Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Schedule: Financial Services sessions

9:00am–5:00pm Tuesday, 09/11/2018
Location: 1A 08
Alistair Croll (Solve For Interesting), Robert Passarella (Alpha Features), Amro Alkhatib (National Health Insurance Company-Daman), Mridul Mishra (Fidelity Investments), Patrick Angeles (Cloudera), James Psota (Panjiva ), Andreas Kohlmaier (Munich Re), Paul Lashmet (Arcadia Data), Nick Curcuru (Mastercard), Robin Way (Corios), Theresa Johnson (Airbnb), Jane Tran (Unqork), Swatee Singh (American Express)
From analyzing risk and detecting fraud to predicting payments and improving customer experience, take a deep dive into the ways data technologies are transforming the financial industry. Read more.
9:35am–9:50am Wednesday, 09/12/2018
Location: 3E
Jeffrey Wecker (Goldman Sachs)
Average rating: ***..
(3.12, 26 ratings)
Jeffrey Wecker leads a deep dive on data in financial services, with perspectives on the evolving landscape of data science, the advent of alternative data, the importance of data centricity, and the future for machine learning and AI. Read more.
10:25am–10:45am Wednesday, 09/12/2018
Location: 3E
Joseph Lubin (Consensus Systems)
Average rating: ***..
(3.00, 12 ratings)
Ethereum is a world computer on top of a peer-to-peer network that runs smart contracts - applications that run exactly as programmed without the possibility of censorship, fraud, or third-party interference. Until now, businesses had to build their systems on database technologies that resulted in siloed and redundant information in typically adversarial contexts. Read more.
11:20am–12:00pm Wednesday, 09/12/2018
Location: Expo Hall Level: Non-technical
Usama Fayyad (Open Insights & OODA Health, Inc.), Troels Oerting (WEF Global Cybersecurity Center)
Average rating: ***..
(3.00, 1 rating)
Usama Fayyad and Troels Oerting share outcomes and lessons learned from building and deploying a global data fusion, incident analysis/visualization, and effective cybersecurity defense based on big data and AI at a major EU bank, in collaboration with several financial services institutions. Read more.
2:05pm–2:45pm Wednesday, 09/12/2018
Location: 1A 21/22 Level: Non-technical
Jim Scott (MapR Technologies)
Average rating: **...
(2.67, 3 ratings)
Jim Scott details relevant use cases for blockchain-based solutions across a variety of industries, focusing on a suggested architecture to achieve high-transaction-rate private blockchains and decentralized applications backed by a blockchain. Along the way, Jim compares public and private blockchain architectures. Read more.
4:35pm–5:15pm Wednesday, 09/12/2018
Location: 1A 06/07 Level: Beginner
Masha Westerlund (Investopedia)
Average rating: *****
(5.00, 2 ratings)
Businesses rely on user data to power their sites, products, and sales. Can we give back by sharing those insights with users? Masha Westerlund explains how Investopedia harnessed reader data to build an index that tracks market anxiety and moves with the VIX, a proprietary measure of market volatility. You'll see how thinking outside the box helps turn data into tools for users, not stakeholders. Read more.
5:25pm–6:05pm Wednesday, 09/12/2018
Location: 1A 06/07 Level: Intermediate
Zachary Hanif (Capital One)
Average rating: ****.
(4.67, 3 ratings)
An understanding of graph-based analytical techniques can be extremely powerful when applied to modern practical problems, and modern frameworks and analytical techniques are making graph analysis methods viable for increasingly large, complex tasks. Zachary Hanif examines three prominent graph analytic methods, including graph convolutional networks, and applies them to concrete use cases. Read more.
5:25pm–6:05pm Wednesday, 09/12/2018
Location: 1A 15/16 Level: Intermediate
Joshua Patterson (NVIDIA), Onur Yilmaz (NVIDIA)
GPUs have allowed financial firms to accelerate their computationally demanding workloads. Today, the bottleneck has moved completely to ETL. The GPU Open Analytics Initiative (GoAi) is helping accelerate ETL while keeping the entire workflow on GPUs. Joshua Patterson and Onur Yilmaz discuss several GPU-accelerated data science tools and libraries. Read more.
