December 6, 2019

316 words 2 mins read

Operationalize deep learning models for fraud detection with Azure Machine Learning Workbench

Operationalize deep learning models for fraud detection with Azure Machine Learning Workbench

Advancements in computing technologies and ecommerce platforms have amplified the risk of online fraud, which results in billions of dollars of loss for the financial industry. This trend has urged companies to consider AI techniques, including deep learning, for fraud detection. Francesca Lazzeri and Jaya Mathew explain how to operationalize deep learning models with Azure ML to prevent fraud.
Talk Title Operationalize deep learning models for fraud detection with Azure Machine Learning Workbench
Speakers Francesca Lazzeri (Microsoft), Jaya Susan Mathew (Microsoft)
Conference Strata Data Conference
Conf Tag Making Data Work
Location London, United Kingdom
Date May 22-24, 2018
URL Talk Page
Slides Talk Slides
Video

Recent advancements in computing technologies along with the increasing popularity of ecommerce platforms have radically amplified the risk of online fraud for financial services companies and their customers. Failing to properly recognize and prevent fraud results in billions of dollars of loss per year for the financial industry. This trend has urged companies to look into many popular artificial intelligence (AI) techniques, including deep learning for fraud detection. Deep learning can uncover patterns in tremendously large datasets and independently learn new concepts from raw data without extensive manual feature engineering. For this reason, deep learning has shown superior performance in domains such as object recognition and image classification. Although, neural networks have been used for fraud detection for decades, recent advancements in computing technologies along with large volumes of data available today have dramatically improved the effectiveness of these techniques. Using a sample dataset that contains transactions made by credit cards in September 2013 by European cardholders, Francesca Lazzeri and Jaya Mathew explain how to build, deploy, and operationalize a deep learning model to identify and prevent fraud, using Azure Machine Learning Workbench to show the main steps in the operationalization process (from data ingestion to consumption) and the Keras deep learning library with Microsoft Cognitive Toolkit CNTK as the backend.

intelligence financial azure intel keras math dataset microsoft network deep learning machine learning performance neural network artificial intelligence
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