Agenda

Predictive Analytics World for Financial 2023

June 18-22, 2023 l Red Rock Casino Resort & Spa, Las Vegas


To view the full 7-track agenda for the six co-located conferences at Machine Learning Week click here or for the individual conference agendas here: PAW Business, PAW Financial, PAW Healthcare, PAW Industry 4.0, PAW Climate or Deep Learning World.

Session Levels:

Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level

All times are Pacific Daylight Time (PDT/UTC-7)

Workshops - Sunday, June 18th, 2023

8:30 am
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

This one day workshop reviews major big data success stories that have transformed businesses and created new markets.

The Workshop Description will be available shortly.
Instructors
Vladimir BarashGraphika Labs
Chief Scientist
Graphika
Marc SmithConnected Action Consulting Group
Chief Social Scientist
Connected Action Consulting Group
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

Gain experience driving R for predictive modeling across real examples and data sets. Survey the pertinent modeling packages.

The Workshop Description will be available shortly.
Instructor
Jared LanderLander Analytics
Chief Data Scientist
Lander Analytics
4:30 pm
End of Pre-Conference Training Workshops
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Workshops - Monday, June 19th, 2023

8:30 am
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

This workshop dives into the key ensemble approaches, including Bagging, Random Forests, and Stochastic Gradient Boosting.

The Workshop Description will be available shortly.
Instructor
Dean AbbottAbbott Analytics
President
Abbott Analytics
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).

The Workshop Description will be available shortly.
Instructor
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalize it.

The Workshop Description will be available shortly.
Instructor
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

This one-day introductory workshop dives deep. You will explore deep neural classification, LSTM time series analysis, convolutional image classification, advanced data clustering, bandit algorithms, and reinforcement learning.

The Workshop Description will be available shortly.
Instructors
Leo BetthauserMicrosoft
Senior Data Scientist
Microsoft
James McCaffreyMicrosoft
Senior Scientist Engineer
Microsoft
4:30 pm
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Predictive Analytics World for Financial - Las Vegas - Day 1 - Tuesday, June 20th, 2023

8:00 am
Registration & Networking Breakfast
8:45 am

Machine Learning Week Founder Eric Siegel will welcome you to the event and kick things off.

Session description
Speaker
Eric SiegelPredictive Analytics World
Conference Founder
Predictive Analytics World
8:50 am

In this informal but informative presentation, Dr. James McCaffrey – who leads Microsoft Research’s CEO-mandated initiative to transfer deep learning intelligence into all products, services, and supporting systems across the enterprise – will describe six of the latest trends in machine learning and deep neural systems. Today's hottest trends include the deployment of generative AI systems like GPT-x/ChatGPT, Codex, Copilot, and DALL-E – yet they also include innovative deployments of more classical ML methods. The emphasis will be on information that is practical and can drive revenue, not theoretical ideas filled with math equations and Greek letters. Particular emphasis will be given to explaining the difference between systems that require massive resources or expensive licensing, and systems that can be successfully implemented by organizations with a limited budget.

Session description
Speaker
James McCaffreyMicrosoft
Senior Scientist Engineer
Microsoft
9:15 am

Google continues to take a bold and responsible approach to developing and deploying AI through the company’s infrastructure, tools, products, and services. Google brought further AI breakthroughs into the real world through Google Cloud’s launch of the next wave of generative AI across core areas of their business, and new partnerships and programs grounded in Google’s commitment to an open AI ecosystem. At the same time, AI, as a still-emerging technology, poses complexities and risks; and the development and use of AI must address these risks in a structured, transparent, and accountable way. A robust governance structure – and rigorous testing and ethics reviews — is necessary to put responsible AI principles into practice. And with AI regulation coming soon, Jen will share learnings, challenges, and practical tips on how Google is maturing its responsible AI practices, processes, and tools in advance of greater regulatory, global standards, and consumer expectations.

Session description
Speaker
Jen GennaiGoogle
Head of Responsible Innovation, Global Affairs
Google
9:40 am
Sponsored Session
10:00 am
Exhibits & Morning Coffee Break
10:30 am
Uncertainty estimation

Machine models are the most powerful predictors, but they are often black-box models and incorporate uncertainty by nature. Quantifying the ML uncertainty is critical for adding confidence to ML adoptions. Based on reliable uncertainty estimators,  risks of ML underperformance can also be quantified and transferred through insurance solutions. We will demonstrate this application through real case studies of collaborations between ML providers and the insurance industry.

