
REGISTRATION & LIGHT BREAKFAST
Courtney McGill - Bank of New York Mellon
Courtney McGill is the Lead Machine Learning Engineer at Bank of New York Mellon within the Clearance and Collateral Technology (CCT) group.
CCT is responsible for two systemically important systems: Broker Dealer Clearance, which handles the Clearance and Settlement for FED-eligible securities
and Repo-Edge system, which runs the Global Repo Triparty business. Both systems handle multi-trillion USD of assets on daily basis.
Courtney's work is focused on leveraging machine learning and data science to look for opportunities to streamline market efficiency, increase resilience, and reduce overall industry costs.
Courtney holds a Bachelor's degree in Computer Science and Sociology from Colgate University.


TRADITION VS. NEW TECH: THE CHANGING FACE OF FINANCE


Uday Singh - Head of Process Automation and Robotics - Credit Suisse
Opportunities and Challenges for Automation and AI Adoption
Uday Singh - Credit Suisse
Opportunities and Challenges for Automation and AI Adoption
Automation and AI is a key enabler for Credit Suisse to pursue its vision of becoming the most admired bank, which includes establishing exemplary Control, Compliance, and Risk management Processes. Credit Suisse started its process reengineering and automation journey over a couple of years back, with initial focus on RPA. With increasing maturity and deployment of RPA, the focus is now shifting towards more complex automation, to augment human decision making processes, by leveraging Machine Learning and AI. During the session, four key areas will be covered based on experience at Credit Suisse – a) opportunity identification approach across a global bank that has varied business lines (including Swiss Private Bank, Global Markets, International Wealth Management, and Investment Banking & Capital Markets), b) organizational setup (e.g., AI implementation center, data modelling, AI tools management) to drive AI/ automation adoption in a long-term sustainable manner, c) deep dive into select examples (e.g., KYC, Payments processing, asset servicing) of successful deployment of AI/ Automation, and d) challenges and future opportunities/ focus areas (likely to be of interest to systems integrators and startups with product & service offerings in this space)
Uday is leading the process automation and robotics agenda for Credit Suisse, covering the Bank’s various businesses, regions, and shared services functions. In this capacity, he is responsible for opportunity identification through engagement with the various business and functional experts, prioritizing those opportunities to develop viable business cases (with benefits across enhanced controls, risk mitigation, fast time to market, etc.,), ensuring standardization of AI and automation tools used across the bank (so as to not add to the complexity of IT landscape), and driving engagement with AI/ automation vendors (including startups). Prior to Credit Suisse, he spent a few years as a consultant with McKinsey & Co., and was also involved in product development for a couple of Silicon Valley startups. He has Master of Business Administration from Columbia Business School and Bachelor of Engineering from Osmania University, India




Ambika Sukla - Executive Director - AI and Machine Learning - Morgan Stanley
AI in Finance: The Current Landscape
Ambika Sukla - Morgan Stanley
AI in Finance: The Current Landscape
Statistical models have always played a big part in financial decision making. I will talk about evolution of statistical modeling in finance and how newer AI methods can complement traditional modeling and enable exploration of previously untapped data. Using concrete real-life examples, I will explain current possibilities and future potential of AI. Where in finance is AI working well? Where is it still evolving? What are the challenges? How do we integrate AI into current business processes? How to build an organization-wide AI strategy? How to build a good AI team? These are some of the questions I will answer based on our experience.
Ambika Sukla leads Morgan Stanley’s AI Center of Excellence, where his team is focused on researching latest developments in AI and applying selective techniques to different areas of finance. His area of expertise ranges from core statistical/probabilistic models to newer deep learning algorithms. The team works on business problems in sales & trading, research, investment management, risk and other divisions. His research focus is on combining newer deep learning models with traditional econometrics, and devising novel unsupervised and semi-supervised models to extract meaning from vast amounts of unlabeled text. Ambika’s background is in signal processing and information theory and he holds a Masters degree in Telecommunication Engineering from NJIT.




Yuanyuan Liu - Director, Statistical Machine Learning - AIG
Overview of Artificial Intelligence in Finance with Applications in Insurance
Yuanyuan Liu - AIG
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.


COFFEE
AI IN ENTERPRISE
Peter Brodsky - HyperScience
Peter Brodsky is the CEO of HyperScience, an AI company focused on automating office work. The company delivers automation solutions to Global 2000 companies and government institutions around the world, helping them focus on their core business by reducing bureaucratic burden on them and their customers.
Prior to founding HyperScience, Peter was a Director at Soundcloud, where he led an engineering team that built audio fingerprinting, genre classification and audio recommendations based on audio analysis and user behavior. Peter was also founder and CTO of Instinctiv, a startup that provided powerful software solutions for audio content identification and predictive algorithmic recommendations for media content, and which was acquired by SoundCloud in 2012.


