{"id":35923,"date":"2018-11-13T19:40:52","date_gmt":"2018-11-14T00:40:52","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/american-express-machine-learning-is-a-potential-gold-mine\/"},"modified":"2018-11-13T19:40:52","modified_gmt":"2018-11-14T00:40:52","slug":"american-express-has-struck-gold-with-machine-learning","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/american-express-has-struck-gold-with-machine-learning\/","title":{"rendered":"American Express has Struck Gold with Machine Learning"},"content":{"rendered":"

American Express (\u201cAmex\u201d) is an issuer processor, network and merchant acquirer. Their revenues are derived from three primary sources: annual card fees; interest income on late payments; and merchant and network fees on each transaction.[1]<\/sup> Amex has become one of the most profitable and prestigious companies in the card market globally. Yet, they still have much untapped potential, which can be unleashing from harnessing the power of machine learning.<\/p>\n

Over the past 150 years, they have accumulated data from transactions that have been made on their cards. Given that in 2017 alone, they processed $1.1 trillion worth of transactions and had 112.8 billion cards in force, the volume of data they possess is unparalleled.[1]<\/sup> Moreover, as the card market is one in which trust, customer service and risk systems are paramount, effective use of this data through machine learning presents tangible upside to the company\u2019s performance. In this short essay, I will discuss a few key issues inherent in this market, which can drive the profitability of Amex if handled effectively: A) fraud detection; B) customer acquisition; and C) customer service.<\/p>\n

The first use case we shall discuss is fraud detection. The incidence of credit card fraud is on the rise, with scammers getting increasingly smart and creative in their thieving ways.[2]<\/sup> An individual makes thousands of transactions a year, how can we instantaneously stop those which are fraudulent? Using machine learning, the management of Amex is aiming to detect and prevent fraudulent transactions as they are taking place, whilst allowing all normal transactions to go through.[3]<\/sup> Given the vast data set of historic transaction information on an individual and similar individuals, they can recognize spending patterns \u2013 Where do customers spend? What do customers spend on? Predictive technology can be used to indicate whether all future transactions are considered normal or abnormal. In the latter case, anomalies can be prevented and investigated further, avoiding potential substantial losses to scammers.<\/p>\n

In the medium term, new customer acquisition will be crucial in sustaining a successful business. Amex has engaged in targeted marketing through machine learning models.[3]<\/sup> This allows them to identify potential customers and place them in dynamic groups based on interest variables derived from their online interactions. This information allows Amex to identify which features individuals value most and to accurately target certain groups with certain products (i.e Individual X travels a lot, we should direct them to our zero foreign transaction fee card).[4]<\/sup> This will not only increase demand for Amex cards, but also reduce acquisition costs which have historically been high due to direct mail campaigns.[3]<\/sup><\/p>\n

The final use case is to drive customer satisfaction through their recommendation model.[3]<\/sup> By using the transaction history and profile data of an individual, Amex can predict which restaurants an individual will like and make customized restaurant recommendations accordingly. This will improve a customer\u2019s experience (hopefully reducing the churn rate), and also has potential to drive further transactions and create advertising revenue.<\/p>\n

But why stop there? Amex can further harness the potency of machine learning in the short and medium term. Amex can take the lessons learned from improving the new customer acquisition process to develop new products. By combining existing transaction data with the dynamic grouping of targeted marketing, Amex can truly understand the needs of its current and future customers. This will allow them to predict what exact features each of the distinct customer segments desire and create products accordingly.<\/p>\n

They can also further augment the customer service with other machine learning use cases. Amex can develop methods to accurately preempt customers\u2019 questions and answer them before they have to call customer support for help. By tracking the way customers generally navigate through their website or mobile application before they reach out with their issues, Amex will be able to develop a help page which pushes the most likely issue a customer is facing. Other financial institutions have reached a 53% accuracy in determining the top 3 potential issues a customer may have.[5]<\/sup> Given the vast data set available, Amex is likely to generate similar if not better outcomes.<\/p>\n

Overall, this short essay brushes the surface of the potential use cases of machine learning in this industry. Exploiting all potential avenues of this megatrend, Amex can benefit substantially through the generation of greater card and transaction fees, lower costs and lower loan loss provisions.<\/p>\n

A couple of open questions related to machine learning in this context would be:<\/p>\n