Agenda

Predictive Analytics World for Financial 2021

May 24-28, 2021


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.

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

Workshops - Wednesday, May 19th, 2021

7:15 am
Workshop:

Full-day: 7:15am – 2:30pm 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
7:30 am
Workshop:

Full-day: 7:30am – 3:30pm 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
Robert MuenchenUniversity of Tennessee
Manager of Research Computing Support
University of Tennessee
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Workshops - Thursday, May 20th, 2021

8:00 am
Workshop:

Full-day: 8:00am – 3:00pm PDT

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

The Workshop Description will be available shortly.
Instructor
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
8:00 am
Workshop:

Full-day: 8:00am – 3:00pm 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
8:00 am
Workshop:

Full-day: 8:00am – 3:00pm 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
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Workshops - Friday, May 21st, 2021

7:15 am
Workshop:

Full-day: 7:15am – 2:30pm 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
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Predictive Analytics World for Financial - Virtual - Day 1 - Monday, May 24th, 2021

8:45 am
David Stephenson Ph.D.DSI Analytics
Author and Founder
DSI Analytics
9:00 am
MACHINE LEARNING WEEK KEYNOTE
Lessons from: Amazon

Amazon's vision is to be earth's most customer-centric company. This talk explores how the Alexa Hybrid Science team in Pittsburgh, PA applies a customer-centric lens to cutting-edge machine learning research. The team is responsible for developing on-device Alexa automatic speech recognition models to provide a faster, more reliable Alexa experience. Our research includes neural network compression techniques, end-to-end spoken language understanding and optimizing machine learning for edge devices.

Session description
Speaker
Nathan SusanjAmazon.com
Applied Science Manager
Amazon
9:50 am

The quality of any machine learning or deep learning model depends on the values that define the model structure and corresponding hyperparameters. Many practitioners may find themselves investing countless hours manually searching for the right model and related hyperparameter values. Some use highly inefficient grid search methods. Others will use simple random sampling, which actually works fairly well. But alone, this method only offers a globalized search, and other sampling methods may be better suited to the job. 

Why not use machine learning to automate the search for the best model? 

This presentation details an advanced approach that uses both global and local search strategies that can be evaluated in parallel to ensure a quick and efficient exploration of the decision space. In the case of this presentation, a genetic algorithm (GA) will be examined for the global search because the selection and crossover aspects of a GA distinguish it from a purely random search. A generating set search (GSS) will be used to greedily search the local decision space. 

Session description
Sponsored by
SAS
Speaker
Robert BlanchardSAS
SAS Senior Data Scientist.
SAS
10:10 am
Room change
10:20 am

Healthcare risk adjustment is the most impactful financial mechanism to account for the differences in risk in members that enroll with different carriers. In the ACA program alone, billions of dollars are moved each year between health plan carriers. The calculations rely on risk assessment programs which are typically based on linear regression. Simplicity, understandability, and transparency are paramount considerations in the modeling due to the zero-sum nature of risk adjustment. This brings us to an important question: if healthcare expenditures by member are non-linear (which in fact they are), is the additional complexity of machine learning worth it to model these more accurately?


This session provides an overview of this mathematics that is critical to healthcare financials in each line of business (commercial, medicare, medicaid) and also compares and contrasts traditional methods such as linear regression against machine learning variants that involve use of neural networks. We will also discuss the considerations involved in picking the right tools for a couple of applications related to risk assessment.

Session description
Speaker
Syed MehmudWakely Consulting Group
Principal & Senior Consulting Actuary, ASA
Wakely Consulting Group, Society of Actuaries
11:05 am
Break & Expo Hall
11:30 am
Case Study: ABN AMRO

Hardly a week goes by without news of money laundering affecting this or that financial institution. As gatekeepers of the financial system, banks play a crucial role in finding and reporting instances of financial crime. In this talk, we describe the challenges of detecting money laundering and outline why employing machine learning techniques is critically important. We will discuss some of the challenges that we faced at ABN AMRO as well as the solutions we came up with, including a method to deal with changes in feature space while running models in production and an in-house-developed model explainability technique.

