• TIMES IN PDT

  • 08:00

    WELCOME & OPENING REMARKS - 8am PDT | 11am EDT | 4pm BST

  • CURRENT LANDSCAPE OF OPPORTUNITIES & APPLICATIONS

  • 08:10
    Kevin Kim

    AI Beyond Pattern Recognition: Decision Making Systems

    Kevin Kim - Data Scientist - Nasdaq

    Down arrow blue

    AI Beyond Pattern Recognition: Decision Making Systems

    While machine learning and artificial intelligence technologies are now advanced enough to outperform humans in variety of tasks, how we make decisions with models varies by practitioner. Reinforcement learning is promising but it is limited to adversarial settings; or, in vernacular, situations where decisions directly impact the environment. Without figuring out how AI systems can make good decisions in environments they cannot influence, we may forever be stuck in a limbo of pattern recognition, prediction, and analytics. What if we can develop a “theory” of AI decision making? Can we view different decision making situations as a set of engineering systems? Can we define key components of an AI decision maker? Answering such questions would enable us to design AI systems in a modular fashion much like how we design many industrial goods like cars. We may even be able to develop industry standards and manuals on how to design AI decision makers. Using actual use cases and other potential real-world applications in both financial and non-financial settings as examples, a systems view on decision making AI systems is proposed. Furthermore, ways to design and build such systems are explored.

    Key Takeaways: 1) AI systems are far more valuable making decisions than making simple predictions and pattern recognitions

    2) We need a “theory of design” for AI decision making systems: For AI to become a trusted part of decision making both in and out of industry, we need to understand and generalize components of AI systems and define what it means to be “robust”.

    3) This starts with looking at actual use cases, identifying similarities, and quantifying key parameters – so we may use standard design techniques to design AI systems.

    Kevin is a data scientist with strong interest in an interdisciplinary approach that combines artificial intelligence, operations research, systems engineering, economics, and quantitative finance. He is a member of Nasdaq’s Machine Intelligence Lab, a team dedicated to using AI to improve capital markets. At Nasdaq, he has worked on projects that cover topics such as alternative data, capital market operations, financial surveillance, and portfolio management. His most recent interest is in developing a procedure for designing robust and fail-proof decision-making AI systems. Kevin holds a Bachelor’s degree in Computer Science from Washington University in St. Louis.

    Linkedin
  • 08:35
    Brian Alexander

    Reduce Compliance Risks with Trustworthy AI

    Brian Alexander - CEO North America - Omina Technologies

    Down arrow blue

    PRESENTATION: Reduce Compliance Risks with Trustworthy AI

    Increasing complexity in the regulatory environment, rates of regulatory change and need for accountability are driving new compliance risks for financial services companies. Trustworthy AI reduces compliance risks while balancing human control and oversight with accountability. AI can automatically identify relevant regulatory changes and predict the impact to the organization (e.g., business units, policies, controls, products/services, contracts). Regulatory changes can be routed to impacted business units with compliance risk indicators/ratings and impact predictions. The AI solution can predict and automatically take actions necessary to maintain compliance, which can be accepted or overridden by the business unit.

    ROUNDTABLE: Defining a Governance Framework Covering the Appropriate Level of Automation/Human Control

    Points of Discussion: Defining compliance and regulatory risks Identifying and rating regulatory/organizational changes Defining compliance risk treatment strategies Including user feedback to improve the AI-enabled compliance management

    Brian is responsible for the strategy and management of Omina Technologies US. He has a B.S. Mech. Eng. and a J.D. Brian has spent the last 25 years working with technology companies and new technologies in capacities ranging from legal advisor to executive to investor. While working in private law practice, Brian represented technology organizations in intellectual property, regulatory and litigation matters. Brian also has advised technology, financial services, energy/utilities and healthcare companies regarding corporate risk and compliance. Prior to joining Omina, Brian worked for C2C with responsibilities including internal legal advisory, corporate strategy and software development, as well as client management on numerous projects covering diverse risk/compliance matters such as information/cybersecurity and data privacy.

    Linkedin
  • 09:00
    Manuela Veloso

    Future of AI Research in Finance

    Manuela Veloso - Head of AI Research - JPMorgan Chase & Co.

    Down arrow blue

    Insights on AI in Finances

    Manuela M. Veloso recently joined J.P.Morgan Chase to create and head an Artificial Intelligence (AI) Research Center. Veloso is on leave from Carnegie Mellon University (CMU) where she is Herbert A. Simon University Professor in the School of Computer Science, and where she was the Head of the Machine Learning Department until June 2018. She researches in AI, Robotics, and Machine Learning. Veloso is AAAI Fellow, ACM Fellow, AAAS Fellow, and IEEE Fellow, Einstein Chair Professor of the Chinese National Academy of Science, the co-founder and past President of RoboCup, and past President of AAAI. Veloso and her students research a variety of autonomous robots, including mobile service robots and soccer robots. See www.cs.cmu.edu/~mmv for further information, including publications.

    Twitter Linkedin
  • 09:25

    COFFEE & NETWORKING BREAK: MEET WITH ATTENDEES VIRTUALLY FOR 1:1 CONVERSATIONS

  • 09:35
    Pamela Negosanti

    Finance Is Not All About Numbers...What About Words?

    Pamela Negosanti - Head of Sales and Sector Strategy, FSI - expert.ai

    Down arrow blue

    PRESENTATION: Finance Is Not All About Numbers...What About Words?

    Every company, regardless of industry, owns some level of business expertise. Every company, at one point or another, experiences the same challenge: scaling that expertise. As a bank, how do you deliver quality service to your customers when inquiries triple on your digital channel in a matter of months?   How do you scale risk management or your onboarding operations when those teams are already at capacity? The capability to integrate technology that can read and understand unstructured documents is no longer a “nice-to-have” within the financial services industry. It’s a necessity. Learn how Natural Language Understanding can power your business.

