Agenda

Predictive Analytics World for Financial Las Vegas 2020

May 31-June 4, 2020 – Caesars Palace, Las Vegas

Session Levels:

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

Pre-Conference Workshops - Sunday, May 31st, 2020

8:30 am
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This one day workshop reviews major big data success stories that have transformed businesses and created new markets. Click workshop title above for the fully detailed description. 

Session description
Instructor
Marc Smith
Chief Social Scientist
Connected Action Consulting Group
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

Gain experience driving R for predictive modeling across real examples and data sets. Survey the pertinent modeling packages.  Click workshop title above for the fully detailed description. 

Session description
Instructor
Robert MuenchenUniversity of Tennessee
Manager of Research Computing Support
University of Tennessee
4:30 pm
End of Sunday Pre-Conference Training Workshops
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Pre-Conference Workshops - Monday, June 1st, 2020

8:30 am
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning). Click workshop title above for the fully detailed description. 

Session description
Instructor
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

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. Click workshop title above for the fully detailed description. 

Session description
Instructor
James McCaffreyMicrosoft
Senior Scientist Engineer
Microsoft
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

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. Click workshop title above for the fully detailed description. 

Session description
Instructor
Clinton BrownleyWhatsApp
Data Scientist
WhatsApp
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalize it.  Click workshop title above for the fully detailed description. 

Session description
Instructor
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
4:30 pm
End of Monday Pre-Conference Training Workshops
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Predictive Analytics World for Financial - Las Vegas - Day 1 - Tuesday, June 2nd, 2020

8:00 am
Registration & Networking Breakfast
8:45 am
Eric SiegelPredictive Analytics World
Conference Founder
Predictive Analytics World
8:50 am
MACHINE LEARNING WEEK KEYNOTE
Lessons from: Lyft

In this keynote address, Gil Arditi will cover the areas of machine learning development at Lyft, talk about friction points in the model lifecycle – from prototyping and feature engineering to production deployment – and show how Lyft streamlined this process internally. He will also cover a step-by-step example of a model that was recently developed and taken to production.

Session description
Speaker
Gil ArditiLyft
Product Lead, Machine Learning
Lyft
9:15 am
MACHINE LEARNING WEEK KEYNOTE
Lessons from: Google

As principles purporting to guide the ethical development of Artificial Intelligence proliferate, there are questions on what they actually mean in practice. How are they interpreted? How are they applied? How can engineers and product managers be expected to grapple with questions that have puzzled philosophers since the dawn of civilization, like how to create more equitable and fair outcomes for everyone, and how to understand the impact on society of tools and technologies that haven't even been created yet. To help us understand how Google is wrestling with these questions and more, Jen Gennai, Head of Responsible Innovation at Google, will run through past, present and future learnings and challenges related to the creation and adoption of Google's AI Principles.

Session description
Speaker
Jen GennaiGoogle
Head of Responsible Innovation, Global Affairs
Google
9:40 am
The Session Description will be available shortly.
Session description
10:00 am
Exhibits & Morning Coffee Break
10:30 am
Insurance applications
10:30 am - 10:50 am
Case study: ESIS
The Session Description will be available shortly.
Session description
Speaker
Keith HigdonESIS
President
ESIS
Risk management
10:55 am - 11:15 am
Case study: Swiss Reinsurance Company

Understanding risk is a core necessity for any insurer and reinsurer. The dynamically shifting risk landscape with new risks emerging and new regulatory requirements coming into place demands flexible solutions to efficiently steer business, price risks and allocate capital. Swiss Re has developed over the last years a broad range of predictive algorithms to understand risks. These new models have generated on the one hands improvements with respect to traditional models as well as they allow to insure risks that previously were thought to be uninsurable. In this session we will deep-dive into two concrete risk prediction models used to better identify risks in motor insurance as well as in travel insurance.

Session description
Speaker
Christian ElsasserSwiss Re
Manager Data Analytics
Swiss Reinsurance Company Ltd
11:15 am
5-minute transition between sessions
11:20 am
Long-term risk management
11:20 am - 11:40 am
Case study: Safety National Casualty

The Safety National Casualty Corporation is the leader in Excess Workers' Compensation.   Predictive Analytics is used internally to identify those claims that it may not see for 7 to 10 years from today.  But, given the low volume of these occurrences, the individualistic nature of each and the potential financial ramifications,  finding the right balance in identification and action is not just a numbers game.

