There is a growing trend to use modern advanced technology in the finance industry. Information is often obtained on much larger scales, in various modalities, and from multiple dimensions, which greatly enriches the profiles of financial entities and leads to a rapid increase in the complexity of financial analytics. In the meantime, there’s increasing demand for automating the process of data statistics, feature engineering, and model tuning.
Through collaboration with some of the top payments companies around the world, Intel has developed an end-to-end solution for building fraud detection applications. Yuhao Yang explains how Intel used and extended Spark DataFrames and ML Pipelines to build the tool chain for financial fraud detection and shares the lessons learned during development.
Yuhao Yang is a senior software engineer on the big data team at Intel, where he focuses on deep learning algorithms and applications—particularly distributed deep learning and machine learning solutions for fraud detection, recommendation, speech recognition, and visual perception. He’s also an active contributor to Apache Spark MLlib.
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