October 23-27, 2016
New York
Delivering on the promise of data science
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Full Agenda – Financial – October 23-27, 2016

  Day 1: Tuesday, October 25, 2016

8:00-8:45am • Room: Exhibit Area 1A

Registration & Networking Breakfast

8:45-8:50am • Room: 1A24

Welcome from the Event Master-of-Ceremonies

Steven Ramirez
Beyond the Arc

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8:50-9:40am • Room: 1A24

Case Study: Barclays
Big Data Analytics Front-to-Back: Application Case Studies in Financial Services in the Back and Front offices

This talk will focus on issues and opportunities in adopting Big Data technology and supporting predictive analytics in banking applications. We will cover case studies in front and back offices: from consumer-facing applications to drive engagement and revenue to applications in back office functions including Finance, Risk, Treasury and Compliance. With Big Data comes a very strong need to apply the proper Data Governance approach which is a pragmatic necessity and not just a regulatory and controls issue in order to avoid the "data chaos" that many institutions find themselves in once they embark on the Big Data journey.

Usama Fayyad
Chief Data Officer

9:40-10:00am • Room: 1A24

Diamond Sponsor Presentation
4 Ways the Financial Services Industry Can Unlock the Possibilities of Big Data

With current data analytics solutions being expensive, complicated and often not tailored to meet specific needs, many businesses have struggled with harnessing the power of big data. However, utilizing free and open source software, or an integrated infrastructure based on a flexible, open, and customizable architecture, businesses can realize actionable insights at a low cost. Name, Title, EastBanc Technologies will discuss the potential structure of open source software stacks for predictive analytics, how to scale predictive analytics tools, the pros and cons of FOSS and cloud relative to proprietary tools, and ways to overcome the cons.

Eric Hoffman
Vice President
EastBanc Technologies

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10:00-10:30am • Room: Exhibit Area 1A

Exhibits & Morning Coffee Break

10:30-11:15am • Room: 1A24

Expert Panel
Gaining Traction: How to Establish Buy-In for Predictive Analytics

The value proposition is sound to you, but you can't get the go-ahead. Predictive analytics can face numerous organizational hurdles before it is green-lit by the powers that be. These include layperson comprehension, manager trust, and data access authority. The seasoned experts on this panel will guide your path around these obstacles with tidbits, best practices, and the odd colorful story.

Steven Ramirez
Beyond the Arc

Anasse Bari
University Professor of Computer Science
New York University

Wolf Ruzicka
EastBanc Technologies

Sri Raghavan
Aster Product Marketing Director
Teradata Aster

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11:20am-12:05pm • Room: 1A24

Stress Testing, Cross-Enterprise Applications
Predictive Analytics for Stress Testing - Industry Challenge

This presentation will provide a perspective on analytical challenges for stress testing at Financial Institutions. It will highlight the key challenges faced by quantitative analysts in dealing with data and modeling and how predictive analytics can address those challenges. It will also provide insights into how this information can be used for improved decision making: credit analysis, customer targeting, and recoveries.

Sanjay Gupta
Executive Vice President and Head of Model Development
PNC Bank

Namit Sureka
Vice President, Co Head of Banking Analytics & Head of Retail Analytics
EXL Analytics

12:05-1:50pm • Room: Exhibit Area 1A

Lunch in the Exhibit Hall

1:50-2:35pm • Room: 1A24

Accelerating Analytics Maturity in the Financial Services Industry

Analytics have been recognized as one of the most important value drivers in today's business. Most financial companies have created analytics capability in different ways and forms. The benefits are obvious, but sometime it also brings back challenges that many companies have experienced in the past: overlapping, redundancy, and conflicting. Bin Mu, Chief Analytics Officer of MetLife, will discuss how to create efficacy in analytics, and maximize analytics capabilities across business and function.

Bin Mu
VP, Business Analytics

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2:40-3:25pm • Room: 1A24

Risk Management
Case Study: Paychex
Risk Management Algorithms - Paving the Way to Value Creation

Small businesses are under increasing pressure in today's environment which can severely impact critical cash flow in day to day operations. Paychex, as a valuable partner, from payroll and taxes to benefits and retirement, can greatly influence how businesses survive and even thrive in today's current business climate. When you transact almost a trillion dollars a year through soft credit exposure through your ACH pipe network, good models are a critical. Join us as we dive into the Risk Management process and our custom algorithms to both identify key risks and, through mitigating efforts, help businesses meet the needs of their employees when those risks threaten their success.

