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

Tuesday, September 11, 2018
New York, NY

Alistair Croll, Strata Conference Chair and Findata Day Host

Finance is information. From analyzing risk and detecting fraud to predicting payments and improving customer experience, data technologies are transforming the financial industry. And we're diving deep into this change with a new day of data-meets-finance talks, tailored for Strata Data Conference events in the world's financial hubs. Bringing together bankers, analysts, entrepreneurs, financiers, and technologists, it's a can't-miss day for anyone working in the financial sector.

Tuesday, 09/11/2018

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9:00am
Location: 1A 08
Secondary topics:  Financial Services
Alistair Croll (Solve For Interesting), Robert Passarella (Alpha Features)
Average rating: *****
(5.00, 2 ratings)
Program chairs Alistair Croll and Robert Passarella welcome you to Findata Day. Read more.
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9:30am
Location: 1A 08 Level: Beginner
Secondary topics:  Health and Medicine
Amro Alkhatib (National Health Insurance Company-Daman)
Average rating: ****.
(4.20, 5 ratings)
Processing claims is central to every insurance business. Amro Alkhatib shares a successful business case for automating claims processing, from idea to production. The machine learning-based claim automation model uses NLP methods on non-text data and allows auditable automated claims decisions to be made. Read more.
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10:00am
Location: 1A 08 Level: Intermediate
Secondary topics:  Ethics and Privacy, Financial Services
Mridul Mishra (Fidelity Investments)
Average rating: *****
(5.00, 5 ratings)
Currently, most ML models—and particularly those for deep learning—work like a black box. As a result, a key challenge in their adoption is the need for explainability. Mridul Mishra discusses the need for explainability and its current state. Mridul then provides a framework for considering these needs and offers potential solutions. Read more.
10:30am
Location: 1A & 1E Halls
Morning Break (30m)
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11:00am
Location: 1A 08 Level: Non-technical
Secondary topics:  Financial Services
Patrick Angeles (Cloudera)
Average rating: ***..
(3.20, 5 ratings)
The financial crisis of 2008 exposed systemic issues in the financial system that resulted in the failures of several established institutions and a bailout of the entire industry. Patrick Angeles explains why banks and regulators are turning to big data solutions to avoid a repeat of history. Read more.
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11:30am
Location: 1A 08
Swatee Singh (American Express)
Average rating: ****.
(4.33, 6 ratings)
Artificial intelligence (AI) is now being adopted in the financial world at an unprecedented scale. Swatee Singh discusses the need to “democratize” AI in the company beyond the purview of "unicorn" data scientists and offers a framework to do this by stitching AI with the cloud and big data at its backend. Read more.
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12:00pm
Location: 1A 08 Level: Non-technical
James Psota (Panjiva )
Average rating: ****.
(4.00, 6 ratings)
James Psota explains how organizationsåBusinesses are pouring massive amounts of money into data science projects, and expectations are sky-high. But how many of those projects will deliver real value to customers? The history of other hyped new technologies predicts that many will fail, leaving a sense of disillusionment in their wake. Read more.
12:30pm
Location: 3A
Lunch (1h)
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1:30pm
Location: 1A 08 Level: Beginner
Andreas Kohlmaier (Munich Re)
Average rating: ***..
(3.50, 4 ratings)
Munich Re is increasing client resilience against economic, political, and cyberrisks while setting and shaping trends in the insurance market. Recently, Munich Re successfully launched a data catalog as the driver for analyst adoption of a data lake. Andreas Kohlmaier explains how cataloging new data encouraged users to explore new ideas, developed new business, and enhanced customer service. Read more.
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2:00pm
Location: 1A 08 Level: Non-technical
Secondary topics:  Financial Services
Paul Lashmet (Arcadia Data)
Average rating: **...
(2.60, 5 ratings)
Artificial intelligence and deep learning are used to generate and execute trading strategies. Regulators and investors demand transparency into investment decisions, but the decision-making processes of machine learning technologies are opaque. Paul Lashmet explains how these same machines generate data that can be visualized to spot new trading opportunities. Read more.
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2:30pm
Location: 1A 08 Level: Beginner
Secondary topics:  Data preparation, governance and privacy, Ethics and Privacy
Nick Curcuru (Mastercard)
Average rating: ****.
(4.00, 5 ratings)
Data—in part, harvested personal data—brings industries unprecedented insights about customer behavior. We know more about our customers and neighbors than at any other time in history, but we need to avoid crossing the "creepy" line. Laura Eisenhardt discusses how ethical behavior drives trust, especially in today's IoT age. Read more.
3:00pm
Location: 1A & 1E Halls
Afternoon Break (30m)
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3:30pm
Location: 1A 08 Level: Intermediate
Secondary topics:  Financial Services, Recommendation Systems
Robin Way (Corios)
Average rating: ***..
(3.00, 1 rating)
Robin Way shares case study examples of next-best offer strategies, predictive customer journey analytics, and behavior-driven time-to-event targeting for mathematically optimal customer messaging that drives incremental margins. Read more.
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4:00pm
Location: 1A 08 Level: Beginner
Secondary topics:  Temporal data and time-series analytics
Theresa Johnson (Airbnb)
Average rating: ***..
(3.75, 4 ratings)
Theresa Johnson explains how Airbnb is building its next-generation end-to-end revenue forecasting platform, leveraging machine learning, Bayesian inference, TensorFlow, Hadoop, and web technology. Read more.
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4:30pm
Location: 1A 08
Secondary topics:  Financial Services
Jane Tran (Unqork)
Average rating: **...
(2.50, 4 ratings)
Data’s role in financial services has been elevated. However, often the rollout of data solutions fails when an organization’s existing culture is misaligned with its capabilities. Unqork is increasing adoption by honoring existing capabilities. Jane Tran explores methods to finally implement data solutions through both qualitative and quantitative discoveries. Read more.