Artificial Intelligence (AI) and Machine Learning (ML) are disrupting the financial services industry, and rightly so. The Finance, Banking and Insurance industries are sitting atop a mountain of customer data and are well positioned to benefit their
business and their customers if they can utilize it effectively. AI can serve to improve decision-making, affect overall business strategy, generate new revenue, predict customer behavior, automate customer service, improve risk models, reduce costs,
enhance business operations, improve customer experience, offer tailored products and advice, prevent fraud, and optimize internal processes. This track brings together business leaders and data science practitioners from the leading banks, insurance
firms, asset management organizations, broker and investment firms, and fintech startups.
- How organizations are adopting AI, ML, data analytics, image, voice recognition and NLP technologies across their enterprise to improve their businesses and better serve their customers
- Integrate AI into business strategy development in banking, finance and insurance to make data-driven management decisions for the enterprise
- How are innovators and Centers of Excellence bridging the gap between the tech and the business and developing a business case for AI
- Applying AI to compliance, anti-money laundering (AML), fraud detection and digital identity
- Using AI, ML and Deep Learning to improve personalization and predict customer behavior in banking, finance and insurance
WEDNESDAY, OCTOBER 23, 2019
ROUNDTABLE: What is Better for Introducing AI to Larger Companies – Centralized COE vs Distributed Collective Development
Moderator: Alex Muller, MBA, Senior Vice President, Entrepreneur in Residence, Synchrony Financial
ROUNDTABLE: Enabling Advanced Analytics Implementations at Enterprise Level – Tech and Business Perspectives
Moderators:
Srinivaas Krovvidy, PhD, Head, Advanced Analytics Enablement, Enterprise Data, Fannie Mae
Prabhakar Bhogaraju, MBA, Vice President, Digital Products, Fannie Mae
ROUNDTABLE: Solving Anonymous ID Stitching – Handling Multiple Personas in Silo-ed Systems
Moderator: Sridhar
Raman, Product Development Leader, Intuit Inc
ROUNDTABLE: AI and the Financial Crime Arms War: Pairing Consortium Data with Advanced Analytics
Moderator: Rivka Gewirtz Little, Research Director, Global Payment Strategies, IDC
Thursday, October 24
Cityview 2
7:45 am Registration Opens
8:00 Continental Breakfast (Harborview Foyer)
9:00 - 12:25 pm Plenary Keynote Sessions (Harborview)
12:25 pm Networking, Coffee & Dessert in the Expo (Commonwealth Hall)
1:30 pm Opening Remarks
Rivka Gewirtz Little, Research Director, Global Payment Strategies, IDC
1:35 INTRODUCTORY USE CASE: How Can AI and ML Help Prevent Money Laundering?
Vishal Gossain, Vice President, AML/ATF Analytics, Global Risk Management, Scotiabank; MIT Computer Science and Artificial Intelligence Laboratory
Scotiabank is leveraging innovative ways to detect money laundering, while reducing false positives to create efficiencies. This session will provide a high-level overview of how AI can help regulators and FIs in detecting money laundering, while protecting
the consumer’s data privacy.
2:00 KEYNOTE: Building a Responsible AI/ML Program in Financial Services
Jason Wittenbach, PhD, Manager, Machine Learning, Lead Researcher in Deep Learning Explainability, Capital One
Co-Developed with Dave Castillo, PhD, Managing Vice President & Head of Center for Machine Learning, Capital One
This talk will discuss how Capital One is building a Responsible AI program, and offer best practices and insights for other financial services organizations. It will discuss best practices and a framework for Responsible AI in finance, including
research in explainable AI; working with multidisciplinary experts; cross-functional internal working groups; and partnering with academics and others to ensure the advancement of the responsible use of AI/ML in a way that prioritizes the well-being
of customers and humans.
2:30 USE CASE: Principles of an AI-Ready Analytics Organization: Experience from Fraud Management
Zsolt Sapi, Senior Vice President, Global Independent Fraud Risk Management, Citibank
Companies are adopting advanced analytics and AI to automate and “smarten” their decision-making process. This transformation changes the way analytics teams operate; to keep delivering value they have to be better in cooperating with
other functions and more embedded in business strategy management. The presentation will focus on the key levers of effectiveness for analytics teams to successfully navigate in the new business environment. Lessons learnt from a global, complex
organization such as Citi could help analytics leaders and their business partners designing their analytics organization and set priorities in order to successfully embed advanced analytics and AI initiatives.
