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 five 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 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
Room: Sienna
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.

Sunday, June 18, 2023 – Red Rock Casino Resort & Spa, Las Vegas

Full-day: 8:30am – 4:30pm PDT

Intended Audience: People who want to use R to make predictions and discover valuable relationships in their data.
Knowledge Level: An introductory knowledge of R and machine learning is helpful, but not required.

Workshop Description

R offers a wide variety of machine learning (ML) functions, each of which works in a slightly different way. This one-day, hands-on workshop starts with ML basics and takes you step-by-step through increasingly complex modeling styles. This workshop makes ML modeling easier through the use of packages that standardize the way the various functions work. When finished, you should be able to use R to apply the most popular and effective machine learning models to make predictions and assess the likely accuracy of those predictions.

The instructor will guide attendees on hands-on execution with R, covering:

  • A brief introduction to R’s tidyverse functions, including a comparison of the caret and parsnip packages
  • Pre-processing data
  • Selecting variables
  • Partitioning data for model development and validation
  • Setting model training controls
  • Developing predictive models using naïve Bayes, classification and regression trees, random forests, gradient boosting machines, and neural networks (more, if time permits)
  • Evaluating model effectiveness using measures of accuracy and visualization
  • Interpreting what “black-box” models are doing internally

Hardware: Bring Your Own Laptop
Each workshop participant is required to bring their laptop

Schedule

  • Workshop starts at 8:30am PDT
  • AM Break from 10:00 – 10:15am PDT
  • Lunch Break from 12:00am – 12:45pm PDT
  • PM Break: 2:15 – 2:30pm PDT
  • End of the Workshop: 4:30pm PDT

Instructor

Jared P. Lander, Chief Data Scientist, Lander Analytics

Jared P. Lander is Chief Data Scientist of Lander Analytics, the Organizer of the New York Open Statistical Programming Meetup and the New York and Government R Conferences, an Adjunct Professor at Columbia Business School, and a Visiting Lecturer at Princeton University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. Jared oversees the long-term direction of the company and acts as Lead Data Scientist, researching the best strategy, models and algorithms for modern data needs. This is in addition to his client-facing consulting and training. He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization and statistical computing. He is the author of R for Everyone (now in its second edition), a book about R Programming geared toward Data Scientists and Non-Statisticians alike. The book is available from Amazon, Barnes & Noble and InformIT. The material is drawn from the classes he teaches at Columbia and is incorporated into his corporate training. Very active in the data community, Jared is a frequent speaker at conferences, universities and meetups around the world.



Instructor
Jared LanderLander Analytics
Chief Data Scientist
Lander Analytics
4:30 pm
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Workshops - Monday, June 19th, 2023

8:30 am
Room: Red Rock Ballroom A
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).

Monday, June 19, 2023 – Red Rock Casino Resort & Spa, Las Vegas

 Full day: 8:30am – 4:30pm PDT

 Intended Audience: Interested in the fundamentals of modern machine learning techniques.

Knowledge Level: For this introductory-level workshop, it is helpful for attendees to already be familiar with the basics of probability and coding.

Free Book! Each attendee will be reimbursed by the organizers for the cost of buying a copy of Dr Elder’s “Handbook of Statistical Analysis and Data Mining Applications 1st Edition” (up to $50, receipt required).

Companion Workshop: This workshop is the perfect complement for Dr. Elder’s other one-day PAW workshop, “The Deadly Dozen: The Top 12 Analytics Mistakes and the Techniques to Defeat Them,” although both workshops stand alone and may be taken in either order.

Workshop Description

This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).
Predictive analytics has proven capable of generating enormous returns across industries – but, with so many machine learning modeling methods, there are some tough questions that need answering:

  • How do you pick the right one to deliver the greatest impact for your business, as applied over your data?
  • What are the best practices along the way?
  • How do you make it sure it works on new data?

In this workshop, renowned practitioner and hugely popular instructor Dr. John Elder will describe the key inner workings of leading machine learning algorithms, demonstrate their performance with business case studies, compare their merits, and show you how to select the method and tool best suited to each predictive analytics project.