AI TOOLS & TECHNIQUES IN FINANCE


Roxana Geambasu - Associate Professor - Columbia University
Certifying Robustness to Adversarial Example Attacks in Machine Learning
Roxana Geambasu - Columbia University
Certifying Robustness to Adversarial Example Attacks in Machine Learning
Adversarial examples that fool machine learning models, particularly deep neural networks, have been a topic of intense research interest, with attacks and defenses being developed in a tight back-and-forth. Most past defenses are best-effort and have been shown to be vulnerable to sophisticated attacks. Recently a set of certified defenses have been introduced, which provide guarantees of robustness to norm-bounded attacks, but they either do not scale to large datasets or are limited in the types of models they can support. This presentation describes the first certified defense that both scales to large networks and datasets (such as Google's Inception network for ImageNet) and applies broadly to arbitrary model types. Our defense is based on a novel connection between robustness against adversarial examples and differential privacy, a cryptographically-inspired technique, that provides a rigorous, generic, and flexible foundation for defense. I discuss the relevance of adversarial example attacks, as well as our defense, for broader applications of machine learning, including the financial sector.
Roxana Geambasu is an Associate Professor of Computer Science at Columbia University and a member of Columbia's Data Sciences Institute. She joined Columbia in Fall 2011 after finishing her Ph.D. at the University of Washington. For her work in cloud and mobile data privacy, she received an Alfred P. Sloan Faculty Fellowship, a Microsoft Research Faculty Fellowship, an NSF CAREER award, a ``Brilliant 10'' Popular Science nomination, an Early Career Award in Cybersecurity from the University of Washington Center for Academic Excellence, the Honorable Mention for the 2013 inaugural Dennis M. Ritchie Doctoral Dissertation Award, a William Chan Dissertation Award, two best paper awards at top systems conferences, and the first Google Ph.D. Fellowship in Cloud Computing.



Bernhard Hientzsch - Director, Head of Model, Library, and Tools Development for Corporate Model Risk - Wells Fargo
Deep Learning and Computational Graph Techniques for Derivatives Pricing and Analytics
Bernhard Hientzsch - Wells Fargo
Deep Learning and Computational Graph Techniques for Derivatives Pricing and Analytics
We review some new approaches from research and literature and Wells Fargo’s work to apply deep learning techniques and computational graph techniques (including algorithmic differentiation) to the solution of high-dimensional forward-backward SDE and PDE in derivative pricing, present some fundamental ideas, applications to derivatives pricing and analytics with some results, and some current and planned extensions
Bernhard Hientzsch is the Head of Model, Library, and Tool Development in the Corporate Model Risk Management Group at Wells Fargo. His group is responsible for the implementation of models, libraries, components, and tools for the validation, benchmarking, and oversight of models at Wells Fargo. Prior to joining Wells Fargo, he was a postdoctoral scientist at New York University in several DoE supported projects and consulting on mathematical, financial, and computer modelling in the USA and Germany. Bernhard received his PhD in applied mathematics from the Courant Institute at New York University.




Igor Halperin - Research Professor of Financial Machine Learning/ AI Asset Management - NYU/ Fidelity Investments
Reinforcement Learning for Portfolio Optimization and Market Modeling
Igor Halperin - NYU/ Fidelity Investments
We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them. Our approach is based on a combination of Inverse Reinforcement Learning (IRL) and RL. First, the IRL component learns the intent of fund managers as suggested by their trading history, and recovers their implied reward function. At the second step, this reward function is used by a direct RL algorithm to optimize asset allocation decisions. We show that our method is able to improve over the performance of individual fund managers.



LUNCH
SMART LEDGERS: THE INTELLIGENT ECONOMY


Toby Simpson - CTO & Co-Founder - Fetch.AI
Decentralised Algorithms: Harnessing The Power of Collective Predictions
Toby Simpson - Fetch.AI
Decentralised Algorithms: Harnessing the Power of Collective Predictions
Imagine if anyone could create an algorithm and apply it to vast amounts of data, not just a handful of giant web companies. The Fetch system is based on a concept of Useful Proof of Work, a new consensus mechanism underpinning its distributed ledger that allows large amounts of machine learning computing to occur on a massive decentralised virtual machine. The talk will focus on new approaches to machine learning such as the use of ‘Embeddings’, establishing a trust model for algorithms deployed across the Fetch network as well as examples and approaches for building new ‘bottom-up’ problem solvers whose foundations are rooted in biology.
Toby is Fetch.AI’s Chief Technology Officer and brings with him over a quarter of a century of experience in software architecture and development and more than a decade as CTO across three companies. As a software developer and manager, he’s built and directed several successful computer games including producing the highly successful Creatures series in the 90s. Armed with a fresh, biologically inspired approach to managing software complexity and creating digital intelligence he went on to become Head of Software Design at DeepMind and to construct a shared virtual world architecture called Alice, which powered the world’s most complex online 3D real-time strategy game.


NLP & FRAUD DETECTION


Eric Charton - Senior AI Director - National Bank of Canada
Robustness Challenge with Dialog Systems, Myths and Solutions
Eric Charton - National Bank of Canada
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 holds 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.