    Key Takeaways: * Why language must be leveraged as data

    • How language can be transformed into actionable knowledge

    *How to easily use our Natural Language Understanding technology

    ROUNDTABLE: The Value of Language in Finance: Demo & Discussion

    Learn how your organization can use Natural Language Understanding based solutions to scale your enterprise. The capability to integrate technology that can read and understand unstructured documents is no longer a “nice-to-have” within the financial services industry. It’s a necessity. Join us to listen to real world examples and see, firsthand, how Natural Language can power your business.

    Key discussion topics: Live demonstration which goes beyond data mining. Discover how our AI automatically reads, learns and understands the financial industry knowledge at speed and scale. Learn more about how to transform language into value thanks to concrete KPIs and benefits.Learn how your organization can use Natural Language Understanding based solutions to scale your enterprise. The capability to integrate technology that can read and understand unstructured documents is no longer a “nice-to-have” within the financial services industry. It’s a necessity. Join us to listen to real world examples and see, firsthand, how Natural Language can power your business.

    Key discussion topics: *Live demonstration which goes beyond data mining.

    *Discover how our AI automatically reads, learns and understands the financial industry knowledge at speed and scale.

    *Learn more about how to transform language into value thanks to concrete KPIs and benefits.

    Pamela Negosanti is the head of sales and sector strategy for financial services and insurance at expert.ai. She specializes in helping companies execute business transformation through artificial intelligence. Pamela has spent the majority of her career in the technology sector where she has established expertise in artificial intelligence, cognitive computing, intelligent automation, natural language understanding and semantics, to name a few. She owns a degree in translating and interpreting with a specialization in computational linguistics, and speaks fluent Italian, English, French and German. Fueled by change and curiosity, she is firm believer in the power of knowledge share and communities.

    Twitter Linkedin
  • 10:00

    PANEL: Conversational AI & NLP in Financial Services

  • Yanshuai Cao

    PANELIST

    Yanshuai Cao - Senior Research Team Lead - Borealis AI

    Down arrow blue

    Yanshuai Cao is a Senior Research Team Lead at Borealis AI, the AI R&D Institute of RBC. He has done a range of works in AI, from data science consulting to research in core machine learning, computer vision, and natural language processing. Recently Yanshuai and his team have focused on (text-to-SQL) semantic parsing, a problem central to natural language database interface and the next generation conversational AI. Yanshuai received his Ph.D. from the University of Toronto in Computer Science.

    Linkedin
  • Julie Wall

    PANELIST

    Julie Wall - Reader in Computer Science - University of East London

    Down arrow blue

    Detecting Deception and Tackling Insurance Fraud Using Conversational AI

    We are developing an explainable pipeline that will identify and justify the behavioural elements of a fraudulent claim during a telephone report of an insured loss. To detect the behavioural features of speech for deception detection, we have curated a robust set of acoustic and linguistic markers that potentially indicates deception in a conversation. Using statistical measures and machine learning approaches, the detection of these linguistic markers in the right context is being investigated. The explainable pipeline means that the output of the decision-making element of the system will provide transparent decision explainability, overcoming the “black-box” challenge of traditional AI systems.

    Dr Julie Wall is a Reader in Computer Science, Director of Impact and Innovation for the School of Architecture, Computing and Engineering and leads the Intelligent Systems Research Group at the University of East London. Her current research focuses on developing machine learning and deep learning approaches for speech enhancement, natural language processing and natural language understanding and she maintains collaborative R&D links with industry. This has led to the successful acceptance of two Innovate UK grants with a combined total value of £2,273,177. Since starting her PhD in 2006, Julie has been exploring the overarching research area of designing intelligent systems for processing and modelling temporal data. This primarily involves investigating the architectures and learning algorithms of neural networks for a variety of data sources.

    https://www.uel.ac.uk/research/intelligent-systems

    Twitter Linkedin
  • Ehsan Amjadian

    PANELIST

    Ehsan Amjadian - Director of AI & Technology - RBC

    Down arrow blue

    Dr. Ehsan Amjadian earned his Ph.D. in Deep Learning & Natural Language Processing from Carleton University, Canada. He is published in a variety of additional Artificial Intelligence and Computer Science domains including Cybersecurity, Recommender Engines, Information Extraction, and Computer Vision. He is currently the Director of AI & Technology at Royal Bank of Canada (RBC), where he has led numerous advanced AI products from ideation to production and has filed patents in the areas of Data Protection and Finance & Climate. He has also led various open source artificial intelligence initiatives as well as a multitude of research teams. He is a member of IEEE as well as ACL.

    Linkedin
  • Eric Charton

    PANELIST

    Eric Charton - Senior AI Director - National Bank of Canada

    Down arrow blue

    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.

    Twitter Linkedin
  • 10:00

    BREAKOUT SESSIONS: ROUNDTABLE DISCUSSIONS WITH SPEAKERS

  • Vladimir Teodosiev

    ROUNDTABLE: Speech Recognition in Financial Services: Making A Significant Contribution to the UK Economy

    Vladimir Teodosiev - Sales Manager - Nuance Communications

    Down arrow blue

    PRESENTATION: Speech Recognition: Comprehensive Insights in Real-World AI for Business Leaders

    Learn how to streamline your workload, boost efficiency and automate processes with Dragon Speech Recognition. This live short tutorial will show you how you can overcome challenges such as documentation, collaboration across teams in real time and how to get key business insights that will help you perform better as a business and improve your services to customers.

    ROUNDTABLE: AI & Speech Recognition in Financial Services

    Economies are responding to many challenges (e.g. Brexit and the Covid-19 pandemic). Businesses need to find ways to remain both competitive and sustainable. As markets and profitability wax and wane under these influences, the financial services sector faces real and present pressures. How can we advance speech recognition in this time, and consequently build better products & provide better services?

