October 29-November 2, 2017
New York
The premier machine learning conference
Click here for upcoming PAW events

All level tracks Blue circle workshops are for All Levels
Red triangle workshops are Expert/Practitioner Level


Agenda Overview – Financial – October 29-November 2, 2017
Pre-Conference Workshops: Sunday, October 29, 2017
Full-day Workshop
Big Data: Proven Methods You Need
to Extract Big Value

Vladimir Barash, Graphika
Morning Session Workshop
R Bootcamp: For Newcomers to R
Max Kuhn, RStudio
Full-day Workshop
R for Machine Learning:
A Hands-On Introduction

Max Kuhn, RStudio

DAY 1, Monday, October 30, 2017
(PAW Business runs in parallel on this day - dual registration required)
Conference Moderator: Steven Ramirez, Beyond the Arc

All Sessions will Take Place in Room 1E14, Located in Hall 1E
Exhibit Hall Hours are Monday 8:00am - 7:00pm and Tuesday 8:00am - 3:30pm

8:00-8:45am Registration
Room: Hall 1E
8:00-9:10am Networking over Coffee
Room: Exhibit Hall
9:10-10:00am
Keynote
 Chatbots, Robo-Advisors, and AI, Oh My! Predictive Analytics and Machine Learning Case Studies for Financial Services and Fintech

Steven Ramirez, Beyond the Arc
10:00-10:30am Exhibits & Morning Coffee Break
Room: Exhibit Hall

Book Signing with Eric Siegel, author of The Power to Predict who Will Click, Buy, Lie, Or Die
10:30am-11:15am Analytics strategy
Case Study: Ernst & Young
Growing Customer Relationship Value through Analytics

Joe Kruse, Senor Manager, Ernst & Young LLP.
11:20am-11:40am Insurance claims and fraud
Case Study: Plymouth Rock
How to Prevent Medical Abuse in Automobile Injury Claims Through Predictive Analytics

Li Yang, Plymouth Rock
11:40-12:00pm Analytical methods
Case Study: John Hancock
Black Box vs. White Box, Single Model vs. Stratified - Who What Where When Why How

Vishwa Kolla, John Hancock Insurance
12:05-1:40pm Lunch
Room: Exhibit Hall
1:40-2:35pm
Keynote
Case Study: Signifyd
Real-Time Fraud Detection: Strategies for Speed and Actionability

Julia Minkowski, Signifyd (formerly Fiserv)
2:40-3:00pm Insurance; project management
Case Study: John Hancock
Analytics Capstone Projects: Embedding Analytics Throughout Your Organization

Robert M Horrobin, John Hancock
Michael Thurber, Elder Research
3:05-3:25pm Risk management; analytical methods
Improving Credit Scoring with Hierarchical Bayesian Modeling
Wen Shi, Concord Advice
Dongyang Fu, Concord Advice
3:25-3:55pm Exhibits & Afternoon Coffee Break
Room: Exhibit Hall
3:55-4:15pm Trading
Enhancing The Human Trader with Predictive Analytics and Context Sensitive Data
Tom Doris, OTAS Technologies
4:15-4:45pm Social data for trading
Case Study: Twitter
Leveraging Alternative Data Sources to Gain a Critical Competitive Advantage

Stephen Morse, Formerly at Twitter
4:45-5:30pm Deep learning for porfolio management
Predictive Modeling Using Deep Learning with TensorFlow
Aaron Goldenberg, Independent Quantitative Consultant
5:30-7:00pm Networking Reception
Room: Exhibit Hall
7:00pm Dinner with Strangers
Meet at Registration

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DAY 2, Tuesday, October 31, 2017
(PAW Business runs in parallel on this day - dual registration required)
Conference Moderator: Steven Ramirez, Beyond the Arc

All Sessions will Take Place in Room 1E14, Located in Hall 1E
Exhibit Hall Hours are Monday 8:00am - 7:00pm and Tuesday 8:00am - 3:30pm

8:00-8:45am Registration
Room: Hall 1E
8:00-8:45am Networking over Coffee
Room: Exhibit Hall
8:45-8:50am Welcome from the Event Master-of-Ceremonies
Steven Ramirez, Beyond the Arc
9:00-9:40am
Keynote
Case Study: XL Catlin
Model Deployment - If You Build It, Will They Come?

James Gottshall, XL Catlin
9:40-10:00am Diamond Sponsor Presentation
Opportunity - Driven Enterprise: Turning Business On Its Head

Krishna Kallakuri, diwo
10:00-10:45am Analytics strategy
Case Study: MetLife
Pragmatic Analytics for Financial Services

Michael Grandy, MetLife
10:45-11:15am Exhibits & Morning Coffee Break
Room: Exhibit Hall


Book Signing with Seth Stephens-Davidowitz, author of Everybody Lies
and former Google data scientist



Book Signing with Eric Siegel, author of The Power to Predict who Will
Click, Buy, Lie, Or Die
11:15-11:35am Alternative data sources
Google Searches and the Market
Seth Stephens-Davidowitz, Author, Everybody Lies and former Google data scientist
11:40-12:00pm Trading
Wall Street and the New Data Paradigm
Anasse Bari, New York University
12:00-1:15pm Lunch
Room: Exhibit Hall
Trading; analytics tactics
1:15-2:10pm Special Featured Session
How to Tell if Your Market Timing System Will Work: A New Measure of Model Quality

John Elder, Elder Research, Inc.
Expert Panel Shared with PAW Business in Room 1E10
2:15-3:00pm Expert Panel
Women in Predictive Analytics: Opportunities and Challenges

Moderator: Anne Robinson, Verizon Wireless
Panelists:
Tracie Coker Kambies, Deloitte Consulting LLP
Julia Minkowski, Signifyd (formerly Fiserv)
Pallavi Yerramilli, The Trade Desk
3:00-3:30pm Exhibits & Afternoon Coffee Break
Room: Exhibit Hall
3:30-4:15pm Analytics strategy
Case Study: Citigroup
Predictive Analytics in Today's Era of Digital, Machine Learning, and AI:
A Financial Industry Perspective

Yulin Ning, Citigroup
4:15-5:00pm Litigation prediction - analytics in law
Case Study: Gallagher Bassett
Workers' Compensation Litigation Propensity Predictive Analytics

Mei Najim, Gallagher Bassett
Gary Anderberg, Gallagher Bassett

Post-Conference Workshops: Wednesday, November 1, 2017
Full-day Workshop
The Advanced Data Preparation Bootcamp: Whip your Data into Shape
Dean Abbott, Abbott Analytics
Full-day Workshop
The Best and the Worst of Predictive Analytics:
Machine Learning Methods and Common Data Science Mistakes

Dr. John Elder, Elder Research, Inc.
Full-day Workshop
Spark on Hadoop for Machine Learning: Hands-On Lab
James Casaletto, MapR Technologies


Post-Conference Workshop: Thursday, November 2, 2017
Full-day Workshop
Supercharging Prediction with Ensemble Models
Dean Abbott, Abbott Analytics

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© 2017 Predictive Analytics World
Produced by Prediction Impact, Inc. and Rising Media, Inc.

Predictive Analytics Company           Predictive Analytics Event Producer