AI in Finance Summit AI in Finance Summit schedule

Between 60%-85% of AI projects fail to achieve the return on investment promised during the planning stage.

In this presentation we’ll discuss how good AI implementation is a lot like making the perfect piece of toast. 

You will walk away with practical tools, tested in the real world, that help create solid alignment between business leaders, implementation experts and data scientists, and we’ll introduce you to a simple, jargon-free approach that helps focus all stakeholders on what really matters – outcomes for end users and verifiable business returns that live up to the promise of AI.

Bjorn Austraat brings more than two decades of diverse experience in taking complex business problems and finding pragmatic, profitable solutions to them through machine learning, AI and other technologies.

Currently, he serves as SVP and Head of AI Acceleration at Truist where he is building out the new AI & Analytics Accelerator (A3) to enable innovation and accelerate scalable solution deployment for AI and analytics across the enterprise. The Accelerator will provide a complete incubation environment including open source and commercial software, AI-optimized hardware, design thinking and Agile methodologies as well as synthetic data to support and empower teammates across Truist to grow ideas from early stage to MVP to fully deployed solutions.

Formerly, Bjorn was SVP for Agile AI at Wells Fargo, Global Cognitive Finance Leader with IBM for a top-3 International Bank and the Global Leader for Cognitive Visioning and Strategy for IBM Watson, where he provided strategic direction for marquee engagements including H&R Block and Vodafone.

Prior to joining IBM, Bjorn held a number of senior leadership roles in companies ranging from Silicon Valley startups to large, multinational consulting enterprises working with companies such as Apple, AT&T, Microsoft and Ford.

Bjorn began his career as an interpreter and translator in Vienna. Today he uses those skills to detect meaningful patterns in noisy business data and craft scalable and profitable strategies that are in tune with the competitive landscape and diverse stakeholder needs.

Bjorn holds an MBA from the Haas School of Business at UC Berkeley, an M.A. in Translation and Interpretation from the Monterey Institute of International Studies, and an M.A. in Conference Interpretation from the University of Vienna, Austria.

When he is not busy with all things technology, Bjorn spends his time with photography, international travel, nutritional science and AI ethics.

In the highly competitive age of digital transformation financial service organizations are facing accelerated urgency to improve their customer and employee experience while simultaneously reducing operating costs, and managing risk and compliance.

To meet these competing demands on their business, these organizations are racing to deploy deep learning to achieve a new competitive edge by optimizing their back office operations with intelligent document processing, personalizing their customer experience with cutting edge NLP models, and reducing fraud and risk using state-of-the-art deep learning.

AI is here and delivering new capabilities to help businesses solve large and complicated challenges. Join Bob Gaines to learn what that means for your business and how deep learning is helping organizations:

• Achieve higher compliance, faster and with lower costs • Dramatically improve Customer Experience • Reduce time to value from years to weeks

Sergio Rego is a customer engineer at SambaNova Systems where he helps clients deploy purpose-built, deep learning solutions in weeks rather than years. Sergio started his career in financial services, where he worked in strategy; active and index management; and product design and management. Sergio also served as a senior data scientist and team manager for a system integrator where he helped federal government agencies deploy ML and AI solutions.

Do you wish to be in the 53% of AI projects which make it from prototype to production? Why not in the select group of the 15% of AI and machine learning projects which deliver?
The key to being successful with AI can be found in replacing your reference to “A” with a different word and driving a strategy from that viewpoint. We will discuss this in detail at the AI Summit and provide examples on how to build or re-build your AI capability from the ground up and be successful in doing so. This talk provides you with insights on how to develop a vision and deliver on a strategy for Artificial Intelligence, while working with executives in business and technology, while maintaining touch with the latest advances in key areas of machine learning, deep learning, NLP/U/G, while managing stakeholder expectations and with a goal of deploying and maintaining trustworthy AI/ML solutions to production and delivering the best business value possible. Join us in this talk to learn more about what A should be and how you can set your path to be in the 15%.

Machine / Deep Learning, NLP, AutoML, MLOps, AWS, Business Analytics.

Strong technology and business acumen with resilience in decision making and demonstrated ability in engaging and communicating with stakeholders, as well as solid technical, strategic and business analysis skills with a drive for solving complex problems and delivering solutions.

17+ years’ hands-on project management and team leadership (small and large teams), including AI technologies, process automation, cloud computing, UX, QA/QC, agile, SDLC, with engineering mindset and delivery focus.

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.