Attendees will leave with an understanding of the most popular algorithms, including classical regression, decision trees, nearest neighbors, and neural networks, as well as breakthrough ensemble methods such as bagging, boosting, and random forests.

This workshop will also cover useful ways to visualize, select, reduce, and engineer features – such as principal components and projection pursuit. Most importantly, Dr. Elder reveals how the essential resampling techniques of cross-validation and bootstrapping make your models robust and reliable.

Throughout the workshop day, Dr. Elder will share his (often humorous) stories from real-world applications, highlighting mistakes to avoid.

If you’d like to become a practitioner of predictive analytics – or if you already are and would like to hone your knowledge across methods and best practices – this workshop is for you.

What you will learn:
  • The tremendous value of learning from data
  • How to create valuable predictive models with machine learning for your business
  • Best Practices, with real-world stories of what happens when things go wrong

Why Attend?

View Dr. Elder describing his course, “The Best of Predictive Analytics,” in this brief video:

Schedule

  • Workshop starts at 8:30am PDT
  • AM Break from 10:00 – 10:15am PDT
  • Lunch Break from 12:00am – 12:45pm PDT
  • PM Break: 2:15 – 2:30pm PDT
  • End of the Workshop: 4:30pm PDT

Special offer: Register for both this workshop as well as Dr. Elder’s other one-day PAW workshop, “The Deadly Dozen: The Top 12 Analytics Mistakes and the Techniques to Defeat Them” (complementary but not required), and also receive his co-authored book Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions.


Instructor

Dr. John Elder, Founder and Chair, Elder Research

John Elder leads America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Washington DC, Maryland, North Carolina, and London. Dr. Elder co-authored books on data miningensembles, and text mining — two of which won book-of-the-year awards. John was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker. Dr. Elder is an (occasional) Adjunct Professor of Engineering at UVA, and was named by President Bush to serve 5 years on a panel to guide technology for national security.

Instructor
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Room: Red Rock Ballroom D
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.

Monday, June 19, 2023 – Red Rock Casino Resort & Spa, Las Vegas

Full-day: 8:30am – 4:30pm PDT

Intended Audience: Managers, decision makers, practitioners, and professionals interested in a broad overview and introduction
Knowledge Level: All levels

Workshop Description

Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalizeit. To get to that point, your business must follow a gold standard project management process, one that is holistic across organizational functions and reaches well beyond executing the core number crunching itself.

At this workshop, you will gain a deep understanding of the concepts and methods involved in operationalizing machine learning to deliver business outcomes. This workshop focuses on the elements of a machine learning project that define and scope the business problem, ensure that the result is useful in business terms, and help deliver and operationalize the machine learning outcome. Based on CRISP-DM – the most well known, established industry standard management process for machine learning – this course does not dive into the core machine learning technology itself, but focuses instead on how machine learning must be applied in order to be effective. Attendees will have opportunity to apply what they learn to real-life scenarios.

Key Topics:

  • Apply machine learning to business operations through the structure of CRISP-DM
  • Use decision modeling to understand real-world business problems in a way that allows machine learning to be applied effectively
  • Take a decision-centric and business-focused approach to machine learning projects
  • Evaluate and deploy machine learning results to minimize the gap between analytic insight and business improvement

Coverage of the CRISP-DM Project Management Phases:

  • An overview of CRISP-DM and its basic approach
  • Discuss and demonstrate the importance of decisions in the Business Understanding phase
  • Introduce and teach decision modeling as a way to assess the situation and set goals for the project
  • Discuss decision-centric approach to Data Understanding phase of CRISP-DM
  • Discuss decision-centric approach to Data Preparation and Modeling phase of CRISP-DM
  • Discuss decision-centric approach to Evaluation phase of CRISP-DM
  • Discuss decision-centric approach to Deployment phase of CRISP-DM
  • Brief discussion of technical deployment options
  • Specification of business rules in a decision model to turn predictive analytic into prescriptive one
  • Importance of ongoing decision (not just model) monitoring and management

