Discover how to enhance Anti-Money Laundering (AML) fraud detection using Azure Machine Learning in this 49-minute conference talk. Explore the AML domain and typical detection workflows before delving into machine learning algorithms that improve detection accuracy and prioritization of flagged activities. Learn to implement these enhancements using Azure Machine Learning, achieving both qualitative and quantitative improvements. Gain insights into applying similar machine learning approaches to other financial services areas, such as insurance claims fraud detection. Through demonstrations and practical examples, master techniques like data visualization, model training, experiment execution, and web service deployment to create more effective AML solutions.
Enhanced AML Fraud Detection Solutions with Azure Machine Learning
Overview
Syllabus
Introduction
What is AML
What is Money Laundering
Typical AML Flow
Common AML Pitfalls
Enhanced AML Fraud Detection
Machine Learning for AML
Machine Learning with Azure
Azure Machine Learning Studio
Machine Learning Studio
Data Visualization
Machine Learning
Train Model
Running the Experiment
Evaluate Model
Web Service Setup
Web Service Output
Deploy Web Service
Multiple Machine Learning Models
Demo
Case management
Insurance fraud
Visual workflow development
Data Jet
Report Column
Straind Model
Retraining Pipeline
Be Testing
Taught by
NDC Conferences
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