Arria is the global leader in Natural Language Generation - transforming data with the power of language. In this presentation, Jay will explain how Arria software replicates, through data analysis, knowledge automation, language generation and tailored information delivery, the human process of expertly analyzing and communicating data insights. Join us and hear how Arria dynamically turns data into written or spoken narrative—at machine speed and massive scale.

Prior to joining Arria, Jay held key executive positions in global companies including EMC, Documentum, Unisys, and Lockheed Martin. Jay has successfully led companies from growth phase through to post-acquisition integration.

“Arria’s key technology links applications to humans in ways never before possible. Arria is in a key leadership position with its core NLG technology supported by key patents.” - Jay DeWalt

It is estimated that as much as 80% of trading in US stocks is machine-led. This is attributed to the rise of algorithmic trading and quantitative managers, many of whom use AI in their processes. How are fundamental managers and investment professionals to keep up? In this session, Spencer Reich will show the audience how to incorporate AI-driven research into equity portfolios – no coding or engineering knowledge necessary. From idea generation to position sizing to alternative data integration, Spencer will help bring AI-driven investing to the masses and show how Boosted Insights can improve results for all types of managers.

Spencer Reich has over 15 years of investing experience as a portfolio manager, research analyst, and trader across all macro products, including interest rates, currencies, commodities, and equities. Prior to joining Boosted.ai, Spencer was a senior investment analyst and portfolio manager at Tiger Management, reporting directly to Mr. Julian Robertson, Chairman of Tiger. He has ten years of hedge fund experience across all aspects of the investment process including fundamental research and idea generation, trade structuring and execution, and risk management. Spencer began his career at Goldman Sachs in the fixed income division on the interest rate products desk. He earned an MBA from The Wharton School and graduated from Duke University in 2003 with a BA in Economics, Phi Beta Kappa and Magna Cum Laude.

ROUNDTABLE: How Deep Learning Models Help Infuse AI to Assess, Automate and Accelerate Core Financial Functions

Finance has long been seen as visionary; applying machine learning algorithms to automate and accelerate key business processes from capital markets to compliance, risk, and enhancing the customer experience. Today, many of the legacy models are losing their effectiveness while the velocity of data has grown exponentially with an increasing variety of data types and formats. Quite often, the teams that build these older models are long gone. The convergence of these factors presents both a dilemma and an opportunity. In this roundtable session you will learn how NLP models can uncover non-linear patterns between disparate & unstructured datsets impacting risk, compliance, capital markets and customer engagement technology.

Bill Cox is the Senior Director Sales North America at SambaNova Systems driving deep learning implementations for both Public Sector and Enterprise accounts. Bill has extensive experience leading Global Sales teams in HPC compute and storage companies.

• Working with customers who tend not to have substantial footprint with traditional credit bureaus, Octane supplements traditional underwriting with forward-looking cashflow analysis in which income is a key data element. • Validating an applicant’s trustworthiness via an innovative ML/AI solution to detect outliers who have mis-stated their income on application. • Using a smart workflow to automate income verification by triangulating the application income with various alternative data (payroll data, historical spend and payment patterns, earnings on previous employment) to stay true to the company’s mission of automated, smooth, frictionless application experience, with optimization to achieve the right ROI on the automation.

Experienced data scientist with over a decade's experience in leveraging various Machine Learning and analytical techniques to synthesize large amounts of data into evidence-based models and strategies to improve business efficiency and profitability.

Proven people leader with a track record of building, developing, and inspiring teams of data scientists of various sizes across geographic locations.

Accoladed collaborator who can bring people together across functional teams and across levels to drive enterprise level initiatives from inception to successful completion.

Substantial expertise in credit data and alternative data for consumers, merchants and businesses in the U.S.

In this session, Elif Tutuk, Vice President of Innovation and Design will cover the shift from linear decision support to closed loop decision automation systems. Generating insights from data with AI in a linear fashion only goes so far in addressing how we can respond in today’s complex and uncertain world. Data Analytics pipelines supporting closed-loop decision automation need to be more action focused and more outcome driven while ensuring trust is present at each step of the pipeline. In this session, Elif will cover how Qlik’s Active Intelligence platform augments the user with near real time predictive insights and compels trusted actions with application automation.