Learning Objectives:

  • Frame data quality and other data needs in decision-centric terms
  • Evaluate machine learning outputs against decision models to determine business value
  • Use decision models to show how machine learning results can be captured and compared
  • Understand different ways in which machine learning can be used to improve decision-making
  • Read and understand a decision model built using the Decision Model and Notation (DMN) standard
  • Develop basic decision modeling skills for use on machine learning projects
  • Understand how decision modeling complements CRISP-DM as an approach to machine learning
  • Understand technology architecture required for machine learning project deployment
  • Be able to use decision model to frame organizational and process change requirements for machine learning project
  • Understand use of business rules and business rules technology alongside machine learning

Schedule

  • Workshop starts at 8:30am PDT
  • AM Break from 10:00 – 10:15am PDT
  • Lunch Break from 12:00am – 12:45pm PDT
  • PM Break: 2:15 – 2:30pm PDT
  • End of the Workshop: 4:30pm PDT

Instructor

James Taylor, CEO, Decision Management Solutions

James Taylor is the CEO of Decision Management Solutions and is a leading expert in how to use business rules and analytic technology to build decision management systems. He is passionate about using decision management systems to help companies improve decision-making and develop an agile, analytic and adaptive business. He provides strategic consulting to companies of all sizes, working with clients in all sectors to adopt decision-making technology. James is an expert member of the International Institute for Analytics and is the author of multiple books and articles on decision management, decision modeling, predictive analytics and business rules, and writes a regular blog at JT on EDM. James also delivers webinars, workshops and training. He is a regular keynote speaker at conferences around the world.


Instructor
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Room: Red Rock Ballroom G
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.

Monday, June 19, 2023 – Red Rock Casino Resort & Spa, Las Vegas

Full day: 8:30am – 4:30pm

Important note: Each workshop participant is required to bring their own laptop. 
Intended Audience: Anyone who wishes to learn how to create deep learning systems using PyTorch, TensorFlow, Keras, and other popular software libraries.
Knowledge Level: Basic knowledge of machine learning terminology. Minimal programming experience with a C-family language such as Python, C/C++, C# or Java is recommended but not required.

Workshop Description

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. It’s a hands-on class; you’ll learn to implement and understand both deep neural networks as well as unsupervised techniques using PyTorch, TensorFlow, Keras, and Python. Just as importantly, you’ll learn exactly what types of problems are appropriate for deep learning techniques, and what types of problems are not well suited to deep learning.

The instructors, Prerna and Bardia, take part in applying cutting-edge Large Language Models and other custom models to address various industries’ needs. They will be sharing case studies and examples from their experience during the workshop. Workshop participants will access much of the same state-of-the-art training material used for this work at Microsoft. Along the way, James will cover case studies detailing large-scale deployments for their internal clients that have generated astounding ROIs.

During the day, workshop attendees will gain the following practical hands-on experience:

  • How to prepare, normalize, and encode data for deep learning systems.
  • How to install deep learning libraries including TensorFlow, Keras, and PyTorch, and the pros and cons of each library.
  • How to create deep learning predictive systems for various kinds of data: classical business data, time series data (such as sales data), image data (such as the famous MNIST dataset for handwriting recognition), and text/document data (such as legal contracts). These datasets are a great place to start – however, for the more experienced attendee, even more challenging, “next level” datasets, such as for object recognition, will be optionally available.

This workshop assumes you have a basic knowledge of machine learning terminology but does not assume you are a machine learning expert. Some theory will be presented but only enough to help you understand how to make a practical, working deep learning system. This is a code-based workshop, so some programming experience will be helpful. However, beginners will be able to follow along but may have to work a bit harder to keep up.

Hardware: Bring Your Own Laptop

Important note: Each workshop participant is required to bring their own laptop.

You are encouraged to bring a Windows 10 or 11 laptop if you have one available, but a Mac laptop will work as well.

Details regarding laptop options as well as pre-install instructions for both platforms will be updated closer to the event, since the new, forthcoming PyTorch 2.0, coming out spring 2023, will be utilized — but for now, you can access last year’s details here.