Yvonne Li - Director, Data Scientist - FINRA
Text Mining Techniques: FINRA & Fraud Detection
Yvonne Li - FINRA
Text Mining Techniques: FINRA & Fraud Detection
Mining and analyzing text helps organizations find valuable business insights in corporate data. Too often we find the common text mining techniques are not effective in many real-world corporate settings, in particular when dealing with short snippets, boilerplate text collected in forms and repeated text due to cut and paste authoring. Choosing the appropriate combinations of the machine learning techniques between supervised (e.g. classification) and unsupervised (e.g. topic modeling or clustering) learning depends on the end goal and the nature of the data (the content, the size and the sparsity, etc.). Even after deciding on clustering approach, there are still various algorithms to consider including well-known K-means and hierarchical agglomerative clustering. In this presentation, I will illustrate a case study and explain how we tackled these challenges.
Yvonne Li joined The New York Stock Exchange (NYSE) Member Regulation Technology as a data architect in 2005 and later as a director at FINRA, the successor to The National Association of Securities Dealers (NASD) and member regulation/enforcement/arbitration of NYSE. For the past 7 years, Yvonne has been working closely with the Office of Risk and Advanced Analytics Team to develop statistical regression and machine learning models for identifying risks at individual, branch and firm levels of broker-dealers by leveraging both structured and unstructured datasets. Prior to joining FINRA, Yvonne was a consultant at JPMorgan Chase for 8 years where she designed and built data warehouse for the Credit Risk and Credit Rating systems. Yvonne also worked at Salomon Brothers supporting the equity trading desk. Yvonne has a BS and MA in Computer Science. Outside of work Yvonne is an avid runner and a passionate saltwater angler.




Chris Merz - Vice President Security and Decision Products - Mastercard
An Inverse Recommender Approach to Detecting Out-of-Pattern Behaviour
Chris Merz - Mastercard
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.



COFFEE
ALGORITHMIC TRADING & STOCK MARKET PREDICTION
Agnes Tourin - NYU
Position Sizing for Pairs Trades
We focus on optimal position sizing in a portfolio of co-integrated assets. We propose a Markov stochastic process optimization model and derive analytical formulae for the dynamic trading strategies. We also conduct an out-of-sample test on historical data in the bitcoin markets. The implementation merely consists of estimating the parameters with a sliding window and the application of the trading strategy, starting in the second window. The algorithm's performance could be enhanced by using reinforcement learning techniques to improve the estimations, and tune the risk aversion parameter which determines our trading volume, with the goal of minimizing transaction costs.
Agnes Tourin grew up in the suburbs of Paris and graduated from the Engineering school MinesParisTech. In 1992, she received her Ph.D in mathematics, from University Paris-Dauphine. Subsequently, she held a tenured position in France, followed by positions at University of Toronto and McMaster University in Ontario. After moving to New York in 2005, and staying at home to care for her two children for 5 years, she got offered her current position at NYU Tandon, where she teaches mathematics for financial engineering. In her research, she applies stochastic control techniques to algorithmic trading.



Yaron Golgher - Co-Founder and CEO - I Know First
Investment Selection By Combining Chaos Theory with Artificial Intelligence
Yaron Golgher - I Know First
Investment Selection By Combining Chaos Theory with Artificial Intelligence
Yaron is the CEO of I Know First, an Israeli AI-Wealth Tech company he co-founded in 2011. I Know First specializes on the development and application of advanced self-learning forecasting algorithms for the capital markets, utilizing artificial intelligence and machine learning. Predictions are generated for over 10,000 securities and are applied to discover best investment opportunities, support existing research and investment processes as well as structure AI-powered investment products. The concept of the current algorithm has crystallized following decades of prior research into the nature of chaotic systems by I Know First's R&D team. The algorithm incorporates multi-layered neural networks and genetic algorithms, it is adaptable and scalable allowing comprehensive customized algorithmic solutions depending on clients' needs - banks, asset management first, family offices, hedge funds and self-directed DIY-investors.
After completing his degree in Engineering at the Tel-Aviv University, Yaron worked as a division manager at OIC, one of the Israeli leading consulting firms. Yaron Golgher has over 18 years of experience leading AI, deep learning and machine learning projects including development of AI forecasting algorithms and predictive analytics, quantitative trading strategies, algorithmic trading applications, and big-data solutions for hedge funds and institutional clients.
He received his executive MBA from Ben Gurion University, before founding I Know First.



Lipa Roitman - Founder, Partner - I Know First
Investment Selection By Combining Chaos Theory with Artificial Intelligence
Lipa Roitman - I Know First
Investment Selection By Combining Chaos Theory with Artificial Intelligence
The I Know First forecasting algorithm models the markets as non-stationary chaotic systems with fractal properties. Features: Multi-representation – wide and deep supervised learning, data munging to expose important features. Generalization through explicit regularization. Plurality of forecasts. Principal Component Analysis results in near orthogonal inputs. Genetic adaptable algorithm. Can Reinforcement Learning system learn to trade the market? We will report here our results of using Reinforcement Learning to create a trading system based on the I Know First forecasts.
With over 35 years of research in artificial intelligence and machine learning, Dr. Lipa Roitman has developed algorithms to predict chaotic events in financial markets. He has a M.Sc. in chemistry and organic chemistry from Novosibirsk University, a Ph.D. in organic and physical organic chemistry, and photochemistry from the Weizmann Institute of Science, a post doctorate in polymer chemistry from the University of Akron, and has a background in mathematics and statistics. His past work includes: R&D Chemist for Rohm and Haas Corp. USA.
As Co-Founder and CTO of I Know First Ltd., the company uses his algorithm to predict markets and supply big data solutions for institutional and large private investors. Lipa is responsible for the design, development, characterization, specification, and implementation of algorithmic trading solutions. In addition, his other responsibilities include development of quantitative trading strategies, predictive analytics, and development of quantitative trading strategies, algorithmic trading applications, and Big-Data solutions for hedge funds and institutional clients.




