{"id":32072,"date":"2018-11-13T14:22:22","date_gmt":"2018-11-13T19:22:22","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/fighting-fraud-with-machine-learning-at-american-express\/"},"modified":"2018-11-13T14:22:22","modified_gmt":"2018-11-13T19:22:22","slug":"fighting-fraud-with-machine-learning-at-american-express","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/fighting-fraud-with-machine-learning-at-american-express\/","title":{"rendered":"Fighting Fraud with Machine Learning at American Express"},"content":{"rendered":"

Days before embarking on a recent trip, I received an email from American Express stating \u201cBased on recent transactions, it appears that you may be traveling soon. We use industry-leading fraud detection capabilities and do not need to be notified of travel plans in advance to recognize when our Card Members are traveling.\u201d[1]<\/span> While other card issuers, such as Chase [2]<\/span> and Wells Fargo [3]<\/span>, require cardholders to notify them in advance of travel, American Express does not provide any way for customers to set up travel alerts, instead relying completely on internal fraud detection technology.<\/span><\/p>\n

Machine Learning at American Express<\/b><\/h2>\n

American Express has long been at the forefront of transaction monitoring and fraud detection. In 1988\u2014at a time when merchants had to call American Express for large credit authorizations over the phone\u2014an American Express employee had to make a quick judgement call to determine if payment could be authorized. American Express leveraged expert systems, a then-cutting-edge technology that used conditional statements and data from up to 13 databases, to determine if a purchase was in line with the customer\u2019s transaction history.[4]<\/span><\/b><\/p>\n

Three decades later, financial institutions around the world continue to combat credit card fraud, collectively spending $27.7 billion on card fraud costs in 2017, a number that expected to grow to $31.7 billion by 2020.[5]<\/span> For its part, American Express has invested heavily in machine learning to more accurately identify card fraud. In an interview shortly before the end of his 40+ year tenure at American Express, Ash Gupta, President of Global Credit Risk & Information Management, identified two main benefits from this investment. First, by training fraud detection models on the over $1 trillion of annual transactions, American Express has developed tools that have proven far more accurate than the manual if-then rules developed in the 1980s and 1990s. Second, training and retraining models on an ever-growing amount of data is a faster process than developing the older format of fraud rules. Whereas expert systems were manually tuned, machine learning models can be rapidly retrained on this new data.[6]<\/span><\/p>\n

American Express has chosen to develop its machine learning technology in-house, employing 1,500 data scientists globally as of March 2017. The company views its investment in machine learning as an evolution of its earlier investment in expert systems, and believes that they will be able to continue developing technology more efficiently internally.[7]<\/span><\/p>\n

Though American Express and its peer institutions have made significant progress through the use of machine learning, there is still more to be gained by improving the accuracy of their fraud detection models. One area for improvement is in reducing false positive rates, transactions that are mistakenly blocked because they are thought to be fraudulent. Card issuers are estimated to lose 13 times as much from false positives than actual fraudulent transactions.[8]<\/span> A study from MIT found that this rate of false positives can be reduced by as much as 54% using Deep Feature Synthesis, an automated method for identifying complex features from input data. For example, a \u201cdeep feature\u201d might be \u201cHow much money was spent in a coffee shop on a Friday morning?\u201d A change from $5 to $15 in a given week might then be flagged as potential fraud.[9]<\/span> Importantly, this technique retains a human-understandable reason for why a transaction might be declined. Gupta has spoken about the importance of identifying <\/span>why<\/span><\/i> an algorithm makes a decision, stating \u201cfor us, it\u2019s very important that the model has sufficient transparency and that we can describe for the customer exactly the reason we are taking the action [that we are taking].\u201d[10]<\/span><\/p>\n

Looking Forward<\/b><\/h2>\n

Questions remain about the future of machine learning at American Express and the effect it will have on the organization. First, to what extent can machine learning be used to augment other areas in the business? American Express has begun to use this technology to provide more targeted ads and deals based on customer spending habits.[11] Are there other areas where American Express can leverage user information to provide a better experience?<\/span><\/p>\n

Second, to what extent will machine learning replace jobs at American Express. The company has stated that its investment in machine learning is in the pursuit of augmentation, not automation. However, as seen in the case of fraud detection, a job that was once completely manual was first augmented with expert systems and is now completely automated through machine learning models.[12]<\/span>\u00a0Will the use of machine learning stretch into other areas in the company and replace other roles?<\/span><\/p>\n

(Word Count: 754)<\/p>\n

 <\/p>\n

[1]\u00a0American Express <AmericanExpress@welcome.aexp.com>, \u201cHelpful Information for Your Upcoming Trip,\u201d email message to Andrew Harris, April 10, 2018.<\/span><\/p>\n

[2]\u00a0Chase, \u201cTravel Notification,\u201d https:\/\/www.chase.com\/personal\/credit-cards\/travel-notification, accessed November 2018.<\/span><\/p>\n

[3]\u00a0Wells Fargo, \u201cTravel Tips and Tools,\u201d https:\/\/www.wellsfargo.com\/goals-banking-made-easy\/travel-tips\/, accessed November 2018.<\/span><\/p>\n

[4]\u00a0Dorothy Leonard-Barton and John Sviokla. March 1988. \u201cPutting Expert Systems to Work\u201d. <\/span>性视界 Business Review<\/span><\/i>. <\/span>https:\/\/hbr.org\/1988\/03\/putting-expert-systems-to-work<\/span>, accessed November 2018.<\/span><\/p>\n

[5]\u00a0Sarah Kocianski and Dan Van Dyke, \u201cAI in Banking and Payments: How artificial intelligence is cutting costs, building loyalty, and enhancing security across financial services,\u201d Business Insider Intelligence, February 6 2018, https:\/\/intelligence.businessinsider.com\/post\/ai-in-payments-and-banking-2017-12, accessed November 2018.<\/span><\/p>\n

[6]\u00a0LendIt Conference, \u201cInnovation in Credit Granting With Big Data – American Express’s Ash Gupta,\u201d YouTube, published March 6, 2017, https:\/\/www.youtube.com\/watch?v=4obUWkBuzs4, accessed November 2018.<\/span><\/p>\n

[7] Thomas H. Davenport and Randy Bean. March 31, 2017. \u201cHow P&G and American Express Are Approaching AI\u201d. <\/span>性视界 Business Review<\/span><\/i>. https:\/\/hbr.org\/2017\/03\/how-pg-and-american-express-are-approaching-ai, accessed November 2018.<\/span><\/p>\n

[8]\u00a0Kocianski and Van Dyke, \u201cAI in Banking and Payments.\u201d<\/span><\/p>\n

[9] Rob Matheson, \u201cReducing false positives in credit card fraud detection,\u201d MIT News Office, September 20, 2018, http:\/\/news.mit.edu\/2018\/machine-learning-financial-credit-card-fraud-0920, accessed November 2018.<\/p>\n

[10]\u00a0LendIt Conference, \u201cInnovation in Credit Granting With Big Data – American Express’s Ash Gupta.\u201d<\/span><\/p>\n

[11]\u00a0Ibid.<\/span><\/p>\n

[12]\u00a0Davenport and Bean, \u201cHow P&G and American Express Are Approaching AI.\u201d<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Global financial institutions spend billions of dollars on credit card fraud. 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