In her role as Vice President of Innovation and Design at Qlik, Elif is responsible for managing a global team of engineers and designers in planning and executing design, UX and innovation strategies for Qlik’s end-to-end cloud data integration & analytics products. She is an experienced product person in analytics area with diversified background; product management, product design, development and research. She has 15 years of experience in Business Intelligence and Analytics. She is passionate about analytics, innovating with data and augmenting human intelligence with the power of analytics. She is the winner of The Business Intelligence Group’s Artificial Intelligence Excellence Awards program. This award recognizes her work in BI and AI, leading the charge to blend AI into analytics to further the Artificial Intelligence (AI)/human interaction with data.

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.

Mr. Sangha is the Chief Quantitative Investment Officer at Token Metrics Ventures, a cryptocurrency hedge fund, where he leads a team of quantitative analysts and data scientists in deploying quantitative alpha-generating strategies in the blockchain arena across a wide variety of digital assets. Previously, he served as the Global Head of Data Science , Risk, and Market Intelligence at State Street Verus where he helped lead a risk and investment strategy team in developing an artificial intelligence platform that integrated machine learning, natural language processing, portfolio and risk management, and human experiences to explore connections and extract relevant insights between market-moving events and multi-asset class portfolios. Mr. Sangha is an accomplished global macro portfolio manager, strategist, and risk manager, with over 20 years’ experience. He has advised firms on developing quantamental approaches to trading --- strategies at the intersection of structural, statistical, and fundamental trading, utilizing data science, machine learning on both structured and unstructured data, and behavioral analytics. Further, he has led several investment strategy and risk management teams and held a number of senior positions, including Chief Investment Strategist at a $6 billion global macro volatility hedge fund, and Senior Global Macro Trader at a $400 million hedge fund. Mr. Sangha began his career at Goldman, Sachs & Co.,in the Global Investment Research Division. He received honors at the International Mathematics Olympiad, served on the Canadian Mathematics Olympic Team, and is a Magna Cum Laude graduate of Brown University.

Sr. Technology Leader with a track record of building high performing teams and successfully driving business transformation & digital innovation using new and emerging technologies. An AI and Machine Learning practitioner with passion for solving business problems using technology.

Accelerate effective use of advanced technologies- Blockchain, AI, and Machine Learning to support core digitization efforts, and new products and services. Lead a multi-geography, multi-disciplinary team of data scientists, and engineers in the execution of AI/ML and Digital Assets strategy.

Partner with fintech’s and other vendors to evaluate new and emerging technologies, and accelerate their adoption in the enterprise.

Edward Tong is Executive Director in Applied AI & Machine Learning at JPMorgan Corporate & Investment Bank. He leads teams developing scalable machine learning solutions across businesses to drive efficiency and automation. He has broad modeling experience covering Custody, Securities / Markets Operations, Wholesale, Corporate and Consumer Credit with industry experience spanning US, UK and Australia. Edward has prior model development and validation experience at Bank of America, AIG and Royal Bank of Scotland. He has published in several journals including European Journal of Operational Research and International Journal of Forecasting. He holds a PhD in Operations Research from University of Southampton, UK and an MSc in Statistics from University of Queensland, Australia.

John leads the global Financial Services and Technology business at NVIDIA. Their teams work with customers around the globe to bring accelerated computing solutions to bear on their most challenging problems.

NVIDIA's Financial Services and Technology team includes quants, data scientists, and IT leaders who worked for banks and trading firms. The local team is the tip of the global spear; backed by the full spectrum of NVIDIA resources and technologies – always brought to bear through an industry lens. They bring unique value to their strategic financial services customers at every level of the organization, making your AI strategy and partnerships better.

Sandeep Kumar is a proven business operations strategist and technology expert with over 30 years’ experience in capital markets consulting. He has worked with some of the world’s largest buy- and sell-side firms to design and implement transformative, global technology solutions that enhance operating models, streamline data capabilities, and increase revenue. Sandeep has been instrumental in the conception of Synechron’s Blockchain-led accelerators which streamline operations, simplify complex data synchronization, and consolidate fragmented business function, as well as in Synechron’s Artificial Intelligence-driven accelerators helping clients to understand how they can derive benefits from techniques like NLP (natural language processing), RPA (robotic process automation), and machine learning into their businesses. Sandeep is currently spearheading Synechron’s Financial Innovation Labs (‘FinLabs’) Accelerator Programs, all focused on solving some of the complex business challenges of the Financial Services industry. Under his leadership, Synechron has launched industry-leading solutions for the Buy-side industry – InvestTech Accelerators – as well as solutions for the Payments industry – PayTech Accelerators. Sandeep is an alumni of the prestigious Birla Institute of Technology and Science, Pilani, India.