American Express handles more than 25 percent of credit card activity in the United States and, in 2014, surpassed handling $1 trillion in transactions. The company interacts with people on both sides of transactions: millions of businesses and millions of buyers.
So it’s no surprise then that American Express has no shortage of data. The only question for the company is how to leverage all of that data.
In 2010, the company realized that traditional databases no longer would be sufficient for the data and analysis that it wanted to handle, and so the company brought itself into the age of big data by upgrading to a Hadoop infrastructure and bringing in machine learning algorithms.
Millions of Decisions, Improved
There’s an old saying that if you can consistently improve by just 1 percent every week, by the end of a year, you will have improved by 50 percent. That’s akin to the model that American Express has developed.
Within the company, millions of decisions are made every day. If, by employing big data analytics and machine learning, the company can make even slightly better decisions, it would have a huge impact.
One place in which the company has implemented machine learning algorithms is in the fraud detection and prevention department. The company is attempting to detect fraudulent transactions as quickly as possible to minimize loss, so it employed a machine learning model that uses of a variety of data sources including card membership information, spending details, and merchant information to detect suspicious events and make a decision in milliseconds by comparing that event to a large dataset. This has enabled American Express to detect more fraudulent transactions and save millions of dollars.
Using big data and machine learning algorithms for fraud prevention has now become commonplace in the industry. Visa also uses this technology and checks many hundreds of aspects of any transaction in near real time. According to estimates by the company, the approach has identified $2 billion in potential annual incremental fraud incidents, which the company was able to sort out before any money was lost.
Preparing for the Future
American Express increasingly is moving away from focusing on its traditional function of providing credit for consumers and providing merchant services for processing transactions, and toward actually making the connection between consumers and the businesses that want to reach them.
The company is using its vast data flows to develop apps that can connect a cardholder with products or services. One app looks at past purchase data and then recommends restaurants in the area that the user is likely to enjoy.
Another one, called Amex Offers, shows real time coupons relevant to a cardholder’s lifestyle and buying habits based on the cardholder’s physical location near the businesses that offer them. And this isn’t just a benefit to the cardholders who use the app, but also hopefully an incentive for more businesses to accept American Express.
On the merchant side, American Express is offering new online business trend analysis and industry peer benchmarking based on anonymized data to help companies see how they are doing compared with their competition. Credit card companies take out any personally identifiable data from the transactions but are still able to provide retailers with detailed trends within specific niche markets or customer segments.
The company is so serious about embracing the tech side of its business that, last year, it opened a tech lab in Palo Alto, California, specifically to handle these business concerns.
The company is on the leading edge of integrating data collection and analysis and machine learning into its business model and practices to the benefit of the company and its end users. Other credit card companies such as MasterCard and Visa are applying similar technologies, and it looks as if big data, analytics, and machine learning are at the forefront of their competitive forces as well. Whichever company will leverage data in the most effective way will win in this highly competitive environment.