Gen Amex Mlbasedfield New Fraud Detection System
- 1 Amex is confident that its new model will help it to remain a leader in the field of fraud detection
Gen amex mlbasedfield is confident that its new model will continue to make it the leader in fraud detection. Unlike its current rule-based fraud detection system, the new model utilizes machine learning, which makes it much more accurate. More details will be announced as they become available. For now, however, it is not clear exactly what the new system will look like. Read on to learn more about the new model. After all, this is only a beginning.
Amex is confident that its new model will help it to remain a leader in the field of fraud detection
It is possible to avoid being victimized by fraudsters by making sure that they do not get access to your personal information by making use of sophisticated software. The credit card issuer keeps a full record of individual transactions, and it is important to ensure that this information is kept safe and secure. Many fraudsters use this information to access personal information such as credit card numbers, so it is vital that they trust the issuer to protect that data.
Gen Amex mlbasedfield based on machine learning
The new fraud detection model from gen Amex mlbasedfield uses machine learning to detect fraudulent transactions more effectively than the rule-based system currently used by the company. The new model was trained with billions of observations and executes a series of 1,000 decision trees. In addition, the new technology ingests data from over $1 trillion in transactions to generate decisions within milliseconds. This new model helps Amex maintain the lowest fraud rates in the credit card industry.
This model, called the Gen X, was first iterated in 2014. It is the largest model used within the operations of AmEx. AmEx built this model based on a prioritization of a business question and a model analyzing eight billion transactions annually. AmEx’s Gen X model analyzes data on the customer’s account history to identify the most fraudulent transactions.