Spyros Chandrinos - Graduate Student - Carnegie Mellon University
AIRMS: A Risk Management Tool Using Machine Learning
Spyros Chandrinos - Carnegie Mellon University
AIRMS: A Risk Management Tool Using Machine Learning
We focus on using ML technology as a consultant for trading decisions. In particular, the artificial intelligent risk management system (AIRMS), that is presented, introduces one of the first efforts in the literature to utilize supervised ML as a risk management tool. Two AIRMSs systems are developed based on artificial neural networks and decision trees. These two systems are applied in a FOREX trading breakout strategy classifying its generated signals as profitable and not. Constructing new portfolios using signals classified only as profitable resulted in a significantly enhanced risk profile of the strategy, increasing at the same time the original profit more than 50%.
Spyros Chandrinos grew up in Kalamata, a seaside town in Greece and graduated from the Civil Engineering school of National Technical University of Athens. His thesis results were published in two international scientific journals of Elsevier. He gained experience in the financial sector working in financial institutions in Greece as quantitative trading analyst. Then, he continued his studies in US pursuing a Master's degree in Computational Finance from Carnegie Mellon University. Recently, he worked as a trend following strategies quantitative researcher fellow in KAUST Investment Management Company (one of the largest university endowments in the world with more $25 Billion AUD).

REGULATION & PRIVACY

PANEL: Assessing The Regulatory Hurdle: AI in Finance
Jordan Brandt - Inpher
Privacy-preserving Machine Learning in Finance
More (good) data yields better models, but increasing consumer awareness, privacy regulations and proprietary barriers mitigate access to valuable feature sets and our ability to leverage them. The conundrum of computing data without exposing it can be addressed with emerging cryptographic methods such as Secure Multiparty Computation and Fully Homomorphic Encryption. Furthermore, this opens the opportunity to monetize analytics while maintaining data privacy, security, and scarcity value. This talk will discuss the basics of the technologies and various real applications in financial institutions including fraud detection, credit analysis, customer discovery and more.
Dr. Jordan Brandt is the CEO and cofounder at Inpher, a data security company pioneering privacy-preserving machine learning. As a Technology Futurist, Jordan’s research and insight on cybersecurity, AI, robotics and 3D printing have been featured in print and live broadcast internationally on Bloomberg, CNBC, Forbes, Financial Times, Wired and other business and technology press. Jordan is the former CEO and cofounder of Horizontal Systems, acquired by Autodesk (Nasdaq: ADSK) in 2011. He went on to serve as the director of Autodesk’s investment fund, while also teaching and conducting research as a Consulting Professor of Engineering at Stanford University. Jordan completed his undergraduate work at the University of Kansas and his PhD in Building Technology at Harvard. In 2014 he was selected as one of Forbes ‘Next-Gen Innovators’.


Gabrielle Haddad - Sigma Ratings
Gabrielle Haddad is the Co-Founder & Chief Operating Officer of Sigma Ratings, Inc., the world's first non-credit rating agency. Gabrielle began her career as an M&A attorney at Milbank, Tweed, Hadley & McCloy in NYC. She subsequently spent several years as an executive at The Global Fund, a Geneva, Switzerland based international financing institution, where she worked on risk and governance issues in over 30 country portfolios across Africa, Asia and the Middle East. Her passion for international development, technology and innovation led her to return to the US and to spend a year studying at MIT, where she met her co-founder and launched Sigma. Gabrielle has an undergraduate degree in Finance from Villanova University, a J.D. from The George Washington University Law School and an M.B.A. from the Sloan School of Management at MIT.



Adrien Delle-Case - Policy Advisor - Digital Finance Regulation & Policy - Institute of International Finance
PANELIST
Adrien Delle-Case - Institute of International Finance
Adrien Delle-Case is a Policy Advisor for Digital Finance Regulation & Policy and UniCredit fellow at the Institute of International Finance (IIF) in Washington D.C, the global association of the financial industry. Adrien began his career as Legal Expert in the Compliance department of UniCredit Bank AG (member of the UniCredit Group) in Munich, Germany. He focused on financial crime regulations related to anti-money laundering (AML), financial sanctions and anti-corruption. He subsequently became the Head of UniCredit Bank AG's General Policy Matters AML team and was involved in developing and implementing the bank's AML policies across all business lines, as well as representing the bank in the AML Working Group of the Association of German Banks. Adrien is currently seconded to the IIF's Digital Finance Regulation & Policy team, where he focuses on policy developments in the digital space, with an emphasis on AML and financial data management. Adrien has a law degree from the Ludwig-Maximilians-Universität in Munich and qualified as a lawyer after his clerkship under the responsibility of the Bavarian Ministry of Justice and Consumer Protection.