Session description
Speakers
William WIlkinsSafety National Casualty Corporation
Chief Risk and Data Analytics Officer
Safety National Casualty Corporation
Carrie Lu Ph.D.Safety National Casualty Corporation
Senior Data Scientist
Safety National Casualty Corporation
Real-time architecture (streaming analytics)
11:45 am - 12:05 pm
Case study: ING

Streaming Analytics (or Fast Data processing) is becoming an increasingly popular subject in financial organizations. The reason for this is that customers want to have notifications and advise based on their online behavior and other users' actions. Moreover, fraud detection requires real-time decision making based on transactions data. All these use cases can be covered by a proper architecture for fast data. In this session, I'll discuss the use cases, architecture, and a sample application.

Session description
Speaker
Bas GeerdinkAizonic
CTO
Aizonic
12:05 pm
Lunch
1:30 pm
KEYNOTE
Lessons from: Manulife

This keynote address will focus on real life “Needle in a haystack” problems and why these problems are becoming much more frequent. Two case studies: defect detection as well as fraud detection models from start to finish and their chances of implementation. We’ll discuss what works and what may be holding you back from a successful implementation. The need for several solution strategies and packaging them in one delivery will also be explored.

Session description
Speaker
Richard LeeManulife
Director of Data Science, US EOIT Advanced Analytics & AI
Manulife
2:15 pm
The Session Description will be available shortly.
Session description
2:35 pm
5-minute transition between sessions
2:40 pm
Operationalization
2:40 pm - 3:00 pm
Case study: Donnelley Financial Solutions

In most organizations, the data science teams and the engineering teams build software separately. This can lead to the development of AI solutions that may not fit into the value delivery pipelines built by the engineering teams. Moreover, the feedback loop to continue learning and improving an ML model becomes challenging with this separation of teams. In this talk, you will learn about an approach of building ML and AI solutions that are developed into micro-services, along with micro front-ends, and deployed into dockerized containers for delivery of quick business value to users.

Session description
Speaker
Naveed AsemDonnelley Financial Solutions
Chief Data and Analytics Officer
Donnelley Financial Solutions
Transaction analytics, ML for audit support
3:05 pm - 3:25 pm

For financial professionals and their attorneys, reviewing the details of transactions like mergers and acquisitions is important, but time consuming. Such transactions have specific sub-events, such as potential terminations that are associated with punitive charges. To enable supervised machine learning models to extract such granular information, human taggers must annotate sections of text associated with each sub-event. This talk will cover some of the challenges in creating this type of training data, including how to train on very small data sets and optimize the tag quality.

Session description
Speaker
Leslie BarrettBloomberg
Senior Software Engineer
Bloomberg LP
3:25 pm
Exhibits & Afternoon Break
3:55 pm
Risk management
3:55 pm - 4:15 pm
Case study: Bloomberg

Using several real-world examples, I will illustrate how we use AI to transform the ERM function from an audit-centric role to a value-add function that proactively identifies and mitigates enterprise-wide risks. 

Audience Takeaways:

  • Data, compute and talent required to successfully transform ERM using AI
  • Scaling learnings across the enterprise
  • Benefits of an ERM-focused AI Center of Excellence for enterprise
Session description
Speaker
Alex SanchezBloomberg
Global Head of Risk Strategy and Analytics
Bloomberg
Algorithmic trading
4:20 pm - 4:40 pm
Cse study: Bloomberg

Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both sell-side and buy-side institutions. Techniques like time series analysis, stochastic calculus, multivariate statistics, and numerical optimization are often used by "quants” for modeling asset prices, portfolio construction and optimization, and building automated trading strategies.

Chakri Cherukuri demonstrates how to apply machine learning techniques in quantitative finance, covering use cases involving both structured and alternative datasets. The focus of the talk will be on promoting reproducible research (through Jupyter notebooks and interactive plots) and interpretable models.