Session description
Speaker
Edo van UitertABN Amro
Senior Data Scientist/Team Lead
ABN Amro
12:15 pm

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

12:45 pm
End of Day 1

Predictive Analytics World for Financial - Virtual - Day 2 - Tuesday, May 25th, 2021

8:00 am

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

8:55 am
Tom AlbyEuler Hermes
Chief Digital Transformation Officer
Euler Hermes
9:00 am

A significant amount of research is conducted on the distribution of talent. Talent dispersion and clustering in different industries such as software engineering is a well-known phenomenon and is discussed widely in academic literature and public media. This talk explores how the pandemic has affected the dispersion of talent and how companies are addressing these changes in the post-vaccine world.

Session description
Speaker
Tarun SoodThe Vanguard Group
Head of Data and Analytics
Vanguard
9:50 am

Predictive analytics reaches out into more and more areas of business, industrial, and research applications. The sheer number of different algorithms and technologies is staggering. In this presentation we give a top-level review of the most popular tree-based algorithms now easily available to anyone through Minitab. From individual trees to powerful modern ensembles, we highlight their strengths, weaknesses, uses, and limitations, especially when compared to the conventional modeling techniques like multiple linear and logistic regression. We illustrate the flexibility of the modern tree-based predictive analytics by building a series of models to predict power generation of a solar energy power plant. In this case, gradient boosting ensemble achieves 20% more accuracy compared to the conventional regression. In addition, the winning model provides great insights into the nature of multivariate dependencies.

Session description
Sponsored by
Minitab
Speaker
Mikhail GolovnyaMinitab
Senior Advisory Data Scientist
Minitab
10:10 am
Room change
10:20 am
Case Study: Bank of America Merrill Lynch

Low signal to noise ratios often lead to the inclusion of a large number of variables in Machine Learning models relying on regularization to overcome variance and overfitting problems. Deep learning models also tend to lead to a large number of estimation parameters, but depending on the setup of the problem, the available, or relevant, data points for training may be limited or too costly to obtain. We will discuss and show examples on how we have applied different techniques from feature elimination using backward greedy or feature importance based on SHAP values, to dimensionality reduction using (beta-) Variational Auto Encoders to address the issue.

Session description
Speaker
George PapaioannouBank of America Merill Lynch
Director, Senior Trading Strategist
Bank of America Merrill Lynch
11:05 am
Break & Expo Hall
11:30 am
Case Study: Thomson Reuters

Thomson Reuters produces timely financial news and alerts that is used by many of our corporate and institutional customers. This is extracted from multiple sources, including corporate disclosures, and presented as an ongoing series of alerts and bulletins. One of the major challenges in this kind of financial work is the lack of accurate or weak training data which required us to employ various innovative data cleaning techniques. We also discuss our NLP-based production system that makes strong use of a BERT pre-trained model in combination with an unsupervised strategy, as well as some of the engineering challenges around making the system operate adequately in real-time.

Session description
Speakers
Ian KnopkeThomson Reuters
Senior Data Scientist
Thomson Reuters
Viktoriia SamatovaThomson Reuters
Head of Technology Innovation
Thomson Reuters
12:15 pm

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

12:45 pm
End of Day 2

Predictive Analytics World for Financial - Virtual - Day 3 - Wednesday, May 26th, 2021

8:00 am

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

8:55 am
Tom AlbyEuler Hermes
Chief Digital Transformation Officer
Euler Hermes
9:00 am
Special Plenary

Models generalize best when their complexity matches the problem.  To avoid overfit, practitioners usually trade off accuracy with complexity, measured by the count of parameters.  But this is surprisingly flawed.  For example, a parameter is equivalent to one "degree of freedom" only for regression; it can be > 4 for decision trees, and < 1 for neural networks.   Worse, a major source of complexity -- over-search — remains hidden.  The vast exploration of potential model structures leaves no trace on the final (perhaps simple-looking) model, but has outsized influence over whether it is trustworthy.

I’ll show how Generalized Degrees of Freedom (GDF, by Ye) can be used to measure the full complexity of algorithmic modeling.  This allows one to fairly compare very different models and be more confident about out-of-sample accuracy.  GDF also makes clear how seemingly complex ensemble models avoid overfit, and lastly, reveals a new type of outlier -- cases having high model influence.