Assistants will also be on hand to help attendees with hardware/software issues.

Attendees receive an electronic copy of the course materials and related code at the conclusion of the workshop.

Schedule

  • Workshop starts at 8:30am
  • Morning Coffee Break at 10:30am – 11:00am
  • Lunch at 12:30pm – 1:15pm
  • Afternoon Coffee Break at 3:00pm – 3:30pm
  • End of the Workshop: 4:30pm

Instructors:

Bardia Beigi, Applied Scientist II, Microsoft 

Bardia Beigi works at Microsoft as an Applied Scientist II in the Industry AI group delivering AI/ML based solutions to various industries within Azure. Bardia has a master’s degree in Computer Science from Stanford University, as well as Bachelor of Applied Science in Engineering Physics from the University of British Columbia. In his spare time, Bardia enjoys traveling, trying out new dessert spots, and learning new life hacks.

Prerna Singh, Applied Scientist II, Microsoft 

Prerna Singh is currently working as an Applied Scientist II in the Industry AI group @Microsoft where she develops machine learning-based solutions for different industrial verticals including finance and sustainability. Before joining Microsoft, she obtained her master’s degree in Electrical and Computer Engineering with a concentration on Machine Learning from Carnegie Mellon University (CMU). Prerna is passionate about machine learning, NLP and deep Reinforcement Learning. Besides work, Prerna enjoys traveling, Zumba and hiking in her free time.

Instructors
Bardia BeigiMicrosoft
Senior Applied Scientist
Microsoft
Prerna SinghMicrosoft
Applied Scientist II
Microsoft
4:30 pm
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Predictive Analytics World for Financial - Las Vegas - Day 1 - Tuesday, June 20th, 2023

8:00 am
Room: Red Rock Foyer
Registration & Networking Breakfast
8:45 am
Room: Red Rock Ballroom B

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

Session description
Speaker
Eric SiegelMachine Learning Week
Conference Founder
Machine Learning Week
8:50 am
Room: Red Rock Ballroom B
GENERATIVE AI

Join Kian Katanforoosh, CEO and Founder of Workera, as he explores the profound impact of generative AI on the workforce and the evolution of personalized learning. With his rich experience in AI education and having taught AI to over 4 million people with Prof. Andrew Ng as founding member of DeepLearning.AI, Kian's insights are uniquely informed and forward-thinking.

In this keynote, Kian will unravel how generative AI is reshaping learning, emphasizing the pivotal role of skills data in actualizing personalized learning. He will discuss the harnessing of this data to tailor learning experiences to individual needs, track progress, identify improvement areas, and improve workforce management.

Drawing from his experiences as a founding member of DeepLearning.AI and the co-creator of the popular Stanford Deep Learning Class, Kian will share his vision for a future where learning is as unique as we are. Attend this session for a deep dive into the convergence of AI, personalized learning, and workforce transformation.


Session description
Speaker
Kian KatanforooshWorkera
CEO
Workera.ai
9:15 am
Room: Red Rock Ballroom B
GENERATIVE AI

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
Room: Red Rock Ballroom B

In this session, Juan Acevedo, a machine learning architect at Google, will discuss how organizations can leverage Google Cloud's generative AI products to bring value to their businesses in a secure environment and responsibly. Juan will cover the following topics: What can you do right now with Google Cloud technology Responsible generative AI This session is for those who are interested in learning more about generative AI and how it can be used to improve their businesses.

Session description
Sponsored by
Google Cloud
Speaker
Juan AcevedoGoogle Cloud
Enterprise Machine Learning Architect
Google Cloud
10:00 am
Room: Charleston Ballroom
Exhibits & Morning Coffee Break
10:30 am
Room: Red Rock Ballroom G
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
Room: Red Rock Ballroom G
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
Head of Financial Products
MPOWER Financing
12:10 pm
Room: Charleston Ballroom
Lunch - Seating available at The Veranda and Red Rock Terrace
1:30 pm
Room: Red Rock Ballroom G

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
Senior Vice President, Advanced Analytics and Practical Applications
Safety National Casualty Corporation
2:15 pm
Room: Red Rock Ballroom G

Identifying key variables that impact risk or explain behavior has always been a core challenge in finance. In the arms race to do this at exponentially decreasing amounts of time for exponentially increasing amounts of data, the emerging technology of quadratic unconstrained binary optimization is a powerful weapon. 

