AI and Machine Learning in Fraud Detection
Machine Learning is transforming the way companies are generating intelligence and making real-time decisions. This talk will cover American Express’s exploration of machine learning and how it was applied to detect and prevent fraud within its global network. You will learn the basics and complexities of fraud and how using credit card spend data can help surgically identify fraud and elevate the payment experience for millions of Card Members across the globe.
Outline/Structure of the Talk
- The Fraud Landscape
- Characteristics of Fraud
- Our Machine Learning Journey
- Data Cleaning
- Data/Feature Discovery
- Human Intuition
- Interpreting the Model Output
- Governance (especially relevant for the financial services industry)
Attendees will learn about American Express's machine learning journey and how fraud detection and prevention was transformed using machine learning. In addition, I will share the learnings we gathered during our journey which can be applied to any predictive modeling solution.
Anyone interested to apply machine learning to solve critical business problems.
None, however, some experience with machine learning models will be beneficial towards appreciating the content.
schedule Submitted 6 months ago
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schedule 7 months agoSold Out!
Recently, we have heard success stories on how deep learning technologies are revolutionizing many industries. Deep Learning has proven huge success in some of the problems in unstructured data domains like image recognition; speech recognitions and natural language processing. However, there are limited gain has been shown in traditional structured data domains like BFSI. This talk would cover American Express’ exciting journey to explore deep learning technique to generate next set of data innovations by deriving intelligence from the data within its global, integrated network. Learn how using credit card spend data has helped improve credit and fraud decisions elevate the payment experience of millions of Card Members across the globe.