Session description
Speaker
Chakri CherukuriBloomberg
Senior Quantitative Researcher
Bloomberg
4:40 pm
5-minute transition between sessions
4:45 pm
Algorithmic trading
Case study: Goldman Sachs

One of the main components of optimally scheduling portfolio trading is the estimation of the intraday and multi-day propagation of risk and market impact. In this talk, we focus on how machine learning techniques like LSTMs, CNNs, random forests and adaptive splines can improve these estimations for better intraday and multi-day trade scheduling. We also provide examples of how these can be used both in pre-trade scheduling, as well as in real-time optimisation.

Session description
Speakers
Michael SteliarosGoldman Sachs
Managing Director
Goldman Sachs
Andreas PetridesGoldman Sachs
Quantitative Researcher
Goldman Sachs
5:30 pm
Networking Reception
7:00 pm
End of first Conference Day

Predictive Analytics World for Financial - Las Vegas - Day 2 - Wednesday, June 3rd, 2020

8:00 am
Registration & Networking Breakfast
8:55 am
KEYNOTE
Lessons from: Fidelity

With data scientists coming with all kinds of backgrounds and experience, how do you know which type you really need to meet your business needs? How many types of data scientist are out there? And where can you find them? What kinds of analytics can they provide for the financial services industry? Drawing from over 25 years of experience in the field with over two decades of management, Victor Lo will introduce a framework for the classification of data scientists and propose a mapping scheme between these talents and various types of projects.

Session description
Speaker
Victor LoFidelity Investments
AI and Data Science Center of Excellence Leader, Workplace Investing
Fidelity Investments
9:40 am
The Session Description will be available shortly.
Session description
10:00 am
5-minute transition between sessions
10:05 am
Project management
10:05 am - 10:25 am
Case study: Wells Fargo

Agile project management methodology has become standard practice in the field of software engineering due to the flexibility and speed it brings to building out new features. But how does agile fit in with data science, a discipline that is famously exploratory in nature? This talk explores lessons learned over the first year of switching to agile project management for data science projects at Wells Fargo, covering the ups and downs, lessons learned and tweaks to the agile process that we found helpful for data science work.

Session description
Speaker
Nathan SusanjWells Fargo
Vice President, Data Science Manager
Wells Fargo
Enterprise deployment
10:30 am - 10:50 am
The Session Description will be available shortly.
Session description
Speaker
Sravan KasarlaThrivent
Chief Data Office
Thrivent
10:50 am
Exhibits & Morning Coffee Break
11:20 am
The cyborg effect
11:20 am - 11:40 am

We continue to hear about machine learning and AI and its disruption within our economy. Data science is no exception as various opinions would suggest the demise of the data science role within a more machine-driven world. In this session, we explore how the need for data science and human intervention has historically always been critical for success in financial services. Yet in a more automated and algorithmic business environment, the need for this human aspect of data science is even more paramount. Case studies and examples will highlight how the human and the machine work together in optimizing the prediction of customer behaviour within financial services.

Session description
Speaker
Richard BoireEnvironics Analytics
Senior Vice President
Environics Analytics
Financial applications; transactional data
11:45 am - 12:05 pm
Case study: Visa

The US has one of the highest penetrations of credit and debit cards. As the usage of card transactions increases, so does our ability to extract strong macroeconomic as well as consumer trends insights. A single card transaction, be it a face to face or an online transaction, comes wit aa tremendous amount of metadata around location, time, device and most importantly merchants. Merchant data in particular gives direct insight into the patterns in consumer behavior and economic winds. 

This talk will dissect transaction data and go over use cases for driving marketing relevancy, reducing fraud, and driving business strategies.

Session description
Speaker
Abhishek Joshi ‘AJ’Visa
Senior Director
Visa Consulting & Analytics
12:05 pm
Lunch
1:15 pm
Client transaction optimization
Case study: Charles Schwab

Our clients' interactions generate, on average, nearly ~3 billion rows of data every month. In order to measure and analyze client behavior related to completing servicing needs, we need to construct a client journey across channels for the transaction.  Client journeys map the universe of interactions from all channels to specific servicing needs  For some channels, this is relatively simple, but online behavior is high volume, messy and ever-changing.  