    Vladimir Teodosiev is a Sales Manager at Nuance delivering value to high-profile private and public organisations. Commercially savvy and with a strong interest in technology, he has extensive experience in creating and delivering value to high-profile private and public organisations. Highly customer-focused and results-driven, he supports businesses in developing best practices that facilitate efficient document creation.

    Twitter Linkedin
  • Nataliya Le Vine

    ROUNDTABLE: Introduction to Swiss Re’s Risk Resilience Platform

    Nataliya Le Vine - Lead Data Scientist - Swiss Re

    Down arrow blue

    Developing Early Warning System to Identify Relevant Events in Unstructured Data

    Swiss Re is a leading player in the global reinsurance sector. Its role is to anticipate, understand and price risk in order to help insurers manage their risks and absorb some of their biggest losses. As one way to stay ahead of the curve and provide thought leadership to its clients, Swiss Re is developing an early warning expert community platform based around big data and natural language processing. The platform is intended to work on the front lines, to detect events that have the potential to change our view on risk drivers and to help us make business decisions in shorter timescales.

    Key Takeaways: 1) The traditional methods to identify relevant events become unreliable when information volume rapidly increases; 2) Uncoordinated views pose a challenge in taking proactive and strategic actions to manage risks; 3) Early warning expert community platform leverages new data techniques to identify relevant signals and helps integrating experts in a more joined up process.

    Nataliya Le Vine is a data scientist at Advanced Analytics Center of Excellence at Swiss Re, bringing machine learning and AI to drive the technology transformation in insurance. Over the last decade, she worked in academia, tech and insurance industries both in EMEA and Americas with a core expertise in predictive modeling and machine learning.

    Linkedin
  • Annie Xue

    GUEST

    Annie Xue - Head Global L&H Product Risk Data Service - Swiss Re

    Down arrow blue

    Annie leads the global product team of Swiss Re’s Risk Data Service platform along with various client risk management services for L&H insurers.

    A recent example of Annie’s work is with the Swiss Re Risk Resilience Center Center. The ambition is to join the forces of academia, insurers, and data partners to solve Covid-related problems that are most relevant to the insurance industry and beyond.

    In a past life, she has over a decade experience in Reserving, Inforce Management and Experience Studies. Annie is an eternal optimist and brings unparalleled energy and passion into her work.

    Linkedin
  • Brian Alexander

    ROUNDTABLE: Defining a Governance Framework Covering the Appropriate Level of Automation/Human Control

    Brian Alexander - CEO North America - Omina Technologies

    Down arrow blue

    PRESENTATION: Reduce Compliance Risks with Trustworthy AI

    Increasing complexity in the regulatory environment, rates of regulatory change and need for accountability are driving new compliance risks for financial services companies. Trustworthy AI reduces compliance risks while balancing human control and oversight with accountability. AI can automatically identify relevant regulatory changes and predict the impact to the organization (e.g., business units, policies, controls, products/services, contracts). Regulatory changes can be routed to impacted business units with compliance risk indicators/ratings and impact predictions. The AI solution can predict and automatically take actions necessary to maintain compliance, which can be accepted or overridden by the business unit.

    ROUNDTABLE: Defining a Governance Framework Covering the Appropriate Level of Automation/Human Control

    Points of Discussion: Defining compliance and regulatory risks Identifying and rating regulatory/organizational changes Defining compliance risk treatment strategies Including user feedback to improve the AI-enabled compliance management

    Brian is responsible for the strategy and management of Omina Technologies US. He has a B.S. Mech. Eng. and a J.D. Brian has spent the last 25 years working with technology companies and new technologies in capacities ranging from legal advisor to executive to investor. While working in private law practice, Brian represented technology organizations in intellectual property, regulatory and litigation matters. Brian also has advised technology, financial services, energy/utilities and healthcare companies regarding corporate risk and compliance. Prior to joining Omina, Brian worked for C2C with responsibilities including internal legal advisory, corporate strategy and software development, as well as client management on numerous projects covering diverse risk/compliance matters such as information/cybersecurity and data privacy.

    Linkedin
  • James Fort

    ROUNDTABLE: Solving Retail Challenges with Computer Vision

    James Fort - Senior Product Manager, Computer Vision - Unity

    Down arrow blue

    PRESENTATION: Power Up Your Visual AI with Synthetic Data

    Computer Vision is rapidly changing the retail landscape with respect to both the customer experience and in-store day to day logistics like inventory monitoring, brand logo detection, shopper behavior analysis , autonomous checkout. Traditional methods of training models with real world data is becoming a big bottleneck to faster deployment of these vision models. Learn how machine learning and computer vision engineers are using Unity to get faster, cheaper and more unbiased access to high quality synthetic training data and accelerating model deployment.

    Key Takeaways: 1) Computer vision is becoming essential in retail with applications ranging from planogram verification to inventory monitoring to cashier-less checkout. 2) Labeled data is critical to computer vision but the traditional approach of using real-world training data is expensive, time-consuming, and often insufficient for training a production-level system. In contrast, synthetic datasets are less expensive, faster to produce, perfectly labeled, and tailored with the end application in mind. 3) Unity has technology to produce synthetic datasets with structured environments and randomizations that lead to robust model performance. This presentation shows samples from a Unity retail-oriented dataset that you can download.

    ROUNDTABLE: Solving Retail Challenges with Computer Vision

    Join Unity Computer Vision Experts and peers to discuss the rapidly growing field of computer vision and how it is impacting the retail world and some of the challenges associated with deploying computer vision in Retail. This is a freeform session where you can come to the table with your questions and we will have an engaging and interactive conversation around those topics. You can also use this time to talk with the Unity team about using synthetic data for training computer vision models and dig deeper into customer stories and proof points around synthetic data

    James has 14 years of experience building and applying simulation and artificial intelligence technologies. He started his career in the simulation brand at Dassault Systèmes, where he worked on mechanical simulation solutions for automotive and aerospace customers. He spent several years managing the delivery of natural language systems for Alexa at Amazon. He has worked as a product manager in the AI organization at Unity since 2019 focusing on Unity Simulation and Unity’s solutions for computer vision and is excited about the next frontiers in AI.