Frank Fiorille
Sr. Director of Risk Management
Paychex, Inc.

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3:25-3:55pm • Room: Exhibit Area 1A

Exhibits & Afternoon Break

3:55-4:40pm • Room: 1A24

Algorithmic Trading and Beyond
What Data Scientists Can Learn from Quants

Quants were the forebearers to today's data scientist. In this talk, we'll explore lessons from quant trading including how human judgement is often systematically flawed, why humans are biologically wired by evolution to make poor gut decisions, and how data science can help us make better decisions. We'll delve into case studies using contemporary events like the 2008 stock market crash or the Vioxx drug approval scandal to understand some common simple mistakes even data scientists make and the limitations of data science.

Michael Li
The Data Incubator

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4:45-5:30pm • Room: 1A24

Best Practices
Q&A: Ask Dean and Steve Anything (About Best Practices)

Preeminent consultant, author and instructor Dean Abbott, along with Steven Ramirez, field questions from an audience of predictive analytics practitioners about their work, best practices, and other tips and pointers.

Dean Abbott
Co-Founder and Chief Data Scientist

Steven Ramirez
Beyond the Arc

5:30-7:00pm • Room: Exhibit Area 1A

Networking Reception

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  Day 2: Wednesday, October 26, 2016

8:00-8:45am • Room: Exhibit Area 1A

Registration & Networking Breakfast

9:00-9:05am • Room: 1A24

Welcome from the Event Master-of-Ceremonies

Steven Ramirez
Beyond the Arc

9:05-9:55am • Room: 1A24

Case Study: FICO
Fraud Screening for 2/3rds of All Card Transactions: A Consortium and Its Data

FICO scores 65% of all the world's payment card transactions for fraud. It accomplishes this by maintaining a payment card transaction consortium utilized in the production of the Falcon platform, which monitors a majority of the world's payment cards. Maintaining this data consortium poses challenges given the large number of financial services contributors. FICO utilizes analytics ranging from automated statistical testing, massive model robustness tests, outlier analytics, and auto-encoder technologies focused on ensuring data quality and integrity. Learn how FICO tackles the consortium with advanced analytics to monitor new trends in the global payment card space and ferrets out new transaction patterns used to protect your payment card.

Scott Zoldi
Chief Analytics Officer

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10:00-10:45am • Room: 1A24

Case Study: Bizfi
Cross-Enterprise Applications
Leveraging Predictive Analytics on the Path to Fintech Success

Financial technology, or fintech, is helping to create new products and new customer experiences in financial services. Alternative lenders, payment platforms, and other new fintech players are leveraging predictive analytics to architect many of these new offerings. In this session, you'll learn how Bizfi has deployed advanced analytics across several functional areas of the company and transformed their business. Tomo and Steven will discuss Bizfi's experiences, and the best practices that others can apply to accelerate the adoption of predictive analytics in their own organizations.

Steven Ramirez
Beyond the Arc

Tomo Matsuo
Chief Operating Officer

10:45-11:15am • Room: Exhibit Area 1A

Exhibits & Morning Coffee Break

11:15am-12:00pm • Room: 1A24

Predictive Investing (VC)
Case Study: Microsoft Strategy
Predicting Startup Success: Finding the Unicorns among Wildebeests

In this presentation, Mukund from Microsoft Strategy will talk about predicting startup outcomes. Using data from over 74 public and private sources, we attempt to quantify our pipeline of startups, deal flow and portfolio of companies. The predictive analytics techniques we use are to help us determine which startups have a higher likelihood of outperforming the market. We use the research and analytics to help us source better companies, manage our pipeline of deals and help support their portfolio companies to scale and grow.

Mukund Mohan
Microsoft Strategy

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12:00-1:00pm • Room: Exhibit Area 1A

Lunch in the Exhibit Hall

1:15-2:00pm • Room: 1A24

Analytical Methods
Luck, Skill, or Torture? How Target Shuffling Can Tell if What Your Data Says is Real

When you mine past data and find a pattern, to what degree is it real, or chance? Ancient statistics geniuses devised formulas to answer this for special-case scenarios. Yet, their calculus only applies to handmade analyses where a few hypotheses are considered. But modern predictive analytic algorithms are hypothesis-generating machines, capable of testing millions of "ideas." The best result stumbled over in its vast search has a great chance of being spurious, leading to failing models molded to the noise. The good news is an antidote exists! John Elder will reveal a simple breakthrough solution he calls "Target Shuffling", and illustrate how it insures results are reliable, using a real-world engagement in hedge funds as well as examples in medicine, marketing, and gas exploration. Bottom line: Honest data analysis can save experimental science!