Session description
Speaker
Yuanyuan LiMunich Re
Research Scientist
Munich Re
11:15 am
Short Break
11:25 am
Credit risk
Case Study: MPOWER Financing

International students, DACA, immigrants, and other new Americans represent 13% of the US population and are growing. However, a lack of existing credit history prevents this population from accessing affordable loans. In this case study, we’ll share how MPOWER Financing – a large provider of financing to international and DACA students – uses an innovative, forward-looking credit model based on alternative data and new modeling techniques to provide affordable education financing to people with no or limited credit history.

Session description
Speaker
Mack WallaceMPOWER Financing
Senior Director, Head of Student Loans
MPOWER Financing
12:10 pm
Lunch
1:30 pm

Risk takes on many forms in the insurance industry, Underwriting Risk, Investment Risk, Operational Risk including Reputational Risk, Legal Risk Information Technology Risk & Process Risk. Predictive analytics can be used in almost all aspects of the company to help improve outcomes and reduce the associated risk.

Session description
Speaker
William WIlkinsSafety National Casualty Corporation
VP, Chief Risk and Analytics Officer
Safety National Casualty Corporation
2:15 pm
Sponsored Session
2:40 pm
Risk management

Machine Learning (ML) models are quickly becoming ubiquitous and widely applied in banking. However, the use for high risk applications such as credit underwriting demands higher requirements in terms of model explainability and testing beyond performance evaluation. In this talk, I am going to share how we approach model interpretability without the potential pitfall of post-hoc explainers by employing inherently interpretable machine learning. Beyond interpretability, comprehensive testing to ensure model robustness and resilience are required for high risk applications. I'm going to discuss our approach illustrated by a tool that we recently released, PiML (Python Interpretable Machine Learning), an integrated environment to develop and validate machine learning models for high risk applications.

Session description
Speaker
Jie ChenWells Fargo
Head of Decision Science and Artificial Intelligence Model Validation
Wells Fargo
3:25 pm
Exhibits & Afternoon Break
3:55 pm

Rexer Analytics began surveying data scientists in 2007. This year's Data Science Survey is a collaboration with "Predictive Analytics" author and conference series founder Eric Siegel. In this session, Karl and Eric will present preliminary results from this year's survey. Topics will include algorithm choices, data science job satisfaction trends, deep learning, model deployment, and deployment challenges.

Session description
Speakers
Karl RexerRexer Analytics
President
Rexer Analytics
Eric SiegelPredictive Analytics World
Conference Founder
Predictive Analytics World
4:45 pm
Algorithmic trading; advanced methods
Case Study: Barclays

Reinforcement Learning (RL) agents proved to be a force to be reckoned with in many complex games like Chess and Go. Financial firms are leveraging the power of RL, given it potential to automate all the steps involved in algorithmic trading. However, it is quite challenging to understand and interpret a RL based models.

This talk focuses on an approach to understand and interpret Reinforcement Learning (RL) based trading strategies. We first briefly introduce the concept of reinforcement learning in the context of algorithmic trading, followed by demonstration of an RL- interpretability infrastructure. We then discuss possible derived outcomes of using this infrastructure when applied to trading a market instrument.

Session description
Speaker
Hariom TatsatBarclays
Vice President
Barclays
5:30 pm
Networking Reception
7:00 pm
End of Conference Day 1

Predictive Analytics World for Financial - Las Vegas - Day 2 - Wednesday, June 21st, 2023

8:00 am
Registration & Networking Breakfast
8:45 am

Enjoy some machine learning laughs with Evan Wimpey, a predictive analytics comedian (and we're not just talking about his coding skills). No data topic is off-limits, so come enjoy some of the funniest jokes ever told at a machine learning conference.*

* Note the baseline.

Session description
Speaker
Evan WimpeyElder Research
Director of Strategic Analytics
Elder Research
8:55 am

Join us for a dynamic and entertaining keynote session on Data Storytelling with Gulrez Khan, Data Science leader at PayPal. Gulrez is known for infusing his presentations with humor and personal stories, making the learning experience both engaging and enjoyable. You'll be inspired by Gulrez's insights and experience as he guides you through the process of turning numbers into narratives. Discover how to craft compelling stories that bring your data to life, and learn how to share your insights in a way that engages, educates, and inspires your audience. 

Session description
Speaker
Gulrez KhanPayPal
Data Science Lead
PayPal
9:40 am
Sponsored Session
10:05 am
Insurance underwriting

In the life insurance industry, underwriting is an important process to assess the insurability of an individual. Traditional underwriting is often lengthy and expensive due to the requirement of blood and urine tests. The accelerated underwriting process replaces these lab tests with data and machine learning algorithms, thereby saving both time and money for the policyholders. The process made life insurance purchases easier and faster, thereby allowing the industry to serve a wider demographics and improve social good. This talk seeks to provide an overview of the AUW, emerging data sources, best practices, challenges, and regulatory implications.