    Twitter Linkedin
  • Kevin Kim

    ROUNDTABLE: Brainstorming AI Design Principles – Implementation & Theory

    Kevin Kim - Data Scientist - Nasdaq

    Down arrow blue

    AI Beyond Pattern Recognition: Decision Making Systems

    While machine learning and artificial intelligence technologies are now advanced enough to outperform humans in variety of tasks, how we make decisions with models varies by practitioner. Reinforcement learning is promising but it is limited to adversarial settings; or, in vernacular, situations where decisions directly impact the environment. Without figuring out how AI systems can make good decisions in environments they cannot influence, we may forever be stuck in a limbo of pattern recognition, prediction, and analytics. What if we can develop a “theory” of AI decision making? Can we view different decision making situations as a set of engineering systems? Can we define key components of an AI decision maker? Answering such questions would enable us to design AI systems in a modular fashion much like how we design many industrial goods like cars. We may even be able to develop industry standards and manuals on how to design AI decision makers. Using actual use cases and other potential real-world applications in both financial and non-financial settings as examples, a systems view on decision making AI systems is proposed. Furthermore, ways to design and build such systems are explored.

    Key Takeaways: 1) AI systems are far more valuable making decisions than making simple predictions and pattern recognitions

    2) We need a “theory of design” for AI decision making systems: For AI to become a trusted part of decision making both in and out of industry, we need to understand and generalize components of AI systems and define what it means to be “robust”.

    3) This starts with looking at actual use cases, identifying similarities, and quantifying key parameters – so we may use standard design techniques to design AI systems.

    Kevin is a data scientist with strong interest in an interdisciplinary approach that combines artificial intelligence, operations research, systems engineering, economics, and quantitative finance. He is a member of Nasdaq’s Machine Intelligence Lab, a team dedicated to using AI to improve capital markets. At Nasdaq, he has worked on projects that cover topics such as alternative data, capital market operations, financial surveillance, and portfolio management. His most recent interest is in developing a procedure for designing robust and fail-proof decision-making AI systems. Kevin holds a Bachelor’s degree in Computer Science from Washington University in St. Louis.

    Linkedin
  • 10:20

    COFFEE BREAK

  • 10:30

    PANEL: Analyzing AI Advancements & Applications for Fighting Financial Crime & Improving Compliance

  • Changxin Miao

    MODERATOR

    Changxin Miao - Machine Learning Engineer - ING

    Down arrow blue

    Machine Learning Engineer at ING. Currently, I am using NLP to extract key information from documents. I have also worked with credit risk analysis using machine learning models for SME customers. Before joined ING, I worked in Accenture and IBM with AI projects with anti-money laundering and financial assistant topics. Coming from an academic AI background, I always keen on applying the state of art models to real-life financial problems. Nevertheless, my passion does not limit in R&D, but also bring the models in production and benefit users.

    Linkedin
  • Mehrdad Mamaghani

    PANELIST

    Mehrdad Mamaghani - Head of Department, Data Science & Applied AI, Anti-Financial Crime - Swedbank

    Down arrow blue

    Mehrdad Mamaghani is the head of Department for Data Science at Swedbank. The Department is the bank’s Centre of Excellence for Analytics & AI and focuses mainly on financial crime and cybersecurity.

    Mehrdad has a PhD in applied mathematical statistics and has spent the last three years building data science capabilities in the bank within a wide variety of applications and business domains.

    Twitter Linkedin
  • Chris Merz

    PANELIST

    Chris Merz - Vice President Security and Decision Products - Mastercard

    Down arrow blue

    Chris Merz has been with Mastercard for 18 years. He leads several teams of data scientists that provide an AI layer to such security and decision products as Decision Intelligence, Safety Net, Smart Authentication, AI Express, Stand In, AML, and other R&D efforts. Chris has also spent time in Mastercard Labs, Loyalty Solutions, and Advisors, applying machine learning techniques to engage cardholders. Before joining Mastercard, he worked at a startup for online personalization and performed machine learning research at the NASA COE for AI, and the McDonnell Douglas Research Lab. He has a PhD in Machine Learning from the University of California, a Master of Science in Computer Science from Missouri University of Science and Technology, and a Bachelor of Science in Computer Science from the University of Missouri, St. Louis.

    Twitter Linkedin
  • 11:20

    MAKE CONNECTIONS: MEET WITH ATTENDEES VIRTUALLY FOR 1:1 CONVERSATIONS & GROUP DISCUSSIONS

  • 11:50

    END OF DAY 1

  • TIMES IN PDT

  • 08:00

    WELCOME & OPENING REMARKS - 8am PDT | 11am EDT | 4pm BST

  • MODEL ADVANCEMENTS & CONSIDERATIONS

  • 08:10
    Dhagash Mehta

    Fund2Vec: Mutual Funds Similarity Using Graph Learning

    Dhagash Mehta - Senior Manager, Investment Strategist - Vanguard

    Down arrow blue

    Fund2Vec: Mutual Funds Similarity Using Graph Learning

    Identifying similar mutual funds (including exchange-traded funds) with respect to the underlying portfolios has found many applications in fund recommender systems, competitors analysis, marketing and sales of the products. The traditional methods are either qualitative, and hence prune to biases and often not reproducible,or, are known not to capture all the nuances (non-linearities) among the portfolios from the raw data. We propose a radically new approach based on the weighted bipartite network representation of funds and their underlying assets data using a sophisticated machine learning method called Node2Vec to learn an embedded low-dimensional representation of the network. We use this network embedding to identify similar portfolios by computing node similarities in the representation space, which we call Fund2Vec. Our approach provides novel insights to the portfolio similarity problem as well as a data-driven method to remove bias from qualitative categorizations available in the market. Ours is also the first ever study of the weighted bipartite network representation of the funds-assets network.

