Three years ago, Veeramachaneni and Kanter developed Deep Feature Synthesis (DFS), an automated approach that extracts highly detailed features from any data, and decided to apply it to financial transactions.Įnterprises will sometimes host competitions where they provide a limited dataset along with a prediction problem such as fraud. Paper co-authors include: lead author Roy Wedge '15, a former researcher in the Data to AI Lab at LIDS James Max Kanter ’15, SM ’15 and Sergio Iglesias Perez of Banco Bilbao Vizcaya Argentaria. … That’s the most impactful thing to improve accuracy of these machine-learning models.” “We can say there’s a direct connection between feature engineering and false positives. “The big challenge in this industry is false positives,” says Kalyan Veeramachaneni, a principal research scientist at MIT’s Laboratory for Information and Decision Systems (LIDS) and co-author of a paper describing the model, which was presented at the recent European Conference for Machine Learning. Tested on a dataset of 1.8 million transactions from a large bank, the model reduced false positive predictions by 54 percent over traditional models, which the researchers estimate could have saved the bank 190,000 euros (around $220,000) in lost revenue. By doing so, it can better pinpoint when a specific card holder’s spending habits deviate from the norm. The MIT researchers have developed an “automated feature engineering” approach that extracts more than 200 detailed features for each individual transaction - say, if a user was present during purchases, and the average amount spent on certain days at certain vendors. But because consumer spending habits vary, even in individual accounts, these models are sometime inaccurate: A 2015 report from Javelin Strategy and Research estimates that only one in five fraud predictions is correct and that the errors can cost a bank $118 billion in lost revenue, as declined customers then refrain from using that credit card. If any given customer spends more than, say, $2,000 on one purchase, or makes numerous purchases in the same day, they may be flagged. When you swipe your card, the card pings the model and, if the features match fraud behavior, the sale gets blocked.īehind the scenes, however, data scientists must dream up those features, which mostly center on blanket rules for amount and location. Researchers train models to extract behavioral patterns from past transactions, called “features,” that signal fraud. Using machine learning to detect financial fraud dates back to the early 1990s and has advanced over the years. Now MIT researchers have employed a new machine-learning technique to drastically reduce these false positives, saving banks money and easing customer frustration. One cause is that fraud-detecting technologies used by a consumer’s bank have incorrectly flagged the sale as suspicious. Have you ever used your credit card at a new store or location only to have it declined? Has a sale ever been blocked because you charged a higher amount than usual?Ĭonsumers’ credit cards are declined surprisingly often in legitimate transactions.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |