as a quantitative analyst at the New York Federal Reserve Bank and as a researcher focused on private funds and commodities at the Office of Financial Research at the Department of Treasury. She is very passionate about using R in the statistical and data visualization work she performs in her current role. Prior to her move to the Bay area, she was lead organizer of the R-Ladies New York chapter with a mission to promote gender diversity and create a forum to engage with the open source community. She holds a Masters in Mathematical Finance and a Bachelor’s degree in Economics from Rutgers University.

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  • 11:50
    Brennan Lodge

    A Home Grown Machine Learning Pipeline for the Security Incident Response Team (SIRT)

    Brennan Lodge - Data Science Lead for SIRT - Goldman Sachs

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    A Home Grown Machine Learning Pipeline for the Security Incident Response Team (SIRT)

    Security Incident Response Teams (SIRT) have a blend of infrastructures, disparate logs and data sets, a SEIM, ticketing systems and a need for analytics to better serve and improve their firm's detective cyber security control posture and incident response capabilities. An analytics platform that can be robust, stable, compliant, version controlled and automated to deploy analytics to better serve SIRTs missions are the goals to be discussed. An additional use case of implementing a machine learning application and API service to predict Domain Generating Algorithms with the integrated data science pipeline and platform is also discussed and used as a reference.

    Brennan is a self-proclaimed data nerd. He has been working in the financial industry for the past 10+ years and is striving to save the world with a little help from our machine friends. He has held cyber security, data scientist, and leadership roles at JP Morgan Chase, the Federal Reserve Bank of New York, Bloomberg, and Goldman Sachs. Brennan holds a masters' degree in Business Analytics from New York University and participates in the data science community with his non-profit pro-bono work at DataKind, and as a co-organizer for the NYU Data Science and Analytics Meetup. Brennan is also an instructor at the New York Data Science Academy and teaches data science courses in R and Python.

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  • 12:10

    How to Secure Representative Data Sets & Reduce Bias

  • 12:40

    Networking & Discussion

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