Doubling the Performance of AI for Fraud Detection with Graph
|
Using TigerGraph and some Python machine-learning, we show you how anyone can build a payment fraud system that halves the number of frauds and reduces the number of inappropriately blocked transactions.
Using TigerGraph and some Python machine-learning, we show you how anyone can build a payment fraud system that halves the number of frauds and reduces the number of inappropriately blocked transactions. Richard will cover the entire stack in this demonstration, including data, architecture and code.
Author Name: Big Data LDN
Author Description:
Big Data LDN (London) is a free to attend conference and exhibition, hosting leading data and analytics experts who are ready to equip you with the tools you need to deliver your most effective data-driven strategy. Discuss your business requirements with 130 leading technology vendors and consultants, hear from 150 expert speakers in 9 technical and business-led conference theaters, and network with thousands of fellow data experts.
User Reviews
Be the first to review “Doubling the Performance of AI for Fraud Detection with Graph”
Related Products
Build Deep Learning Models with TensorFlow
Use TensorFlow to build and tune deep learning models.
Data and Programming Foundations for AI
Learn the coding, data science, and math you need to get started as a Machine Learning or AI engineer.
Machine Learning: Logistic Regression
Predict the probability that a datapoint belongs to a given class with Logistic Regression.
Intro to Language Models in Python
Build the basic language models in Python.
Machine Learning: Random Forests & Decision Trees
Learn how to build decision trees and then build those trees into random forests.
Machine Learning: Introduction with Regression
Get started with machine learning and learn how to build, implement, and evaluate linear regression models.
There are no reviews yet.