Whether you are an algorithmic trader trying to detect sparse signals in a large and noisy market, a credit underwriter trying to interpret a vast number of features, or a payments processor trying to identify bad actors - finding the simplest solutions with large numbers of variables and not a lot of training samples is a hard but crucial task. We will explain how QUBO works, why it is well-suited for the task, and why LightSolver can be your partner.

Session description
Sponsored by
Lightsolver
Speaker
Eric Ben-ArtziLightsolver
Head of Financial Solutions
Lightsolver
2:35 pm
Short Break
2:40 pm
Room: Red Rock Ballroom G
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
Room: Charleston Ballroom
Exhibits & Afternoon Break
3:55 pm
Room: Red Rock Ballroom B

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 SiegelMachine Learning Week
Conference Founder
Machine Learning Week
4:40 pm
Short Break
4:45 pm
Room: Red Rock Ballroom B

Competition for top new analytics talent is fierce. While tech and other corporate giants are indeed vacuuming up new grads from top schools, not all great students can or want to go that route. The challenge is to put yourself in a position to attract and land them. It can be done, even if you're not a well-known brand. In this session, you will learn what works from a leader of the analytics program ranked #2 in the world by QS for the past three years. Even if you are from a giant firm, you're still competing for talent. You will come away with ideas to help you gain an advantage!

Session description
Speaker
Vashishtha DoshiUCLA Anderson School of Management
Manager of Industry Relations
UCLA Anderson School of Management
5:30 pm
Room: Charleston Ballroom
Networking Reception in the Exhibit Hall
6:45 pm
Dinner with Friends - sign up in the app

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

8:00 am
Registration & Networking Breakfast
8:45 am
Room: Red Rock Ballroom B
The Session Description will be available shortly.
Session description
Speaker
Eric SiegelMachine Learning Week
Conference Founder
Machine Learning Week
8:55 am
Room: Red Rock Ballroom B

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
Room: Red Rock Ballroom B

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 Analytics Strategy
Elder Research
10:00 am
Short Break
10:05 am
Room: Red Rock Ballroom G
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
Room: Charleston Ballroom
Exhibits & Morning Coffee Break
11:15 am
Room: Red Rock Ballroom G
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
Room: Charleston Ballroom
Lunch - Seating available at The Veranda and Red Rock Terrace
1:15 pm
Room: Red Rock Ballroom B

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
Short Break
2:05 pm
Room: Red Rock Ballroom B

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
Speakers
Cheryl AbundoAmazon Web Services
Principal Solutions Architect
Amazon Web Services
Usha JagannathanMcKinsey & Company
Former Principal Engineer
McKinsey & Company
William Komp
Principal Data Scientist
Komplytics LLC
Jennifer Schaff Ph.D.Elder Research
Vice President of Commercial Services
Elder Research
3:00 pm
Room: Charleston Ballroom
Exhibits & Afternoon Break
3:30 pm
Room: Red Rock Ballroom D
Insurance Applications

Predictive analytics can be applied to pricing car insurance to help insurance companies determine appropriate rates for individual policyholders.  By analyzing large datasets of historical insurance claims and policyholder data, predictive models can be developed to estimate the likelihood of future claims based on various factors such as age, gender, driving history, location, credit score, and type of vehicle.  More recently, insurers have also been able to use the rich datasets from telematics sensors.  This session will provide an overview of how predictive analytics can be applied to pricing car insurance to help insurance companies make more informed pricing decisions and reduce risk, ultimately leading to improved profitability and customer satisfaction.

