Dont beat the market; beat the bots: Adversarial networks in finance
February 24, 2020
Automated investing has brought an immense amount of stability to the market, but it has also brought predictability. Garrett Lander and Al Kari examine if an adversarial network can game the behavior of automated investors by learning the patterns in market activity to which they are most vulnerable.
Introduction to Hilbert AutoML with TensorFlow Extended (TFX) at Yahoo! JAPAN
February 23, 2020
Hilbert is an AI framework that works with TensorFlow Extended (TFX) at Yahoo! JAPAN, which provides AutoML to create production-level deep learning models automatically. Hilbert is currently used by over 20 services of Yahoo! JAPAN. Shin-Ichiro Okamoto details how to achieve production-level AutoML and explores service use cases at Yahoo! JAPAN.
An introduction to machine learning on graphs
February 16, 2020
Graphs are a powerful way to represent knowledge. Organizations, in fields such as biosciences and finance, are starting to amass large knowledge graphs, but they lack the machine learning tools to extract insights from them. David Mack offers an overview of what insights are possible and surveys the most popular approaches.
The Move to Production Enterprise Blockchains: The Challenges of a Maturing Technology
January 27, 2020
Weve reached a tipping point in the enterprise blockchain space. Hundreds of production networks are now running on a number of different technologies and across a mix of industries, with more coming …
Infrastructure first: Because solving complex problems needs more than technology
January 21, 2020
Drawing from work in technology, community development finance, social psychology, complexity theory, and championship sports, Everett Harper moves to the edge of these disciplines, centering on the key practices that are crucial for solving our most critical challenges.
The telemetry data revolution at Microsoft (sponsored by Microsoft)
January 18, 2020
Interested in becoming more data driven and empowering your peers and coworkers with insights and data? Yoni Leibowitz and Sasha Rosenbaum share how Microsoft has been constantly transforming its engineering, support, finance, and marketing work via new tech for data-driven decisions.
Explainable machine learning in fintech
January 11, 2020
Machine learning applications balance interpretability and performance. Linear models provide formulas to directly compare the influence of the input variables, while nonlinear algorithms produce more accurate models. Eitan Anzenberg explores a solution that utilizes what-if scenarios to calculate the marginal influence of features per prediction and compare with standardized methods such as LIME.
How to mitigate mobile fraud risk by data analytics
January 9, 2020
Seonmin Kim offers an introduction to activities that mitigate the risk of mobile payments through various data analytical skills, drawn from actual case studies of mobile frauds, along with tree-based machine learning, graph analytics, and statistical approaches.
Implementing enterprise data management in industrial and scientific organizations
January 9, 2020
To succeed in implementing enterprise data management in industrial and scientific organizations and realize business value, the worlds of business data, facilities data, and scientific datawhich have long been managed separatelymust be brought together. Sun Maria Lehmann and Jane McConnell explore the cultural and organizational differences and the data management requirements to succeed.
Practicing data science: A collection of case studies
January 7, 2020
Rosaria Silipo shares a collection of past data science projects. While the structure is often similardata collection, data transformation, model training, deploymenteach required its own special trick, whether a change in perspective or a particular technique to deal with special case and special business questions.
Applied machine learning in finance
December 27, 2019
Quantitative finance is a rich field in finance where advanced mathematical and statistical techniques are employed by both sell-side and buy-side institutions. Chakri Cherukuri explains how machine learning and deep learning techniques are being used in quantitative finance and details how these models work under the hood.
Keynote: Blockchain Balance: Exploring the Evolving Roles of Permissioned, Public and Hybrid Networks in the Financial Market
December 27, 2019
As enterprise blockchain moves deeper into the finance market, most organizations are looking at where, when and how to deploy the technology. In evaluating the role of blockchain, its important to k …
Using machine learning to automate car damage assessment and document workflows
December 27, 2019
Vladimir Starostenkov and Siarhei Sukhadolski discuss two ML solutions from Altoros: one was developed to facilitate the process of assessing car damage right at the accident scene, while the second helps to automate recognition, extraction, and analysis. Join in to see how to integrate both solutions into the existing workflows of insurance, car rental, and maintenance services.