    Key Takeaways: 1) Machine Learning for mutual funds analytics (sales, marketing, portfolio diversification, etc.) 2) An interesting usecase for application of graph machine learning in investment management industry 3) A bit technical details on a specific graph machine learning algorithm called Node2Vec

    Dr Dhagash Mehta is a Senior Investment Strategies Manager (Machine Learning and Asset Allocation) at Investment Strategies Group at Vanguard, and prior to that was a Principal Research Data Scientist at Vanguard. Dr Mehta is an Editorial Board Member at the Journal of Financial Data Science (https://jfds.pm-research.com/). Dr. Mehta pursued his undergraduate studies in Physics in India, followed by Part III of Mathematical Tripos at the University of Cambridge, and Ph.D. in theoretical particle physics from the University of Adelaide (Australia) as well as Imperial College London (UK). Before joining Vanguard, he was a Senior Research Scientist at United Technologies Research Center (now called Raytheon Technology Research Center), and prior to that a Research Professor at Department of Applied and Computational Mathematics and Statistics at University of Notre Dame. He has held multiple research positions at various research institutes such as Fields Institute in Toronto, Simons Institute for Theory of Computing at Berkeley, the University of Cambridge (UK), Imperial College London (UK), the University of Adelaide (Australia), North Carolina State University (USA), Syracuse University (USA) and National University of Ireland Maynooth (Ireland). Dr. Mehta’s areas of expertise are theory of machine/deep learning, and applications of machine learning in finance.. In particular, he has published 75+ research papers in reputed journals on optimization (convex and nonconvex), computational algebraic geometry, numerical analysis, network science and machine learning to solve various problems arising in financial services and wealth/asset management (and in the past, power systems and control theory; theoretical physics, jet-engines, and smart building systems).

    Twitter Linkedin
  • 08:35
    Vladimir Teodosiev

    Speech Recognition: Comprehensive Insights in Real-World AI for Business Leaders

    Vladimir Teodosiev - Sales Manager - Nuance Communications

    Down arrow blue

    PRESENTATION: Speech Recognition: Comprehensive Insights in Real-World AI for Business Leaders

    Learn how to streamline your workload, boost efficiency and automate processes with Dragon Speech Recognition. This live short tutorial will show you how you can overcome challenges such as documentation, collaboration across teams in real time and how to get key business insights that will help you perform better as a business and improve your services to customers.

    ROUNDTABLE: AI & Speech Recognition in Financial Services

    Economies are responding to many challenges (e.g. Brexit and the Covid-19 pandemic). Businesses need to find ways to remain both competitive and sustainable. As markets and profitability wax and wane under these influences, the financial services sector faces real and present pressures. How can we advance speech recognition in this time, and consequently build better products & provide better services?

    Vladimir Teodosiev is a Sales Manager at Nuance delivering value to high-profile private and public organisations. Commercially savvy and with a strong interest in technology, he has extensive experience in creating and delivering value to high-profile private and public organisations. Highly customer-focused and results-driven, he supports businesses in developing best practices that facilitate efficient document creation.

    Twitter Linkedin
  • 09:00
    Kishore Karra

    Identifying and Addressing Bias in Machine Learning Models Used in Banking

    Kishore Karra - Executive Director, Model Review Group - J.P. Morgan

    Down arrow blue

    Identifying and Addressing Bias in Machine Learning Models Used in Banking

    Banks are increasingly relying on Machine Learning models as decision support systems in various areas such as fraud detection, credit scoring, and optimal order execution. When a model makes a decision on a client application, it is important to ensure that the decision is unbiased and explainable, both from regulatory and moral standpoint. This talk will focus on relevant regulations and some of the ways in which these biases can be identified and addressed.

    Key Takeaways: 1. Talk will present the types of applications of ML models where ‘biases’ could influence model decisions 2. Applicable regulations in banking domain 3. Some statistical techniques that will help identify the biases and possible course of actions to address the biases.

    Kishore Karra is an Executive Director in the Model Review Group at JP Morgan Chase. In this role, he assesses and mitigates risks posed by Machine Learning models used in different areas of the bank. Kishore holds a Master’s degree in Mathematics from Rutgers University and an MBA from the Indian School of Business.

    Twitter Linkedin
  • 09:25

    COFFEE & NETWORKING BREAK: MEET WITH ATTENDEES VIRTUALLY FOR 1:1 CONVERSATIONS

  • AI USE CASES

  • 09:35
    Nitesh Soni

    Transformation of Core Business Function Areas Using Advanced Analytics and AI

    Nitesh Soni - Director - Advanced Analytics & AI for Technology - Scotiabank

    Down arrow blue

    Transformation of Core Business Function Areas Using Advanced Analytics and AI

    Financial institutions have already started to embrace AI as a part of their core business strategy. The AI investments are not only focused on enhancing the customer and risk controls analytics but also to transform and modernize their core business function areas. The AI technologies (Automation, ML, NLP, Deep Learning etc) combined with the unprecedented amount of data are helping to increase the operational efficiencies and reduction in cost while mitigating Risk and Compliance issues by creating timely awareness and preventative measures. This talk will touch upon the challenges to establish the AI framework i.e. how to start a journey from foundational analytics to advanced analytics; processes and methodologies to identify and prioritize the business problems those can be solved and scale quickly across business function areas. It will include the POCs of various types of analytics (Self-serving analytics, ML & NLP) with real examples from function areas such as Technology, Finance, Audit & HR.