2:55 USE CASE: Using AI to Monitor AI: A Framework for Concept Drift Detection
Wally Lo Faro,
PhD, Vice President Data Science, Operations and Technology, Mastercard
Inevitably, a machine learning model’s performance will decline over time but how do we know when it’s time to refresh the model? We present an AI-based alerting system that informs when a model’s scores are being assigned in an
anomalous way.
3:20 Networking Break in the Expo (Commonwealth Hall)
4:05 USE CASE CO-PRESENTATION: Conversational AI: Creating Fran
During our presentation, we will share how we improved members’ digital experience as well as employee productivity by bringing key employees together representing member service, digital experience, and IT departments. We will talk you through
the development of Fran, our conversational AI-driven, member service chatbot. Learn how we developed this innovative solution, from idea to launch, including lessons learned, and best practices.
Ben Maxim, Assistant Vice
President, Software Development, MSU Federal Credit Union
Ami
Iceman-Haueter, Assistant Vice President, Research Digital Experience, MSU Federal Credit Union
Ashleigh Ashbrook, Assistant Vice President, eServices, Member Digital Experience, MSU Federal Credit Union
4:35 PANEL: Using Artificial Intelligence and Machine Learning to Predict Consumer Behavior for Financial Institutions
This discussion will focus on the current state of applications of Artificial Intelligence and Machine Learning in Financial Institutions. We will talk about both in-progress as well as fully developed solutions using AI/ML which various FIs are currently
utilizing to predict consumer behavior. We will discuss about the relative advantages of the different algorithms as well as challenges faced by FIs in their application. Discussion points:
- Are more advanced “black box” AI/ML algorithms suitable for consumer behavior prediction? Are there any in-production cases which have demonstrated success?
- What are the challenges that FIs face (e.g. data, implementation systems, regulatory scrutiny) in these implementations?
- How do we overcome ethical and bias related concerns with the black box models?
Moderator: Vishal Gossain, Vice President, AML/ATF Analytics, Global Risk Management, Scotiabank; MIT Computer Science and Artificial Intelligence Laboratory
Panelists: Jason Wittenbach, PhD, Manager, Machine Learning, Lead Researcher in Deep Learning Explainability, Capital One
Alex Muller, MBA, Senior Vice President, Entrepreneur in Residence, Synchrony Financial
Ben Maxim, Assistant Vice
President, Software Development, MSU Federal Credit Union
Ami
Iceman-Haueter, Assistant Vice President, Research Digital Experience, MSU Federal Credit Union
5:05 Networking Reception in the Expo (Commonwealth Hall)
6:30 Meetup Groups (Cityview)
7:30 Close of Day 2
FRIDAY, OCTOBER 25
Cityview 2
7:45 am Registration Opens
8:00 Continental Breakfast (Harborview Foyer)
8:15 am - 12:30 pm Plenary Keynote Sessions (Harborview)
12:30 Networking, Coffee & Dessert in the Expo - Last Chance for Viewing (Commonwealth Hall)
1:45 Opening Remarks
Rivka Gewirtz Little, Research Director, Global Payment Strategies, IDC
1:50 USE CASE CO-PRESENTATION: Enabling Advanced Analytics Implementations at Enterprise Level; Tech and Business Perspectives
Srinivaas Krovvidy, PhD, Head, Advanced Analytics Enablement, Enterprise Data, Fannie Mae
Prabhakar Bhogaraju, MBA, Vice President, Digital Products, Fannie Mae
As enterprises leverage AI and ML technologies, it is important to make sure appropriate resources and support structure are made available. This includes: Managing Enterprise Data Lake as a shared service; Providing guidance in assessing appropriate
use cases; Developing best practices and patterns for deploying AI and ML solutions and frameworks for integrating with enterprise applications; and Creating forums for close collaboration with business and analytics teams. This presentation will
be very helpful as enterprises transition from their initial exploratory phase of developing AI and ML pilots and proof-of-concepts to deploying enterprise wide solutions. It provides several key ideas for implementing scalable and repeatable
solutions integrating with other enterprise applications. The co-presenters represent the business and technology sides and will share how the tech team provided agility and flexibility in developing and testing their models through our advanced
analytics capabilities and services..