Session description
Speaker
Isaac EspinozaRoot Insurance
Strategy & Reinsurance
Root Insurance
4:15 pm
Short Break
4:20 pm
Room: Red Rock Ballroom G
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
Room: Summerlin D
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.

Thursday, June 22, 2023 – Red Rock Casino Resort & Spa, Las Vegas

Full-day: 8:30am – 4:30pm PDT

Intended Audience: Practitioners who wish to learn how to execute on machine learning with Python.

Knowledge Level: Prior experience programming in any language (for machine learning or otherwise) and fundamental knowledge of machine learning concepts. This workshop can serve as your first experience executing on machine learning hands-on – or, if you already have such experience with a language or platform other than Python, this workshop will serve to facilitate your “lateral move” to Python.

Workshop Description

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. Python provides a great way for machine learning newcomers to begin their hands-on practice, or for experienced practitioners to augment their growing battery of tools.

Note regarding deep learning. Python’s popularity has recently grown even further since it is the most common way to access leading deep learning solutions such as TensorFlow. Note that this workshop day does not cover deep learning, since it serves first-time users by covering a broader, foundational range of traditional machine learning methods. However, this training does provide helpful groundwork for the “Hands-On Deep Learning in the Cloud” workshop scheduled for later in the same week.

During this full-day training workshop, instructor Clinton Brownley – a data scientist at WhatsApp and formerly Facebook, where he gained extensive experience leading internal machine learning trainings – will take you on your first steps with Python, guiding you through challenging hands-on exercises to employ various machine learning capabilities within Python and apply them on real world datasets.

A comprehensive training. The training agenda covers the end-to-end machine learning process, including loading and preprocessing data, building, tuning, and comparing classification and regression models, making predictions, and reporting on model performance.

Topics include:

  • Data preprocessing
  • Cross-validation
  • Model tuning
  • Model evaluation
  • Regression
  • Classification
  • Ensemble methods

Diverse application areas. This workshop’s hands-on exercises cover various applications of predictive modeling that serve to mitigate harm and save money, including: gambling, hospital readmissions, nefarious actor detection, time to failure, and hotel bookings.

Bring your laptop with Python pre-installed. Workshop participants are required to bring their own laptops for use during this hands-on workshop with Python version ≥3.x installed. The primary libraries for the workshop are pandasscikit-learn, and matplotlib, but specific examples may rely on other libraries, so participants should also install seabornpymc3, and jupyter.

Pre-install instructions. The easiest way to have both a compatible version of Python as well as all these required libraries on your laptop is to install the Anaconda Distribution of Python. Please be sure to do prior to the workshop day.

Schedule

  • Workshop starts at 8:30am PDT
  • AM Break from 10:00 – 10:15am PDT
  • Lunch Break from 12:00am – 12:45pm PDT
  • PM Break: 2:15 – 2:30pm PDT
  • End of the Workshop: 4:30pm PDT

Instructor

Clinton Brownley, Data Scientist, WhatsApp

Clinton Brownley, Ph.D., is a data scientist at WhatsApp, where he’s responsible for a variety of analytics projects designed to improve messaging and VoIP calling performance and reliability. Before WhatsApp, Clinton was a data scientist at Facebook, working on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions. As an avid student and teacher of modern analytics techniques, Clinton is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis,” and also teaches Python programming and data science courses at Facebook and in the Bay Area. Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.

Instructor
Clinton BrownleyTala
Lead Data Scientist
Tala
Room: Summerlin E
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.

Thursday, June 22, 2023 – Red Rock Casino Resort & Spa, Las Vegas

Full day: 8:30am – 4:30pm PDT

Intended Audience: Interested in advanced machine learning techniques.

Knowledge Level: For this intermediate-level workshop, it is helpful for attendees to already be familiar with the basics of machine learning methods.

Free Book! Each attendee will be reimbursed by the organizers for the cost of buying a copy of Dr Elder’s “Handbook of Statistical Analysis and Data Mining Applications 1st Edition” (up to $50, receipt required).