Peggy Tsai - Vice President Analytics & Data - Morgan Stanley Wealth Management
MODERATOR
Peggy Tsai - Morgan Stanley Wealth Management
Data Governance Strategy to Support Machine Learning
For the past decade, enterprise data governance programs have been organized under heavy regulatory scrutiny. Financial services and insurance companies have built up various components of a rigorous data programs largely driven from regulations such as BCBS 239, CCAR, GDPR etc. As a result, many financial companies have built up robust business glossaries and data quality monitoring dashboards and now they seek to gain monetary value from their data. The following session will discuss the fundamentals of data governance as the role of data shifts from compliance towards getting business insights to make informed decisions. At Morgan Stanley Wealth Management, the quality of data is integral to the results of the machine learning models. Incomplete, ill-fit, and unavailable data are just a few scenarios where poor data quality can negatively impact the machine learning results. Data monitoring and proper controls of data are critical components of an enterprise data governance program to ensure that models can be trusted and relied upon for decision-making. We will discuss efforts to provide data transparency to meet regulatory compliance as well as support AI initiatives.
Peggy Tsai is Vice President in the Analytics & Data department at Morgan Stanley Wealth Management. She is responsible for leading the adoption of Data Governance standards and processes across Wealth Management. She works closely with Technology, Business Stewards and Enterprise to improve data quality issues that impact the business. In addition, Peggy works on initiatives to leverage machine learning to improve the capture of data assets across the firm. Peggy has over 15 years of experience in data management, stewardship and governance. Prior to joining Morgan Stanley, Peggy was Data Innovation Lead at AIG where she worked in the enterprise data management practice to support Anti-Money Laundering, Solvency II and GDPR in Latin America and Europe. In addition, she led a winning Innovation Boot Camp team with a proof of concept on Natural Language Processing and Machine Learning aimed at improving the underwriting process. Peggy also worked at S&P Global where she held various managerial positions in enterprise data group and technology in order to drive the value of data between the business and IT. Peggy has a Masters in Information Systems from New York University and a Bachelors of Arts in Economics from Cornell University. She is a member of the Data Governance Professional Organization. In her spare time, she hosts externships for Cornell freshmen students in order to increase the awareness of data and analytics among undergraduates.



CONVERSATION & DRINKS

DOORS OPEN
Teal Willingham - NYU Future Labs
Teal is a General Manager at the NYU Future Labs, NYC's first tech incubator and currently the largest community of AI startups in the city. The Future Labs have graduated over 100 companies with a combined valuation of over $1B, including Clarifai, CB Insights, BounceX, Vettery, and others. Prior to the Future Labs, Teal was an analyst at a Hong Kong-based VC firm, focusing on fintech, healthcare, and IoT. She began her career in fundraising and operations at several growth-stage tech startups, including Sailthru and moksha8, a TPG-backed healthcare company, where she helped drive the company’s international expansion and $40M series C.


START UP SESSION


Bryan Healey - CTO & Founder - Aiera
Making AI Explain Itself: Automating Equity Research
Bryan Healey - Aiera
Making AI Explain Itself: Automating Equity Research
Deep learning is an extremely powerful tool for uncovering and surfacing subtle predictive signals buried in massive amounts of data. As such, it has limitless potential application in the world of finance. Unfortunately, deep learning has also been furiously opaque, and "black box" solutions, however accurate they may be, are problematic for investors. Aiera endeavors to make deep learning explain itself, generating not mere prediction but also detailed automated research that allows investors to understand and have confidence in her thinking.
Bryan is the CTO at Aiera. He was most recently the Director of AI at Lola Travel, and was previously one of the early pioneers at Amazon Alexa, leading multiple teams that were responsible for building a large-scale data management platform and automated model building systems for speech and natural language. He has a 12 year track record of growing start-ups and mid-sized businesses in the Boston Metro area, and he frequently writes, speaks, and teaches about machine learning, artificial intelligence, and entrepreneurship. Healey studied Computer Science at Northeastern University before going on to attain an MBA from Norwich University.




Ahmet Salim Bilgin - Founder - FinBrain Technologies
Deep Learning for Modeling The Future Price Movements of the Assets
Ahmet Salim Bilgin - FinBrain Technologies
Deep Learning for Modeling The Future Price Movements of the Assets
Historical financial market data is the time series data from the past. It is one of the most important and the most valuable components for speculating about future prices. With more data, hence more information is available, it is possible to make better conclusions about what will happen in the next time period. ANN/Deep Learning algorithms can "learn" the complex relations between the financial time series by analyzing large amounts of market data. This data has non-linear dynamics, and can only be analyzed with non-linear methods, where the Deep Neural Networks show great performance on capturing non-linearities. Data pre-processing, Neural Network training, hyper parameter search, optimization and avoiding overfitting are the key factors for obtaining accurate prediction results. In this presentation, we will talk about the key factors for effective neural network training, how we integrate different mathematical models together to optimize the performance, and we will demonstrate our prediction results.
Ahmet Salim Bilgin is an Electrical-Electronics Engineer who has a strong background in Signal Processing and Controls fields of Electronics. He has participated in Life Rescue Radar project, and performed research and development activities. The project aimed to detect the life signals of the human beings stuck under the wreckages.
Combining his interest in Finance with Engineering Math and algorithm development background, Ahmet has started working on Artificial Neural Networks. He has developed Deep Learning models for analyzing vast amounts of financial data to predict the future price movements of the assets, and started FinBrain Technologies.




