AI Case Study

American Express Australia used machine learning to identify 24% of customer accounts that would close within four months allowing them to take preventative save actions

American Express has over 100 million credit card customers globally representing over $1 trillion in annual charge volume. The Australian company used advanced data analytics and machine learning to analyse historical transactions along with 115 variables to forecast customer churn. They were able to identify 24% of accounts that would close within four months allowing them to take preventative save actions.

Industry

Financial Services

Banking

Project Overview

"With access to big data, machine learning models can produce superior discrimination and thus better understand customer behavior. Through sophisticated predictive models the company has been able to analyze historical transactions and 115 variables to forecast potential churn."

Reported Results

In the Australian market, they now believe they can identify 24% of accounts that will close within four months allowing them to take actions to retain those customers.

Technology

Machine learning

Function

Customer Service

Account Management

Background

"With a database of over a 100 million credit cards globally, that account for over $1 trillion in charge volume every year, American Express deals with vast quantities of data." They are looking to better understand customer behaviour and improve customer retention and have built strong big data capabilities.

"AmEx is able..to analyze trends and information on cardholder spending and build algorithms to provide customized offers to attract and retain customers and leverage this information to maintain relationships with merchants using targeted marketing to match merchants with the right customers, who are likely to spend more and stay loyal."

Benefits

Data

"Historical transactions and 115 variables to forecast potential churn"