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.

It wasn't so long ago that CEO's and large banks were convinced that bank locations would always be necessary to service their customers. However the last ten years we have seen an emergence of Digital Banks, that have never and will probably never own a physical location, but still manage to grow their user base and add additional services including insurance, mortgages and loans. In the Payments industry we have seen companies like Chase and First Data dominate for well over forty years. However just like the digitization of banking has forced incumbents to change their strategies, the digitization of payments has provided companies like WorldPay, Vantiv and lately even Stripe, PayPal/Braintree and Adyen to take up much of the market share, not by focusing on traditional businesses, but by focusing on startups who have grown to overshadow and sometimes even bankrupt traditional businesses. Think of Blockbuster versus Netflix, Taxi's versus Uber or Traditional Stores versus Amazon.

How are Machine Learning Models going to change the Payments Industry? It wasn't so long ago that CEO's and large commercial banks were convinced that more bank locations would always be necessary to service and acquire new customers. However, in the last ten or five years we have seen an emergence of Digital Banks, that have never and will probably never own a physical location, but still manage to grow their user base and add additional services including insurance, mortgages, and loans. In the Banking industry, we have seen companies like First Bank of Nigeria, United Bank of Africa, Zenith Bank, Guaranty Trust Bank dominate for well over twenty years. However, just like the digitization of banking has forced incumbents to change their strategies, the digitization of payments has provided companies like Flutterwave, Paystack, Remita and lately even Korapay to take up some of the market shares, not by focusing on traditional businesses, but by focusing on startups who have grown to overshadow and sometimes even bankrupt traditional businesses.