Laura McKiernan Boylan - Algorithmic Underwriting - Haven Life (Mass Mutual)
Algorithmic Underwriting for Life Insurance
Laura McKiernan Boylan - Haven Life (Mass Mutual)
Algorithmic Underwriting for Life Insurance
The life insurance industry faces a number of challenges: long-term liabilities that can span decades, outdated legacy infrastructure that limits the potential for innovation, and strict regulations that protect consumers but make quick iteration very difficult. At Haven Life and MassMutual, we use algorithms and machine learning models to streamline the customer experience for policy purchases. We leverage both rules-based systems and mortality-targeted models to improve speed to issue, mortality experience for MassMutual, and the overall consumer experience. This presentation will provide an algorithmic overview, and highlight challenges and successes of using AI in the life insurance industry.
Laura McKiernan Boylan, FSA, CERA is the Product Owner for Algorithmic Underwriting at Haven Life, MassMutual’s in-house startup that’s setting the pace for innovation in life insurance. She works with a team of software engineers to build technology that supports direct-to-consumer life insurance sales and underwriting. Prior to joining Haven Life, Laura was an actuary at AXA US. She worked on predictive underwriting, financial advisor analytics, hedging, and reinsurance. Laura received an A.B. in Applied Mathematics from Harvard College.

Junho Song - zeroone.ai
AI & Uncertainty in Finance
I believe exploring the latent space of financial data through unsupervised learning will provide a starting point for finding a new financial alpha that we have not seen before. And for this, understanding of uncertainty and its visualization in AI-driven finance are the most important interpretability techniques giving insights into the process of selecting good values, taking buy-sell position and managing portfolios. Because it is a bridge between learning and reasoning. In this presentation, I will explore some possibilities for analyzing time series data based on unsupervised learning(latent space visualization), as well as ways to reduce epistemic uncertainty through uncertainty visualization.
Junho Song is the co-founder & CTO of zeroone.ai inc. which was selected as NVIDIA Inception Program Member for AI Startup, and supported by Next Canada, NextAI program. He believes the best data is even more compelling than the best algorithm. As a deep learning data scientist, he is interested in analyzing financial data using deep learning technology for AI-driven financial products and neural net applied trading system. As zeroone.ai's inside projects, not focusing on predict up/down directly, we are carrying out projects that are beneficial to the investment insights and strategies of financial experts : anomaly detection using generative model, covariance analysis between alternative data, re-balancing using reinforcement learning, big player tracking on cryptocurrency data. With many years of experience in applied practical deep learning, as a core planner and project leader, he led two projects; DL Pipeline and Military Training Dataset Generation(generative models) for S.Korea Defense (ADD, Agency for Defense Development). He is a Ph.D. Candidate at Seoul National University, and received his M.Sc. degree from KAIST.



Alexey Khitrov - President - ID R&D
AI in Biometrics: User Authentication As A Multi- Layered Security Process with Hassle-Free UX
Alexey Khitrov - ID R&D
AI in Biometrics. User Authentication as a Multi-Layered Security Process with Hassle-Free UX. Goodbye Passwords
ID R&D combines a unique vision for a new authentication experience with the capabilities of a leading R&D team in the global biometric industry. ID R&D is focused on developing new and exciting authentication technologies that increase the security of interactions in the digital world (including emerging fields such as chats and conversational interface) with improved user experience. ID R&D has developed a portfolio of biometric technologies (including behavioral biometrics) and provides R&D services to the customers looking for an edge in innovation.


COFFEE
AI APPLICATIONS IN FINANCE


Andrew Clark - Principal Machine Learning Auditor - Capital One
Reinventing Auditing with Machine Learning
Andrew Clark - Capital One
Reinventing Auditing with Machine Learning
Internal Audit is responsible for providing the 3rd line of defense assurance over the effectiveness of controls in mitigating enterprise risks. We are primarily a judgment-based operation, relying on "humanness" to ascertain if risks are sufficiently being mitigated. This sort of environment makes it difficult to employ machine learning, given the ambiguity of decisions and the need for interpretability to back up decisions that were made. However, these limitations give us the ability to become more imaginative, finding unique ways to employ machine learning. In this talk, Andrew will provide two examples of prototypes being used in audit, an unsupervised machine learning exploratory "clustering" environment to provide insight into looking at data in new ways; and a supervised NLP model that classifies audit reports into different classes for use in reporting.
Andrew Clark is a Principal Machine Learning Auditor at Capital One where he is creating machine learning powered applications to reinvent the audit process. He is also establishing approaches for auditing and interpreting machine learning algorithms. His primary research focus is the application of advanced statistical and computational techniques to create value-added financial auditing solutions with the use of open source software, primarily in the Python ecosystem. Andrew received a B.S. in Business Administration with a concentration in Accounting, Summa Cum Laude, from the University of Tennessee at Chattanooga and an M.S. in Data Science from Southern Methodist University. He also holds the Certified Analytics Professional, American Statistical Association Graduate Statistician, and AWS Certified Solutions Architect - Associate certifications.