5:25pm–6:05pm Wednesday, 09/12/2018
Location: 1A 23/24 Level: Beginner
Do your analysts always trust the insights generated by your data platform? Ensuring insights are always reliable is critical for use cases in the financial sector. Sandeep Uttamchandani outlines a circuit breaker pattern developed for data pipelines, similar to the common design pattern used in service architectures, that detects and corrects problems and ensures always reliable insights. Read more.
1:10pm–1:50pm Thursday, 09/13/2018
Location: 1A 12/14 Level: Beginner
Bob Levy (Virtual Cove, Inc.)
Average rating: ***..
(3.00, 1 rating)
Augmented reality opens a completely new lens on your data through which you see and accomplish amazing things. Bob Levy explains how to use simple Python scripts to leverage completely new plot types. You'll explore use cases revealing new insight into financial markets data as well as new ways of interacting with data that build trust in otherwise “black box” machine learning solutions. Read more.
2:00pm–2:40pm Thursday, 09/13/2018
Location: 1A 08 Level: Intermediate
Archana Anandakrishnan (American Express)
Average rating: ***..
(3.20, 5 ratings)
Building accurate machine learning models hinges on the quality of the data. Errors and anomalies get in the way of data scientists doing their best work. Archana Anandakrishnan explains how American Express created an automated, scalable system for measurement and management of data quality. The methods are modular and adaptable to any domain where accurate decisions from ML models are critical. Read more.
2:00pm–2:40pm Thursday, 09/13/2018
Location: 1A 23/24 Level: Beginner
Financial service clients demand increased data-driven personalization, faster insight-based decisions, and multichannel real-time access. Tim Walpole details how organizations can deliver real-time, vendor-agnostic, personalized chat services and explores issues around security, privacy, legal sign-off, data compliance, and how the internet of things can be used as a delivery platform. Read more.
3:30pm–4:10pm Thursday, 09/13/2018
Location: 1A 08 Level: Non-technical
Emily Riederer (Capital One)
Emily Riederer explains how best practices from data science, open source, and open science can solve common business pain points. Using a case example from Capital One, Emily illustrates how designing empathetic analytical tools and fostering a vibrant InnerSource community are keys to developing reproducible and extensible business analysis. Read more.
3:30pm–4:10pm Thursday, 09/13/2018
Location: 1E 09 Level: Intermediate
Kevin Lu (PayPal), Maulin Vasavada (PayPal), Na Yang (PayPal)
Average rating: ****.
(4.00, 3 ratings)
PayPal is one of the biggest Kafka users in the industry; it manages and maintains over 40 production Kafka clusters in three geodistributed data centers and supports 400 billion Kafka messages a day. Kevin Lu, Maulin Vasavada, and Na Yang explore the management and monitoring PayPal applies to Kafka, from client-perceived statistics to configuration management, failover, and data loss auditing. Read more.
3:30pm–4:10pm Thursday, 09/13/2018
Location: 1A 03/04/05 Level: Intermediate
Revant Nayar (FMI Technologies LLC )
Average rating: *....
(1.50, 2 ratings)
Machine learning has so far underperformed in time series prediction (slowness and overfitting), and classical methods are ineffective at capturing nonlinearity. Revant Nayar shares an alternative approach that is faster and more transparent and does not overfit. It can also pick up regime changes in the time series and systematically captures all the nonlinearity of a given dataset. Read more.
4:20pm–5:00pm Thursday, 09/13/2018
Location: 1A 08 Level: Intermediate
Harish Doddi (Datatron Technologies), Jerry Xu (Datatron Technologies)
Large financial institutions have many data science teams (e.g., those for fraud, credit risk, and marketing), each often using diverse set of tools to build predictive models. There are many challenges involved in productionizing these predictive AI models. Harish Doddi and Jerry Xu share challenges and lessons learned deploying AI models to production in large financial institutions. Read more.