ai & machine learning in finance, banking & insurance



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

OPENING DAY SEMINARS,
WELCOME RECEPTION & ATTENDEE BREAKOUT ROUNDTABLES


ROUNDTABLE: What is Better for Introducing AI to Larger Companies – Centralized COE vs Distributed Collective Development

Muller_AlexModerator: Alex Muller, MBA, SVP, Chief Product Officer, Synchrony Financial


ROUNDTABLE: Enabling Advanced Analytics Implementations at Enterprise Level – Tech and Business Perspectives

Krovvidy_SrinivasModerators: Srinivaas Krovvidy, PhD, Head, Advanced Analytics Enablement, Enterprise Data, Fannie Mae


Bhogaraju_PrabhakarPrabhakar Bhogaraju, MBA, Vice President, Digital Products, Fannie Mae


ROUNDTABLE: Solving Anonymous ID Stitching – Handling Multiple Personas in Silo-ed Systems

Raman_SridharModerator: Sridhar Raman, Product Development Leader, Intuit Inc


ROUNDTABLE: AI and the Financial Crime Arms War: Pairing Consortium Data with Advanced Analytics

Little_RivkaModerator: Rivka Gewirtz Little, Research Director, Global Payment Strategies, IDC


Thursday, October 24

7:45 am Registration Opens

8:00 Continental Breakfast

8:50 - 12:15 pm Plenary Keynote Sessions

12:15 pm Networking, Coffee & Dessert in the Expo

AI GOVERNANCE: AI/ML IMPLEMENTATION
FRAMEWORK IN FINANCIAL SERVICES

1:30 pm Opening Remarks

Rivka Gewirtz Little, Research Director, Global Payment Strategies, IDC

1:35 AI Executive to be Announced

2:00 KEYNOTE: Building a Responsible AI/ML Program in Financial Services

Castillo_DaveDave Castillo, PhD, Managing Vice President & Head of Center for Machine Learning, Capital One

Dr. Dave Castillo will discuss how Capital One is building a Responsible AI program, and offer best practices and insights for other financial services organizations. Dr. Castillo 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.

2:20 USE CASE: Artificial Intelligence Implementation Framework – Creating Governance and Process to Prioritize AI/ML Projects Across an Enterprise

Mobasseri_RaminRamin Mobasseri, PhD, Artificial Intelligence Lead, Innovation Group, Wells Fargo

Many financial organizations are struggling with an effective methodology and framework to deliver AI within banking meta-use cases. How can AI “must haves” be delivered efficiently whilst complying with data governance and guidelines? Can AI always help? How can you prove conclusively in an exploration mode before investing on expensive projects? Got Big Rocks? Where can you start the prioritization of meta-use cases to optimize ROI in revenue, cost avoidance and savings?

Principles of an AI-Ready Analytics Organization: Use Cases

2:40 USE CASE: Principles of an AI-Ready Analytics Organization: Experience from Fraud Management

Sapi_ZsoltZsolt 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.

3:00 USE CASE: Using AI to Monitor AI: A Framework for Concept Drift Detection

LoFaro_WalterWally 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

Getting Started with AI: Customer Experience & Predicting Behavior

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.

Maxim_BenBen Maxim, Assistant Vice President, Software Development, MSU Federal Credit Union


IcemanHaueter_AmiAmi Iceman-Haueter, Assistant Vice President, Research Digital Experience, MSU Federal Credit Union


Ashbrook_AshleighAshleigh 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?

Gossain_VishalModerator: Vishal Gossain, Vice President, AML/ATF Analytics, Global Risk Management, Scotiabank; MIT Computer Science and Artificial Intelligence Laboratory


Castillo_DavePanelists: Dave Castillo, PhD, Managing Vice President & Head of Center for Machine Learning, Capital One


Muller_AlexAlex Muller, MBA, Senior Vice President, Chief Product Officer, Synchrony Financial


5:05 Session Break

5:35 Networking Reception in the Expo

6:35 Meetup Groups

7:35 Close of Day 2

FRIDAY, OCTOBER 25

7:45 am Registration Opens

8:00 Continental Breakfast

8:50 - 12:45 pm Plenary Keynote Sessions

12:45 Networking, Coffee & Dessert in the Expo - Last Chance for Viewing

Smarter Enterprise Using AI:
Building New & “Tacking on” to Legacy Systems

1:45 Opening Remarks

Rivka Gewirtz Little, Research Director, Global Payment Strategies, IDC

1:50 AI Executive to be Announced

2:20 USE CASE: Machine Learning for Data Quality Management on Big Data Platform

Yang_JenniferJennifer 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.

2:40 Creating a Learning System: Using AI to Build Consumer Experiences that Continuously Improve

Muller_AlexAlex Muller, MBA, Senior Vice President, Chief Product Officer, 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

Making Sense of Data Using AI:
Anonymous ID Stitching & Personalized Content DISCOVERY

3:25 Anonymous ID Stitching

Raman_SridharSridhar 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

Mehrotra_SiddharthSiddharth 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?

Portfolio Management and Smart Indexes

4:05 USE CASE CO-PRESENTATION: Smart Index Management with AI... Making Smart Index Smarter

Chen_SophieShihui (Sophie) Chen, Data Scientist, Machine Intelligence Lab, Nasdaq, Inc.


Lin_XuyangXuyang (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.

Trusted Digital Identity, Fraud and AML: Where Can AI/ML Help?

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

Little_RivkaModerator: Rivka Gewirtz Little, Research Director, Global Payment Strategies, IDC

Sapi_ZsoltPanelists: Zsolt Sapi, Senior Vice President, Global Independent Fraud Risk Management, Citibank


Gossain_VishalVishal Gossain, Vice President, AML/ATF Analytics, Global Risk Management, Scotiabank; MIT Computer Science and Artificial Intelligence Laboratory


4:55 Close of AI World 2019