Machine Learning In The Payments Industry - AI Summary

In recent years many successful machine learning applications have been developed, ranging from data-mining programs that learn to detect fraudulent credit card transactions, to information-filtering systems that learn customers' buying behavior, to the most popular application of AI in financial services -- and perhaps the most limited -- the chatbot, a program that converses with customers through text or speech. So when we talk about Machine Learning, we talk about the process of using digitized labeled data which is stored as a data set in a database, and using automated processes to create analytics from which users can derive information. Whenever a Data Set (X) can be used to create analytics from which users are deriving information, the next step is to use Machine Learning with the help of algorithms to create a New Process changing the data to (X X 1), which in itself provides a new Data Set completing the Machine Learning Model. Payment systems generally generate a lot of data, because of industry standards, and the good thing to know is much of this data is well structured which reduces the effort needed to prepare it for use in machine learning algorithms. In recent years many successful machine learning applications have been developed, ranging from data-mining programs that learn to detect fraudulent credit card transactions, to information-filtering systems that learn customers' buying behavior, to the most popular application of AI in financial services -- and perhaps the most limited -- the chatbot, a program that converses with customers through text or speech.