Santiago Braje - Global Head of Credit Trading - ING Wholesale Bank
Katana: Augmented Intelligence in Financial Markets
Santiago Braje - ING Wholesale Bank
Santiago Braje is the Global Head of Credit Trading at ING Bank. Santiago has 20 years of experience in banking, having worked extensively in public and private credit markets. He joined ING Financial Markets in 2005 after spending his formative years in corporate finance at Societe Generale and Citigroup in Argentina. At ING, Santiago has been responsible for emerging markets and structured credit trading and more recently led the integration of ING's global credit trading franchise. He has pioneered the application of data science in hedging strategies and market-making and backed by ING's Innovation Fund has sponsored Katana, an augmented intelligence tool to assist decision-making in trading. Santiago holds a BA in Economics from University of Buenos Aires, an MSc in Economics from Torcuato Di Tella University and an MSc in Operation Research from the London School of Economics and Political Science.


Alexander De Souza - Data Scientist - ING Wholesale Bank Advanced Analytics
Katana: Augmented Intelligence in Financial Markets
Alexander De Souza - ING Wholesale Bank Advanced Analytics
Alexander De Souza is a data scientist in INGs Wholesale Banking Advanced Analytics group. Alexander has published original research in the fields of medical physics and astrophysics, before turning his research expertise toward machine learning. At ING Alexander has worked on developing neural networks for large scale network analytics, and has been intimately involved in the conceptualization and development of Katana, a streaming analytics platform that is introducing machine learning driven approaches to ING's securities trading business. Alexander holds a BSc in Physics from the University of Waterloo, and earned his MSc and PhD in Astronomy and Astrophysics (specializing in Scientific Computing) while at the University of Western Ontario.

DECISION MAKING & AI


Alberto Rossi - Associate Professor of Finance - University of Maryland
The Promises and Pitfalls of Robo-Advising
Alberto Rossi - University of Maryland
The Promises and Pitfalls of Robo-advising
We analyze a robo-advising portfolio optimizer that constructs tailored strategies based on investors’ holdings and preferences. Adopters are similar to non-adopters in terms of demographics, but have more assets under management, trade more, and have higher risk-adjusted performance. The robo-advising tool has opposite effects across investors with different levels of diversification before adoption. It has significantly positive effects for the least diversified clients, but little to no effect for the most diversified ones. For all investors, robo-advising reduces – but does not fully eliminate – pervasive behavioral biases such as the disposition effect, trend chasing, and the rank effect, and increases attention based on online account logins. We also study the complementarity and substitutability of human and robo-advice in an effort to understand how Fintech is disrupting the operations of the financial advice industry.
Alberto Rossi is an Associate Professor of Finance at the Smith School of Business, University of Maryland at College Park. His research interests include theoretical and empirical asset pricing, household finance and machine learning. His recent work concentrates on networks, individual investor’s performance, and the risk-return trade-off in financial markets. He has worked extensively in analyzing big data, has collaborated with major brokerage houses and asset managers around the world, and has analyzed individual investor performance data. Professor Rossi’s work has been published in leading academic journals such as the Journal of Finance, the Review of Financial Studies, the Journal of Financial Economics and Management Science. Before joining the Smith School, he worked as an economist at the Board of Governors of the Federal Reserve System in Washington DC. He received his PhD in Economics from the University of California, San Diego.



Cristian Homescu - Director, Portfolio Analytics - Bank of America Merrill Lynch
Opportunities and Challenges of Machine Learning in Quantitative Investment and Wealth Management
Cristian Homescu - Bank of America Merrill Lynch
This presentation delves into successes, opportunities, challenges of ML applications for QWIM: • classification and pattern recognition • network analysis and clustering • time series forecasting • reinforcement learning • synthetic financial data generation • testing investment strategies and portfolios • factor-based investment strategies • nowcasting • incorporating market states and regimes into investment portfolios It also presents practical challenges for ML within context of QWIM: • lack of sufficient data • need to satisfy privacy, fairness and regulatory requirements • model overfitting • causality • explainability and interpretability • hyperparameter tuning
Cristian is part of the Portfolio Analytics team within Chief Investment Office, Global Wealth and Investment Management division Bank of America Merrill Lynch. He is developing and investigating quantitative solutions in areas such as investment strategies, goals-based wealth management, asset allocation, machine learning and big data analysis, factor-based investing and risk factor models, portfolio risk and attribution, stress testing and scenario construction. He is very interested in application of state-of-the-art algorithms and numerical methods in wealth and investment management, and in high-performance computing. Prior to joining Bank of America Merrill Lynch, Cristian was a front office quant for Wachovia and Wells Fargo. After supporting interest rate trading desk, he was the lead quant for FX and Commodities trading desks. He has a PhD from Florida State University in computational and applied mathematics, and MSc degrees from University of Paris XI and University of Craiova.


LUNCH
AI & DEVELOPMENT


Anton Prokopyev - Data Scientist - The World Bank
Applying AI to International Trade: Machine Learning For Identifying Non-Tariff Measures in Text
Anton Prokopyev - The World Bank
“What is AI?” To this day, many bear such question in mind when artificial intelligence is brought up in conversations. Anton Prokopyev will deliver a distilled definition of AI and the people behind it. Listeners will stop worrying about AI and walk away with an action plan for applying it at work.
Anton Prokopyev is a Data Scientist at The World Bank Headquarters in Washington D.C, where he is consulting on behalf of the Innovations in Big Data Program. He has worked at some of the Bay Area’s most prominent technology and online startup companies, and received his Master’s degree from University of California, San Diego. He is specializing in Natural Language Processing and Text Analytics using R, and is frantically sharing relevant content on Twitter as @prokopsky. Fluent in Spanish and Russian.