Dr. John Elder
CEO & Founder
Elder Research, Inc.

Teredata Logo

2:00-2:10pm • Room: 1A24

Sponsor Presentation
Rapid Execution of Enterprise Scale Machine Learning with Mortgage Data and the R Language

Modern finance requires analytic agility to quickly analyze new data to reduce risk in financial decisions. Learn how some the leading global financial institutions have embraced new analytics, new data and open source R at scale to rapidly analyze, model and operationalize insights uncovered in mortgage data.

Roger Fried
Sr. Data Scientist

2:15-3:00pm • Room: 1A24

Case Study: OCBC Bank (Singapore)
Challenging the Dogma: Predictive Analytics Problems Poorly Tackled by Theoretical Finance

Years of theoretical research and applications to finance have established a veil of dogma on what types of models are acceptable by industry players and regulators. This is founded on certain model assumptions that are highly flawed empirically but that are interpretable and elegant mathematically. One example is the historical simulation Value at Risk (or any related dynamic models) dogma, for which the mere suggestion of an alternative empirically-based approach stretches beyond the comfort zone of many people in banking. This presentation shows how predictive analytics can be used as a powerful tool in solving problems poorly tackled in finance. Two specific cases will be discussed, one involving assigning ratings to fixed-income securities that are not yet rated by rating agencies, and another on the profiling of time deposit accounts.

Marcelo Labre
Head of Analytics and Market Data

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3:00-3:30pm • Room: Exhibit Area 1A

Exhibits & Afternoon Break

3:30-4:15pm • Room: 1A24

Price Prediction
Case Study: Hopper
Buy or Wait? Consumer-Friendly Airfare Prediction or How the Bunny Saves You Money

Buying a plane ticket is a time-consuming and frustrating process that often leaves the consumer unhappy. Flight prices are less transparent and fluctuate more than almost anything else a consumer buys, even though airfare is one of the most expensive purchases for a typical family.

Our goal at Hopper is to bring more transparency to pricing, by giving consumers advice about where and when to fly -- and when to buy -- to save money on their air travel. We believe this helps consumers buy more quickly, with less effort, and ultimately be happier with their purchase decision. One of our key features is our "when to buy" advice: we'll watch prices for your trip and notify you when the price is right.

Recommending when to buy is tough for two main reasons: first is the airfare marketplace and its idiosyncrasies present unique analytical challenges, and second is that the prediction must be highly consumer-friendly: both easy comprehensible and immediately actionable. If we're too conservative and tell you to buy too early, we risk missing out on a better deal later, but if we're too optimistic and wait too long, you could end up paying more as prices rise towards your departure date. Because prices change at the whim of the airlines in unpredictable ways, it's impossible to know for sure. But this session will outline how we've overcome these challenges to help consumers save 10% on average, and up to 40% in some cases.

Dr. Patrick Surry
Chief Data Scientist

4:20-5:05pm • Room: 1A24

Risk Management
Case Study: GE Capital
Importance of Model Risk Management in Financial Institutions

Effective Risk management is a very critical element in any successful and sustainable business. With the recent crisis of 2008 it becomes more evident that failing to manage this function properly could cause short term and long-term impact in any size of a business or an organization (Big, Medium or Small). Financial Risk Managers, Investors, Researchers and Risk Practitioners have to find way to manage Risks more accurately. In this research we will present some advanced techniques or steps that can guide Risk Practitioners in their process to Manage Risk effectively or more accurately. 1) We will look at the data sources (Big Data and others) use to create the Financial Models for Decision Support Systems, 2) the model types use in Model Risk Management process, 3) the Data aggregation from the model outputs, 4) Ongoing Monitoring - Back Testing 5) Model Reporting, Forecasting and others, 6) last, we will look at the Risk Management Governance Process. Very often we look at Risk Management from top down, in this research we will present a bottom up and top down approach to look at Risk Management. In short, we will present a list of properties that can be missed by looking at Risk Management from top down. Also we will present some negative effects these properties can have in a business or an organization.

Dr. Hevel Jean-Baptiste
Global Senior Program Manager, Model Risk Management Systems
GE Capital

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