    Key Takeaways: 1. Modernization of Business Core Functions is must and should be a part of overall corporate strategy 2. Variety of AI technologies are been leveraged to achieve this 3. AI solutions are bringing Gain in operational efficiencies and mitigation in operation and reputational risks.

    Nitesh Soni is a Director at Scotiabank where he is leading end-to-end advanced analytics and AI projects for Corporate Functions. His career spans over 15 years across top tier banks, consulting firm and world leading research institutes with a core expertise in Predictive modeling, Machine Learning, AI and Big Data. He is a regular speaker at the various national and international conferences. While as a researcher, he has been a part of a couple of major discovery experiments and has won few prestigious awards. He holds a PhD degree in Experimental Particle Physics.

    Linkedin
  • 10:00
    Julie Drew

    Transforming Facebook Finance Operations with AI

    Julie Drew - Applied Research Manager, Enterprise Products - Facebook

    Down arrow blue

    Transforming Facebook Finance Operations with AI

    Facebook’s Enterprise Products Applied Research team develops AI solutions to enhance productivity for our Finance operations. In this talk, we present AI solutions we’ve developed for several stages of the order-to-cash cycle: credit management, collections prioritization and cash application. Managing credit limits for Facebook’s advertisers requires a large and rapidly growing number of credit decisions. We built a machine learning solution to assess advertisers for credit risk and help automate credit decisions. Our model has enabled Facebook to automate the majority of credit decisions while avoiding revenue loss from bad debt write offs as well as from credit outages for good accounts. We also use machine learning to prioritize collection activities. We proactively forecast probability of invoice bad debt write-off, and prioritize collections activities based on expected losses. This ML-based prioritization has led to faster payment. Finally we present an AI-based solution for automating cash application. We use OCR to automatically read remittance instructions, and apply payments accordingly. This automation has led to productivity savings and shorter cash application cycle time.

    Key Takeaways, how FB uses AI to; 1) Automate credit decisions 2) Prioritize collection resources 3) Understand business documents

    Julie Ward Drew is an Engineering Manager in Facebook’s Enterprise Engineering organization, where she builds AI & machine learning solutions for finance and compliance. She has over 20 years of experience applying optimization and machine learning to enable step-level improvements across a wide range of enterprise applications. Julie was awarded the 2009 Franz Edelman Prize for Outstanding Achievement in the Practice of Operations Research and Management Science by INFORMS. She received a Ph.D. in Operations Research from Stanford University and a Sc.B. in Applied Mathematics from Brown University.

    Twitter Linkedin
  • Paul Brandenburg

    Transforming Facebook Finance Operations with AI

    Paul Brandenburg - Program and Analytics Manager - Facebook

    Down arrow blue

    Transforming Facebook Finance Operations with AI

    Paul Brandenburg is a Program & Analytics Manager in Facebook’s Finance organization, where he works on leveraging technology & data to better serve new and existing invoicing customers. He has 13 years of experience across strategy consulting (BCG), corporate development (News Corp) and product management (Amazon). He holds an MBA with Distinction from Columbia Business School and a BSc. in International Relations from the London School of Economics.

    Linkedin
  • David Chi

    Transforming Facebook Finance Operations with AI

    David Chi - Applied Research Scientist - Facebook

    Down arrow blue

    Transforming Facebook Finance Operations with AI

    Facebook’s Enterprise Products Applied Research team develops AI solutions to enhance productivity for our Finance operations. In this talk, we present AI solutions we’ve developed for several stages of the order-to-cash cycle: credit management, collections prioritization and cash application. Managing credit limits for Facebook’s advertisers requires a large and rapidly growing number of credit decisions. We built a machine learning solution to assess advertisers for credit risk and help automate credit decisions. Our model has enabled Facebook to automate the majority of credit decisions while avoiding revenue loss from bad debt write offs as well as from credit outages for good accounts. We also use machine learning to prioritize collection activities. We proactively forecast probability of invoice bad debt write-off, and prioritize collections activities based on expected losses. This ML-based prioritization has led to faster payment. Finally we present an AI-based solution for automating cash application. We use OCR to automatically read remittance instructions, and apply payments accordingly. This automation has led to productivity savings and shorter cash application cycle time.

    Key Takeaways, how FB uses AI to; 1) Automate credit decisions 2) Prioritize collection resources 3) Understand business documents

    David Chi is an Applied Research Scientist in Facebook's Enterprise Engineering organization. He works on machine learning and artificial intelligence for internal applications used by finance, supply chain, and compliance organizations. He holds a bachelor's degree in engineering from UC Berkeley and a Ph.D. in engineering from Stanford University.

    Twitter Linkedin
  • 10:25

    BREAKOUT SESSIONS: ROUNDTABLE DISCUSSIONS WITH SPEAKERS

  • Clara Bove-Ziemann

    ROUNDTABLE: Challenges of ML Interpretability and Business Opportunities

    Clara Bove-Ziemann - Design+ ML Researcher - AXA

    Down arrow blue

    PRESENTATION: How to Explain an ML Prediction to Non-Expert Users?

    Machine Learning has provided new business opportunities in the insurance industry, but its adoption is for now limited by the difficulty to explain the rationale behind the prediction provided. In our latest research, we explore how we can enhance one type of explanations we can extracted from interpretability method called local feature importance explanations for non-expert users. We propose design principles to present these explanations to non-expert users and are applying them to a car insurance smart pricing interface. We present preliminary observations collected during a pilot study using an online A/B test to measure objective understanding, perceived understanding and perceived usefulness of our designed explanations.