THE FUTURE OF AI IN FINANCE


Mark Weber - Strategy & Operations Lead, Applied Research Scientist - MIT-IBM Watson AI Lab
Intelligent Finance for Integral Human Flourishing
Mark Weber - MIT-IBM Watson AI Lab
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.




Manuela Veloso - Head of AI Research - JPMorgan Chase & Co.
Insights on AI in Finances
Manuela Veloso - JPMorgan Chase & Co.
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.



PANEL: FinTech & The Traditional Banking Sector: Bridging the Gap
Catherine Flax - Pefin
As CEO, Catherine leads the business and growth strategy for Pefin. Prior, Catherine was the Managing Director and Head of Commodity Derivatives, Americas at BNP Paribas. Before BNP, Catherine was the Chief Marketing Officer for all of J.P. Morgan's businesses, globally. Catherine was nominated as the Most Influential Woman in European Investment Banking in 2011 and 2012, winning that award in 2012. Catherine has an MA from Brown University and a bachelor’s degree from Texas A&M University. She is married with three sons.


Jimmi Shah - J.P. Morgan
Jimmi Shah is part of the digital organization in J.P. Morgan’s Corporate & Investment Bank (CIB) with diverse experience across Strategy, Product Management and Technology Development. In his current role, Jimmi is responsible for (1) delivering the Markets Execution digital strategy, (2) leads the product incubation team that develops new and innovative product development prototypes to drive client value and (3) evaluate and partner with FinTechs to identify opportunities for them to enhance/ expedite our digital agenda. Prior to this role, Jimmi was a digital product manager lead for J.P. Morgan Markets across the trade lifecycle – research, execution, post-trade, and core platform services. Before his digital roles, Jimmi was part of an internal strategy team in J.P. Morgan focused on technology strategy, e-trading strategy and technology strategic investments.
Jimmi holds a B.S. in finance and computer science from Rutgers University and completed the Chartered Financial Analyst Program (CFA). He currently resides in New Jersey with his wife and two children.


Chris Wallace - Greycroft
Based in the New York office, Chris’ responsibilities include evaluating investment opportunities, sourcing new deals, and supporting existing portfolio companies. Prior to joining Greycroft, Chris worked in Product Management at Bank of America Merrill Lynch. While at Bank of America Merrill Lynch, Chris designed and built in-house analytics solutions and data visualization tools, and explored key strategic partnerships with FinTech startups. Chris holds a B.A. in Economics from Columbia University. He spent four months in Senegal where he studied Alternative Economies and Microfinancing Strategies. While in Senegal, Chris worked as an Entrepreneurial Analyst at the Chamber of Commerce of Industry and Agriculture in Kaolack, providing guidance to entrepreneurs in rural areas on their international growth strategies. He supports SMBs in his community in Harlem, NY, and has experience consulting restaurants in the area on their digital strategy and operations. Chris is passionate about mentoring, as he served as mentor for the Big Sibs Mentoring program at Columbia, and he continues to mentor his mentee of 6 years.


Elena Mesropyan - MEDICI (Let's Talk Payments)
Elena Mesropyan is an research professional with an extensive experience working with international teams across borders. Elena started her FinTech journey as a Market Research Analyst at MEDICI (formerly LTP - Let's Talk Payments), and soon was appointed as the Global Head of Content, in which capacity she directs and coordinates all content published on MEDICI website (gomedici.com), newsletters, and social media platforms.
During her time at MEDICI, Elena wrote 250+ viral insights pieces for MEDICI (formerly LTP - Let's Talk Payments), and has been widely published across the top FinTech-focused sources, and by the world’s largest institutions, research & consulting companies, and associations. Destinations where Elena’s content has been published/cited include American Express, Australian Securities Exchange, ISACA, The Federal Reserve Bank of Atlanta, Moody’s Analytics, Hong Kong University (HKU), Deloitte, Capgemini, European Banking Institute, Information and Communications Technology Council (ICTC), Transatlantic Policy Working Group (TPWG) by Innovate Finance, Irish Tech News, Network Branded Prepaid Card Association (NBPCA), UN Blockchain, The National Money Transmitters Organization (NMTA), CUNA Mutual Group, The INSURTECH Book, Center for Financial Inclusion (CFI), StarSe, Citibank, Business Insider, BankDirector.com, FinXTech.com, edX, Finextra, Computerworld HK, Cognizant, Workforce Planning Board of York Region, National Association of Realtors, and dozens of company blogs.
In November 2017 Elena Mesropyan was ranked in the Women in FinTech Powerlist 2017 by Innovate Finance. In 2018, Elena Mesropyan has been recognized as one of the top 100 women in FinTech globally.



END OF SUMMIT

New to AI? Time to Ask Qs! - WORKSHOP
Networking & Ask the Experts During the Coffee Break

Data Governance Strategy to Support Machine Learning - WORKSHOP
Presentation & Discussion with Peggy Tsai, Morgan Stanley Wealth Management

Investor Panel & Networking Session - NETWORKING SESSION
Industry Insights & Tips for Startups

The Next Move? A Discussion on Supervised Machine Learning Model Selection - WORKSHOP
Workshop with Francis Z Lin, Bank of New York Mellon