2:20 USE CASE: Machine Learning for Data Quality Management on Big Data Platform
Jennifer Yang,
MBA, Head, Data Management and Data Governance, Enterprise Data Technology, Wells Fargo
Traditional rule-based data quality management methodology is costly and hard to scale in the big data environment. It requires subject matter experts within business, data and technology domains. Machine learning technique-based data quality management
methodology enables a cost-effective and scalable way to manage data quality for a large amount of data. The presentation will discuss a use case that demonstrates how the machine learning techniques can be used in the data quality management
on the big data platform in the financial industry.
2:45 Creating a Learning System: Using AI to Build Consumer Experiences that Continuously Improve
Alex Muller, MBA,
Senior Vice President, Entrepreneur in Residence, Synchrony Financial
AI is poised to revolutionize financial services – analysts even predict that it will disrupt the industry changing how trillions of dollars flow. However financial institutions are getting this wrong. They are trying to “tack on”
machine learning to existing systems and user experiences, completely missing the larger opportunity. This session will explain why you need to design for ML/AI across all aspects of the solution. The session will also demonstrate how AI maximizes
the potential for reduced risk/fraud, increase profitability, while providing fair, transparent and legally compliant credit offerings.
3:10 Networking Break (Plaza & Harbor Level Atriums)
3:25 Anonymous ID Stitching
Sridhar Raman,
Product Development Leader, Intuit, Inc.
Anonymous ID stitching is a gnarly problem that is pervasive within FinTech where a person can browse websites and applications either as an anonymous user or as a logged in user. The problem is generally exacerbated by handling these multiple personas
in different siloed systems, and thereby leading to a dichotomy of data. This session will explore how Intuit solves Anonymous ID stitching by leveraging AI, ML, and its Design4Delight process to deliver deeply personalized experiences within
its products.
3:45 Collaborative Filtering for Personalized Content Discovery
Siddharth Mehrotra, Vice President, Technology, Citi
One of the biggest challenges for large sell-side firms producing massive amounts of proprietary content (research, market commentary, data & analytics, etc.) is to help their clients sort through it and discover new content that is customized
for their taste. In this talk we will discuss how collaborative filtering-based recommender systems, that rely on implicit user feedback, can help solve these problems using real-world examples. This presentation tackles a problem that is often
overlooked when it comes to the use of AI in Financial Services. How can we deliver customized market intelligence to users based on their unique interest, that can help them make better decisions?
4:05 USE CASE CO-PRESENTATION: Smart Index Management with AI... Making Smart Index Smarter
Shihui (Sophie) Chen,
Data Scientist, Machine Intelligence Lab, Nasdaq, Inc.
Xuyang (Bill) Lin, Senior
Data Scientist, Machine Intelligence Lab, Nasdaq, Inc.
The financial service industry has been asking the same question for the last decade: how to provide the same level of performance with a much lower cost. In response to this, the global capital markets witnessed an aggressive growth of passive investment.
Its market share has more than doubled in the past 10 years. Our team will explain the challenges of smart index management, and illustrate our application of advanced analytics, machine learning, and optimization for smart portfolio construction.
4:25 PANEL: The AI Identity: Applying Advanced Analytics to Digital Entity Management
Financial Institutions are seeking new ways of managing digital identity to prevent fraud attacks while enhancing experience. AI lies at the heart of emerging digital identity management strategies. Specifically, crunching a wide variety and volume
of data with advanced analytics changes the game in assessing entity risk and establishing credentials. In this session, we’ll discuss:
- The role of AI in entity risk assessment
- Driving federated digital identity with data-driven analytics
- Using AI to bridge onboarding, authentication and fraud monitoring
Moderator: Rivka Gewirtz Little, Research Director, Global Payment Strategies, IDC
Panelists: Zsolt Sapi, Senior Vice President, Global Independent Fraud Risk Management, Citibank
Vishal Gossain, Vice President, AML/ATF Analytics, Global Risk Management, Scotiabank; MIT Computer Science and Artificial Intelligence Laboratory
Stuart Vaeth, Vice President, Digital Identity, Cyber & Intelligence Solutions, Mastercard
Michael Diamond, Executive Director, Product Management, Digital Authentication and Authorization, JP Morgan Chase
4:55 Close of AI World 2019