    ROUNDTABLE: Challenges of ML Interpretability and Business Opportunities

    The rise of Machine Learning (ML) has provided new business opportunities in the insurance industry. ML can for instance help improve pricing strategies, fraud detection, claim management or the overall customer experience. Yet, its adoption can be for now limited by the difficulty for ML to explain the rationale behind predictions. What can be explained from ML models? What do people need to be explained of? How to present explanations? These are some challenges we want to address in ML Interpretability

    I am currently working as a Researcher at AXA and am a PhD Candidate at Laboratoire Informatique de Paris 6 (LIP6). I conduct research on eXplainable AI (XAI) and User Experience (UX) in Machine Learning. I graduated from a Design Master’s Degree in 2015 and works for several years as a User Experience Designer in various fields before starting research on Human+AI interactions.

    Linkedin
  • Xavier Renard

    HOST

    Xavier Renard - Research Data Scientist - AXA

    Down arrow blue

    Xavier Renard is a researcher in data science and machine learning at AXA. He completed a Ph.D. in computer science at Sorbonne University in Paris. His research focuses on the topic of Responsible and Trustworthy AI, in particular on machine learning interpretability.

    Linkedin
  • Pamela Negosanti

    ROUNDTABLE: The Value of Language in Finance: Demo & Discussion

    Pamela Negosanti - Head of Sales and Sector Strategy, FSI - expert.ai

    Down arrow blue

    PRESENTATION: Finance Is Not All About Numbers...What About Words?

    Every company, regardless of industry, owns some level of business expertise. Every company, at one point or another, experiences the same challenge: scaling that expertise. As a bank, how do you deliver quality service to your customers when inquiries triple on your digital channel in a matter of months?   How do you scale risk management or your onboarding operations when those teams are already at capacity? The capability to integrate technology that can read and understand unstructured documents is no longer a “nice-to-have” within the financial services industry. It’s a necessity. Learn how Natural Language Understanding can power your business.

    Key Takeaways: * Why language must be leveraged as data

    • How language can be transformed into actionable knowledge

    *How to easily use our Natural Language Understanding technology

    ROUNDTABLE: The Value of Language in Finance: Demo & Discussion

    Learn how your organization can use Natural Language Understanding based solutions to scale your enterprise. The capability to integrate technology that can read and understand unstructured documents is no longer a “nice-to-have” within the financial services industry. It’s a necessity. Join us to listen to real world examples and see, firsthand, how Natural Language can power your business.

    Key discussion topics: Live demonstration which goes beyond data mining. Discover how our AI automatically reads, learns and understands the financial industry knowledge at speed and scale. Learn more about how to transform language into value thanks to concrete KPIs and benefits.Learn how your organization can use Natural Language Understanding based solutions to scale your enterprise. The capability to integrate technology that can read and understand unstructured documents is no longer a “nice-to-have” within the financial services industry. It’s a necessity. Join us to listen to real world examples and see, firsthand, how Natural Language can power your business.

    Key discussion topics: *Live demonstration which goes beyond data mining.

    *Discover how our AI automatically reads, learns and understands the financial industry knowledge at speed and scale.

    *Learn more about how to transform language into value thanks to concrete KPIs and benefits.

    Pamela Negosanti is the head of sales and sector strategy for financial services and insurance at expert.ai. She specializes in helping companies execute business transformation through artificial intelligence. Pamela has spent the majority of her career in the technology sector where she has established expertise in artificial intelligence, cognitive computing, intelligent automation, natural language understanding and semantics, to name a few. She owns a degree in translating and interpreting with a specialization in computational linguistics, and speaks fluent Italian, English, French and German. Fueled by change and curiosity, she is firm believer in the power of knowledge share and communities.

    Twitter Linkedin
  • Katie Ferriter

    ROUNDTABLE: Discussing Barriers to Adopting Emerging Technologies

    Katie Ferriter - Head, Technology Research & Innovation - BMO Financial Group

    Down arrow blue

    PRESENTATION: Emerging Technology Trends in North American Financial Services

    Join this presentation to delve into the latest technology trends impacting financial service institutions. Katie will spend 20 mins discussing the status, potential impact and the journey to understanding and piloting these trends.

    ROUNDTABLE: Discussing Barriers to Adopting Emerging Technologies

    Join Katie to discuss issues and barriers that prevent emerging technology adoption including but not limited to; maturity, change management, risk management, user acceptance, upskilling. What do you see as pressing issues to solve when it comes to implementing technology solutions?

    Katie is the Head, Technology Research & Innovation at BMO Financial Group. This mandate sees her seeking out knowledge of emerging technologies and determining their maturity and applicability for Financial Services. She runs a team that undertakes research in house as well as partnering with institutions & organizations that are shaping the future of financial technology.

    Linkedin
  • ROUNDTABLE: Cross Industry Extended Q&A on Designing, Strategizing, and Deploying AI

  • Manan Sagar

    GUEST

    Manan Sagar - CTO - Fujitsu

    Down arrow blue

    Trust Me, I’m a Robot – The Future of AI and Automation in Insurance

    Technology is changing the way we live and consume service. Insurance has perhaps been the slowest to react to these changes but it is apparent that data is starting to drive personalisation and precision in insurance. “Real-time-risk-management” enabled by the “subscription model” is, in the very near future, going to become main stream in personal insurance. Advancements in data sciences coupled with a fourfold increase in the number of sensors is going to lead to a seismic shift in insurance models from the traditional insurance model of “Protection” to “Prevention”.

    Key Takeaways: 1. Data is enabling a shift in the insurance model from protection to prevention.

    1. Whilst Automation is going to help reduce processing costs, AI will provide deep insights and the ability to predict.

    2. AI will enable insurance costs to be viewed as a “service charge” rather than an “annual tax”

    Manan is a highly experienced insurance professional and a pragmatic business leader. He has previously lead Lockton’s Singapore business where he delivered organisation-wide changes and then went on to manage one of the largest acquisitions in the insurance industry. A Chartered Accountant by profession and now a technologist by trait, Manan is well regarded for his thought leadership. His career has spanned across the Americas, EMEA and Australasia. As Fujitsu’s Insurance CTO, Manan is responsible for defining the innovation strategy for the insurance sector. In his role he is a strategic advisor to the insurance sector on digital transformation and connected technology solutions.

