American Express is a $100bn market cap payments company that provides card-issuing, merchant-acquiring, and card network services to its clients. It executes around $1 trillion in transactions every year, leading to troves of data being accumulated each time a client swipes an American Express card, or each time an American Express merchant executes a sale.
A key cost for American Express is covering for fraudulent transactions that take place through its payments network or by the use of its cards. The company leverages the vast amounts of data it collects from cardholders and merchants to make fraud assessments in fractions of a second, making sure that the purchasing experience is seamless for legitimate customers while limiting the number of fraudulent transactions that are approved. For example, American Express leverages cardholder membership information, spending trends, merchant details to triangulate whether card-not-present transactions (mostly ecommerce-related) are legitimate or not. In less than a second, American Express’ ML algorithms and fraud prevention tools analyze thousands of datapoints of merchant and cardholder alike to minimize the risk of fraud.
An example of the tools created by American Express’ data scientists is Enhanced Authorization (EA). EA allows merchants and American Express to identify who is behind a credit card transaction by having the merchant send additional information to Amex each time a transaction takes place, beyond the typical credit card number, purchase amount, and type of merchandise information. This additional information includes data points such as IP address, email address, and shipping address, which Amex can cross-reference with what is stored in its data hubs. By leveraging EA, American Express has reduced fraudulent transactions by 60%, and is offered free of charge to merchants.
Tools such as EA create value for cardholders by minimizing stress associated with credit card fraud. Merchants are similarly rewarded because they spend less time worrying about the legitimacy of their customers or handling claims. All in all, better fraud prevention leveraging data science creates incentives for American Express’ customers to be a part of its network. Similarly, it reduces costs for American Express as the reduction in fraudulent transactions leads to lower costs for the company.
However, the actual creation of these ML algorithms that analyze each transaction in the American Express network and make a call is no easy task. The company started investing heavily in Big Data and ML capabilities in 2010, when it upgraded its data stack to Apache Hadoop. Hadoop is an open-source framework that facilitates the storage and processing of large datasets such as the ones created every minute by the +150M American Express cards in circulation. This also led to difficult decisions regarding legacy systems that had previously been in use by the company: an assessment was made regarding the ones considered obsolete and in need of discontinuation.
This technological change brought about complicated personnel decisions, as the teams in charge of the previous infrastructure were not necessarily the best suited to handle new data science projects at the cutting edge of technology. The firm had to invest heavily in a hiring binge, building data science capabilities from scratch to the now close to 800 data scientists that are employed at American Express.
Attracting and retaining this new talent in the data science front was a considerable challenge for a company founded 170 years ago. First, talent is extremely scarce in the data science space, with big-pocketed competitors such as Google, Amazon, Microsoft, and Apple absorbing much of the supply by leveraging large salaries and attractive benefits. Similarly, smaller but sexy Silicon Valley start-ups offer attractive equity-compensation packages. American Express executives wondered how they could compete in the space, as a centuries-old, East Coast financial services company.
The first step was to build a state-of-the-art tech lab in Palo Alto, CA, planting a flag in America’s tech hub and trying to disassociate from American Express’ East Coast, financial services stigma. The second step was to isolate the data science team’s culture, so that testing, iterating, and failing were incentivized instead of penalized. A culture of continuous testing and learning was promoted from the top down, and a sense of empowerment was given to these new hires given their immediate impact in business and financial decisions.
However, the geographical, cultural, and contextual split between the data science and business teams produced additional challenges. It is difficult for data scientists to wrap their heads around the business needs of the projects they are working on, and business decision makers struggle understanding the potential and limitations of data science and AI. American Express circumvented this disconnect by leveraging what they call the “democratization” of data – essentially putting data tools developed by tech employees on the West Coast on the hands of business decision makers in the East Coast. This allowed data scientists to focus on transcendental, value-added projects that lead to immediate applications by business users, while business decision makers received handy tools immediately applicable in their day to day.
As American Express continues to invest in data solutions, it will be interesting to track how they tackle increasing concerns and regulation around privacy. Will regulators allow companies such as American Express to leverage personally identifiable data points across multiple sources when providing services to third-party merchants? Where will the line be drawn with respect to the usability and transferability of this data? Will customers be able to opt out? The answer to these questions will become clearer in the coming years.