Leaning on the related concepts underlying pointwise mutual information and TF-IDF, we developed a simple machine learning approach to differentiate between online "noise" and online behavior related to a client's intent to complete a transaction. We will discuss how the approach works, and how it allows us to create consistent measurement of the online experience that is automatic, scalable, and detects intended or unintended shifts in the online experience.

Session description
Speaker
Jodi BlombergCharles Schwab
Managing Director, Enterprise Analytics
Charles Schwab
2:00 pm
The Session Description will be available shortly.
Session description
2:10 pm
5-minute transition between sessions
2:15 pm

This expert panel session aims to shed some insight about Machine Learning and Data Science Career Development in Financial Services. Questions will be fielded from the audience, and could include:

- What are the required skills and career advancement paths of this career?
- How does one gain support from upper management on data science capability development?
- How does one build a high-performing Data Science/Analytics team?
- How do you predict this career path will change in the next 5 to 10 years?

Session description
Moderator
Mei Najim
CSPA, Founder and Lead Data Scientist
Advanced Analytics Consulting Services, LLC
3:00 pm
Exhibits & Afternoon Break
3:30 pm
Portfolio analytics; data sources

The latest formula for making sound investment decisions involves mining new alternative data sources, using predictive analytics, swarm intelligence and high-performance computing. It offers a more robust framework to generate data-driven investment theses. Data – from satellite images of areas of interest, automated drones, people-counting sensors, container ships’ positions, credit card transactional data, email receipt data, jobs and layoffs reports, cell phone location data, social media, news articles, tweets, online search queries – is now the most valuable commodity for Wall Street. Applying predictive analytics to these alternative data sources can help discover and contextualize actionable insights that can produce better predictions. Knowing something that only few others know affords firms a competitive edge and positions them better to forge new strategies.

In this talk, Prof. Anasse Bari of New York University, advisor to leading Wall Street firms and co-author of “Predictive Analytics for Dummies,” explains how alternative data sources and predictive analytics are driving value in the world of finance and how swarm intelligence algorithms are reinventing Wall Street. He will explain emerging algorithms, filter fact from fiction, outline successful use cases that he and his team have recently led (e.g. how social performance and consumer reviews could be used as predictive features, how to derive actionable insights from geospatial images.) Prof. Bari will also present an overview that can help you design and implement viable solutions to generate a “predictive analytics-based investment thesis.” 

Session description
Speaker
Anasse Bari Ph.D.New York University
Professor of Computer Science
New York University
4:15 pm
5-minute transition between sessions
4:20 pm
Credit scoring
Case study: Vision Fund International

There are many non-profit organizations trying to help develop and build economic opportunities for developing countries around the world. Vision Fund International uses microfinance loans to jump start business opportunities in these countries. However, these developing countries don't have the credit institutions that countries like the United States have. That leaves these institutions and banks hampered on their ability to make loan decisions. This case study will go through the work provided to Vision Fund to help them develop a credit scorecard model to better predict default of customers to help them make better data driven decisions.

Session description
Speaker
Aric LaBarrInstitute for Advanced Analytics at NC State University
Associate Professor of Analytics
Institute for Advanced Analytics at NC State University
5:05 pm
End of second Conference Day
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Post-Conference Workshops - Thursday, June 4th, 2020

8:30 am
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

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.  Click workshop title above for the fully detailed description.

Session description
Instructor
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

Gain the power to extract signals from big data on your own, without relying on data engineers and Hadoop specialists. Click workshop title above for the fully detailed description.

Session description
Instructor
James Casaletto
PhD Candidate
UC Santa Cruz Genomics Institute and former Senior Solutions Architect, MapR
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This workshop dives into the key ensemble approaches, including Bagging, Random Forests, and Stochastic Gradient Boosting. Click workshop title above for the fully detailed description.

Session description
Instructor
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

During this workshop, you will gain hands-on experience deploying deep learning on Google’s TPUs (Tensor Processing Units) at this one-day workshop, scheduled the day immediately after the Deep Learning World and Predictive Analytics World two-day conferences.  Click workshop title above for the fully detailed description.

Session description
4:30 pm
End of Post-Conference Training Workshops
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