    Twitter Linkedin
  • Vaibhav Verdhan

    GUEST

    Vaibhav Verdhan - Principal Data Scientist - Johnson & Johnson

    Down arrow blue

    How Can Machine Learning Help in Getting the Maximum out of the Marketing Budget or Minimize Marketing Waste

    Organizations spend a fortune on marketing. Every dollar spent on marketing demands ROI. With a number of marketing channels in place, be it offline or online - organizations always strive to improve the targeting. Marketing aims to target customers at the right time, with the right product, at optimized price and best time. All of this marketing effort makes it really difficult for the marketing teams to plan and allocate resources and budget. Marketing budgets (particularly after a strong social media presence) have to optimize the budgets and improve the targeting methodology. Data analysis, machine learning and AI allows us to improve customer targeting, product placements. At the same time, these capabilities allow marketing teams to really get the maximum from their budgets. In other words, the teams can minimize the “marketing waste”. This talk focuses on sharing insights on - how can the organizations improve their customer targeting and optimize their marketing budgets.

    Key Takeaways 1) What is required to have a successful ML/AI setup for retail 2) What are the areas in the retail industry which can be benefitted by ML/AI 3) What are the common pitfalls to avoid

    Vaibhav Verdhan is a seasoned data science professional with rich experience spanning across geographies and domains. He is a published author with books on machine learning and deep learning. He is a hands-on technical expert and has led multiple engagements in Machine Learning and Artificial Intelligence. He is a leading industry expert, is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland and is working as a Principal Data Scientist at Johnson and Johnson.

    Linkedin
  • Nitesh Soni

    GUEST

    Nitesh Soni - Director - Advanced Analytics & AI for Technology - Scotiabank

    Down arrow blue

    Transformation of Core Business Function Areas Using Advanced Analytics and AI

    Financial institutions have already started to embrace AI as a part of their core business strategy. The AI investments are not only focused on enhancing the customer and risk controls analytics but also to transform and modernize their core business function areas. The AI technologies (Automation, ML, NLP, Deep Learning etc) combined with the unprecedented amount of data are helping to increase the operational efficiencies and reduction in cost while mitigating Risk and Compliance issues by creating timely awareness and preventative measures. This talk will touch upon the challenges to establish the AI framework i.e. how to start a journey from foundational analytics to advanced analytics; processes and methodologies to identify and prioritize the business problems those can be solved and scale quickly across business function areas. It will include the POCs of various types of analytics (Self-serving analytics, ML & NLP) with real examples from function areas such as Technology, Finance, Audit & HR.

    Key Takeaways: 1. Modernization of Business Core Functions is must and should be a part of overall corporate strategy 2. Variety of AI technologies are been leveraged to achieve this 3. AI solutions are bringing Gain in operational efficiencies and mitigation in operation and reputational risks.

    Nitesh Soni is a Director at Scotiabank where he is leading end-to-end advanced analytics and AI projects for Corporate Functions. His career spans over 15 years across top tier banks, consulting firm and world leading research institutes with a core expertise in Predictive modeling, Machine Learning, AI and Big Data. He is a regular speaker at the various national and international conferences. While as a researcher, he has been a part of a couple of major discovery experiments and has won few prestigious awards. He holds a PhD degree in Experimental Particle Physics.

    Linkedin
  • 10:45

    COFFEE BREAK

  • 10:55

    PANEL: Assessing Regulation & Promoting the Responsible Use of Data for Financial Applications of AI

  • Ronan Brennan

    PANELIST

    Ronan Brennan - Strategy and Innovation Manager - NatWest

    Down arrow blue

    Ronan is a Strategy & Innovation Manager at NatWest, with an academic background examining potential inequality outcomes from widespread “AI” adoption. He has previously helped break ground on AI Model Risk Governance, Emerging Technology Strategy, and Platform Business Models within Financial Services.

    Twitter Linkedin
  • David Bryan

    PANELIST

    David Bryan - Director of Presales - MANTA

    Down arrow blue

    Mr. Bryan directs the presales team with MANTA. Prior to joining MANTA from BlackLine, a financial close management SaaS provider where he was Vice President, Global Presales. Over a 20+ year career in the information technology sector, Mr. Bryan has guided technology teams with Computer Associates and Infor. Before focusing on technology, Mr. Bryan served as Chief Financial Officer in the services industry. Mr. Bryan received his Bachelor of Science degree from the University of Virginia.

    Linkedin
  • Mack

    PANELIST

    Mack - Specialist on FinTech/RegTech - The World Bank

    Down arrow blue

    Mackenzie Wallace is passionate about the use of data and technology, both as a product innovator and financial regulator. He is co-author of the World Bank’s 2021 technical note, “The Next Wave of Suptech Innovation: Suptech Solutions for Market Conduct Supervision,” on the growing use of such solutions by financial authorities globally. He is a former financial regulator and early employee of the U.S. Consumer Financial Protection Bureau (CFPB), where he helped pioneer the authority’s innovative consumer complaint system and public complaint database. He also served as Fintech Policy Advisor at USAID where he helped create the RegTech for Regulators Accelerator (R2A), working with financial authorities globally to embed data and technology into supervision. He currently serves as Director and Head of Product at fintech, MPOWER Financing, designing inclusive financial products to make higher education more accessible.

    Linkedin
  • 11:45

    MAKE CONNECTIONS: MEET WITH ATTENDEES VIRTUALLY FOR 1:1 CONVERSATIONS & GROUP DISCUSSIONS

  • 12:15

    END OF SUMMIT

This website uses cookies to ensure you get the best experience. Learn more