Session description
Speaker
Gordon YangPacific Life
Actuary & Director - Data Science & Advanced Analytics
Pacific Life
10:50 am
Exhibits & Morning Coffee Break
11:15 am
Credit risk

Retail shops need credit to purchase inventory. Credit dependence is more prominent with small retailers (mom & pop stores).  In India, most of these stores don't use digital transactions or worse, don't even have bank accounts. Due to this, they can't get loans from banks as no credit score is present. In absence of formal credit, retailers depend on distributors (businesses who supply retailers inventory) to extend them line of credit (informal credit). Most of this credit offering is based on historical relations between retailer and distributor with no scientific premise. This credit is based on personal relationships without taking into consideration the actual worth of the shop -- therefore, there are high risks of loan default.

With data access of nearly 7.5MN retailers on real time basis we have enough retail transaction level data & hyper local data to train models that can predict credit worthiness of a store without need for any banking information . In this talk, I intend to explain how we use data along with the ML models to build Credit Score for Retailers. A major financial lender use this score to extend credit to retailer achieving lowest delinquencies in the industry

Session description
Speaker
Rohit AgarwalMobisy
Chief Data Officer
Mobisy Technologies
12:00 pm
Lunch
1:15 pm

ML’s great strength is that example cases are all you need to create a predictive model. The predictions work as long as the underlying process is not tampered with. But clients usually seek more: they yearn to understand the "data-generating machinery” in order to improve the outcome that the model predicts. Yet, this is dangerous without additional external information, including the direction of influence between variables. This talk illustrates how to achieve “peak interpretability” by using influence diagrams to model causal relationships, avoid mistaking correlation for causation, and quantify how outcomes will change when we manipulate key values.

Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
2:00 pm
Sponsored Session
2:15 pm

Model explainability or interpretability is often demanded, posed as a requirement. But not always. Under what circumstances is it pragmatically necessary (or even legally required) -- and, when it is called for, what exactly does it mean? Would it suffice to explain each model prediction by showing what differences in inputs would have changed the prediction? Or must the model be "understood" globally? Is understanding how the model derives its predictions sufficient, even without the why -- that is, without causal explanations for the correlations it encodes?

Join this expert panel to hear seasoned experts weigh in on these tough questions.

Session description
Moderator
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
3:00 pm
Exhibits & Afternoon Break
3:30 pm
Strategy & Reinsurance
The Session Description will be available shortly.
Session description
Speaker
Isaac EspinozaRoot Insurance
Strategy & Reinsurance
Root Insurance
4:15 pm
Banking applications

In this presentation, Finicity lead data scientist Natesh Arunachalam will provide a brief primer to Open Banking. He'll cover why Open Banking is a crucial technology for FinTechs and Banks, why it provides more power to the digitally native consumer, data assets that Open Banking provides, and application of AI in Open Banking, including use cases in risk scoring, NLP, and computer vision.

Session description
Speaker
Natesh ArunachalamFinicity
Lead Data Scientist
Finicity
5:05 pm
End of Conference Day 2
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Workshops - Thursday, June 22nd, 2023

8:30 am
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

Python leads as a top machine learning solution – thanks largely to its extensive battery of powerful open source machine learning libraries. It’s also one of the most important, powerful programming languages in general.

The Workshop Description will be available shortly.
Instructor
Clinton BrownleyWhatsApp
Data Scientist
WhatsApp
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

This one-day session reveals the subtle mistakes analytics practitioners often make when facing a new challenge (the “deadly dozen”), and clearly explains the advanced methods seasoned experts use to avoid those pitfalls and build accurate and reliable models.

The Workshop Description will be available shortly.
Instructor
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Post Conference Training Workshop

Generative AI has taken the world by storm, scaling machine learning to viably generate the written word, images, music, speech, video, and more. To the public, it is by far the most visible deployment of machine learning. To futurists, it is the most human-like. And to industry leaders, it has the widest, most untapped range of potential use cases.

In this workshop, participants will get an introduction to generative AI and its concepts, and techniques. The workshop will cover different techniques for image, text, and 3D object generation, and so forth. Participants will also learn how prompts can be used to guide and generate output from generative AI models. Real-world applications of generative AI will be discussed, including image and video synthesis, text generation, and data augmentation. Ethical considerations when working with generative AI, including data privacy, bias, and fairness, will also be covered. Hands-on exercises will provide participants with practical experience using generative AI tools and techniques. By the end of the workshop, participants will have a solid understanding of generative AI and how it can be applied in various domains.

Instructor
Martin Musiol
Generative AI Expert
GenerativeAI.net
4:30 pm
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All times are Pacific Daylight Time (PDT/UTC-7)