Session description
Speaker
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
9:50 am

Deep learning models for forecasting and planning have shown significant promise for handling multiple variables, uncovering hidden patterns, and producing accurate forecasts. However, as one might expect, deep learning models are also complex and rife with pitfalls. Since these techniques often seem like a ‘black box,’ managers -- both technical and nontechnical backgrounds -- can find them hard to master.
In this session, Senior Data Scientist, Javed Ahmed will focus on the intuition behind various deep learning approaches, explore how managers can tackle highly complex models by asking the right questions, and evaluating the models with familiar tools.
Attendees at the Metis session will leave with the tools to:

●      Identify types of forecasting applications that can benefit from deep learning 
●      Broadly understand deep learning approaches relevant to forecasting 
●      Understand pitfalls related to deep learning approaches, and why simpler models may work better
●      Evaluate the results of a forecasting program

Session description
Sponsored by
Metis
Speaker
Javed AhmedMetis
Senior Data Scientist
Metis
10:10 am
Room change
10:20 am
Case Study: Paychex

The shock of a pandemic can be mitigated by developing a plan for being proactive with modeling that can inform decision making. At Paychex we developed a comprehensive COVID-19 insights package released in March 13 that was then transformed into a real time indicator site that included predictive modeling for client losses.

Session description
Speakers
Michael LyonsPaychex
Manager, Data Science
Paychex, Inc.
Jing Zhu
Risk Modeling Analyst
Paychex Inc.
11:05 am

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

11:30 am
Case Study: Goldman Sachs

Algorithmic trading revolves around optimal scheduling, when and how to trade each asset of a portfolio. The schedules produced, however, should not be static; being able to optimally adapt to changing intraday market conditions and analysing effectively other exogenous information is of paramount importance. In this talk, we focus on how machine learning methods like RNNs, LSTMs and decision trees, used in conjunction with classical dynamical system techniques, such as kalman filters, can enrich the toolbox available for real-time algorithmic trading.

Session description
Speakers
Michael SteliarosGoldman Sachs
Managing Director
Goldman Sachs
Andreas Petrides PhDGoldman Sachs
Executive Director, Quantitative Execution Services
Goldman Sachs
12:15 pm
End of Day 3

Predictive Analytics World for Financial - Virtual - Day 4 - Thursday, May 27th, 2021

8:00 am

Grab your real coffee and share experiences virtually with your peers to explore the new challenges of operating in a largely virtual world. Just like pre-show breakfast in a regular conference you’ll join a “round table” with seven fellow attendees and see where the conversation takes you.

8:55 am
Tom AlbyEuler Hermes
Chief Digital Transformation Officer
Euler Hermes
9:00 am
EXPERT PANEL

Despite some dystopian predictions, Machine Learning and AI are not about to replace humans completely. But there's concern and confusion about how best to integrate people and machine learning algorithms. Should you have your algorithms recommend actions and allow people to override them? Should you balance human judgment alongside your algorithms, integrating them into a coherent whole? What about capturing the know-how and experience of your people so you can embed it in the algorithm itself? There are many ways to proceed, each with trade-offs around accuracy, organizational adoption, ethics and legality. This panel will discuss the various ways you can combine people and algorithms and maximize the value of both in your operations.

Session description
Moderator
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
Speakers
Val CareyPaychex
Data Scientist
Paychex, Inc.
Keith McCormick
Data Science Consultant, Trainer, Author, and Speaker
Natalia ModjeskaInfo-Tech Research Group
Research Director
Omdia (part of Informa Tech)
9:50 am

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

10:10 am
Room change
10:20 am
Case Study: Safety National Casualty Corporation

The Safety National Casualty Corporation is the leader in Excess Workers' Compensation. Predictive Analytics is used internally to identify those claims that have potential of exceeding high-cost levels in years. Structured claim data, such as claimant age, nature of injury and body parts, provide fundamental features for predicting claim severity. However, in many cases, they are not informative and distinguishing enough to identify extremely sever claims which are rare in the claim data pool. To address this issue, we explore the unstructured claim data – claim notes, and investigate how different text mining techniques can be used to extract predictive insights from claim notes.

