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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
Workshops - Sunday, June 18th, 2023
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.
Workshops - Monday, June 19th, 2023
Full-day: 8:30am – 4:30pm PDT
This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).
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.
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.
Predictive Analytics World for Financial - Las Vegas - Day 1 - Tuesday, June 20th, 2023
Machine Learning Week Founder Eric Siegel will welcome you to the event and kick things off.
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.
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.
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.

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.
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.
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.
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.

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.
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.
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!
Predictive Analytics World for Financial - Las Vegas - Day 2 - Wednesday, June 21st, 2023
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.
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.
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.
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
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.
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.
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.
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.
Workshops - Thursday, June 22nd, 2023
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.
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.
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.