Companion Workshop: This workshop is the perfect complement for Dr. Elder’s other one-day PAW workshop, “The Best of Predictive Analytics: Core Machine Learning and Data Science Techniques,” although both workshops stand alone and may be taken in either order.

Workshop Description

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.

This workshop covers:

  • Antidotes: the best practices that overcome the most common flawed practices
  • Intuitive explanations of resampling methods that ensure your models work on new data
  • Practical tips for both the hard and the soft skills that will see your project through to implementation

In this workshop, renowned practitioner and hugely popular instructor Dr. John Elder will survey the most advanced analytics tools in the practitioner’s toolkit, with particular emphasis on resampling tools – such as cross-validation and target shuffling (a method to avert p-hacking devised by Dr. Elder) – which reveal the true accuracy of your models.

Workshop topics also include visualization, feature engineering, global optimization, criteria of merit design, ensembles, and “soft” factors that affect success, such as human cognitive biases. Attendees will also leave with an understanding of the inner workings of the most popular algorithms – including regression, decision trees, nearest neighbors, neural networks, bagging, boosting, and random forests.

Throughout the workshop day, Dr. Elder will share his (often humorous) stories from real-world applications, illuminating the technical material covered.

If you’d like to become a more expert practitioner of predictive analytics, this workshop is for you.

What you will learn:

  • The 12 subtle pitfalls to watch out for on any new project
  • The latest ways to increase the value of predictive models and machine learning for your business
  • How to succeed when your biggest threat is not technology, but people (e.g., resistance to change)

Why Attend?

View Dr. Elder describing his course, “The Deadly Dozen,” in this brief video:

Schedule

  • Workshop starts at 8:30am PDT
  • AM Break from 10:00 – 10:15am PDT
  • Lunch Break from 12:00am – 12:45pm PDT
  • PM Break: 2:15 – 2:30pm PDT
  • End of the Workshop: 4:30pm PDT

Special offer: Register for both this workshop as well as Dr. Elder’s other one-day PAW workshop, “The Best of Predictive Analytics: Core Machine Learning and Data Science Techniques” (complementary but not required), and also receive his co-authored book Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions.

Instructor

Dr. John Elder, Founder and Chair, Elder Research

John Elder leads America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Washington DC, Maryland, North Carolina, and London. Dr. Elder co-authored books on data miningensembles, and text mining — two of which won book-of-the-year awards. John was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker. Dr. Elder is an (occasional) Adjunct Professor of Engineering at UVA, and was named by President Bush to serve 5 years on a panel to guide technology for national security.


Instructor
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Room: Summerlin F
Post Conference Training Workshop

Full-day: 8:30am – 4:30pm PDT

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.

Thursday, June 22, 2023 – Red Rock Casino Resort & Spa, Las Vegas

Full-day: 8:30am – 4:30pm PDT

Intended Audience: Interested in generative AI.

Knowledge Level: This intermediate-level workshop is intended for participants with at least some technical background, such as hands-on coding or familiarity with the basics of machine learning methods.

Workshop Description

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.

Schedule

  • Workshop starts at 8:30am PDT
  • AM Break from 10:00 – 10:15am PDT
  • Lunch Break from 12:00am – 12:45pm PDT
  • PM Break: 2:15 – 2:30pm PDT
  • End of the Workshop: 4:30pm PDT

Instructor

Martin Musiol, Generative AI Expert, GenerativeAI.net 

Long before the buzz surrounding generative AI, Martin Musiol was already advocating for its significance in 2015. Since then, he has been a frequent speaker at conferences, podcasts, and panel discussions, addressing the technological advancements, practical applications, and ethical considerations of generative AI. Martin Musiol is a co-founder of generativeAI.net, a lecturer on AI to over 1000 students, and publisher of the newsletter ‘Generative AI: Short & Sweet’. As a Data Science Manager at Infosys Consulting (previously at IBM), Martin Musiol helps companies globally harness the power of generative AI to gain a competitive advantage.

Instructor
Martin MusiolGenerativeAI.net
Generative AI Expert
GenerativeAI.net
4:30 pm
End of Post-Conference Training Workshops
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