Session description
Speakers
Carrie Lu Ph.D.Safety National Casualty Corporation
Senior Data Scientist
Safety National Casualty Corporation
Bala Venkatram BalantrapuSafety National Casualty Corporation
Data Scientist
Safety National Casualty Corp
11:05 am

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Session description
11:30 am
Case Study: Axis Capital

Predictive Analytics is transforming commercial insurance underwriting. It delivers more accurate and real-time risk assessment which helps underwriters efficiently acquire information, prepare more accurate quotes, and speed up and even automate the underwriting process. This talk presents one practical application of predictive analytics on building the triage process to help underwriters prioritize their attention on the most valuable submissions.

Session description
Speaker
Min Yu
Lead Data Scientist
Axis Capital
12:15 pm
End of Day 4

Predictive Analytics World for Financial - Virtual - Day 5 - Friday, May 28th, 2021

8:55 am
Tom AlbyEuler Hermes
Chief Digital Transformation Officer
Euler Hermes
9:00 am
KEYNOTE
Humanitarian Applications of ML

A global NGO identifies and rehabilitates tens of thousands of survivors of human trafficking and slavery situations. Different situations necessitate different rehabilitation “journeys.” We analyzed case worker notes from check-ins with survivors to help understand how specific programs, and especially “trauma-informed” programs, might be affecting survivors’ emotional wellbeing and likelihood to finish their journeys. We found generally negative associations between survivor sentiment and interactions with the justice system, and generally positive associations with training programs, especially for rebuilding social support networks and economic empowerment. These findings have implications for journey planning as the NGO tries to scale up its worldwide operations.

Session description
Speaker
Muneeb AlamQuantumBlack, a McKinsey company
Specialist, Data Science
QuantumBlack, a McKinsey company
9:25 am
KEYNOTE
Humanitarian Applications of ML

There are many non-profit organizations trying to help develop and build economic opportunities for families in developing countries around the world by aiding individuals in starting and maintaining small businesses. Vision Fund International is one such organization that uses micro-finance loans to jump start business opportunities in these countries. However, these developing countries do not have the credit institutions that countries in the developed world have, which leaves banks restricted on their ability to make loan decisions. Analytical modeling approaches are widely used in major banks in developed countries to efficiently make these decisions. This talk details a project worked on in partnership with Vision Fund International that develops scorecard models for use in loan decisions and the impact it is having on the growth of economies around the world.

Session description
Speaker
Aric LaBarrInstitute for Advanced Analytics at NC State University
Associate Professor of Analytics
Institute for Advanced Analytics at NC State University
9:55 am
The Session Description will be available shortly.
Session description
Sponsored by
Coursera
10:00 am

Join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

10:20 am
Case Study: HSBC

Our Next Best Action journey commenced with the simple aspiration of delivering smarter personalised experiences at the moment of need for our customers. While the aspiration was simple, orchestrating NBA is a long-distance race often fraught with challenges, twists in the road, and steep hills to climb. There are many moving parts to orchestrate the right experiences specially when customers adapt their own non-linear paths, as they move through the different aspects of life events. But how does one set in motion a truly effective execution? In this session, Harphajan would share the art & science of his journey in executing Next Best Action strategy within retail financial services especially where customer relationships evolve over long periods of time

Session description
Speaker
Harphajan SinghHSBC
Head Of Analytics
HSBC
11:05 am

Take a break or join your fellow practitioners in this interactive session where you can exchange approaches to shared challenges and hear how your peers are tackling similar issues.

Session description
11:30 am
Case Study: Visa

One of the critical trends that is impacting customer experience is driving personalization and relevancy in various customer touch points. Customer expectation is being reset everyday thanks to ultra-personalized experiences driven by Google, Facebook and now even some FinTechs. There is a constant hunt for 'better data' that can enhance our understanding of the customer preferences. Payments, in addition to being a means of closing commerce transactions, is in fact, a very rich source of data. Especially for credit and debit cards (which enjoy extremely high usage in NA), every single transactions comes with rich metadata (point of sale device, use of mobile wallets, merchant categories, location, etc.). This spend data provides unique insights into customer preferences and trends and can be transformed into actionable personas. Merchants and Financial Institutions can leverage this data to build personalized digital customer experiences that can delight the customer translating into high customer value. This talk goes over the structure on how we should look at the digital customer experience stack and how payments data & analytics layer can enhance the customer experience.

Session description
Speaker
Abhishek Joshi ‘AJ’Visa
Senior Director
Visa Consulting & Analytics
12:15 pm
End of Conference
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All times are Pacific Daylight Time (PDT/UTC-7)