I-Corps: Tree-based artificial intelligence (AI) models for financial fraud detection

University Of California - Merced, Merced CA

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is the potential development of fraud detection software for use in financial services, such as banks and credit card companies. Fraud detection is a costly problem with annual losses of billions of dollars to banks, merchants, and customers. Another cost is reputational, as cardholders reduce patronage to merchants after transaction declines. This team seeks to provide fraud detection artificial intelligence (AI) software for commercial banks with an accuracy that exceeds industry standards, potentially saving banks millions of dollars. The technology is based on a fundamental innovation in learning decision trees and forests which makes it possible to improve the state of the art in fraud detection along several fronts: a superior predictive accuracy and fewer false positives; a faster prediction time, necessary for processing many transactions per second; and models that are explainable and trustworthy. This I-Corps project is based fraud detection that can be framed as a binary classification problem, where a transaction is classified as legitimate or fraudulent. Current practical fraud detection systems often use decision trees and forests trained on large datasets of past transactions. However, learning a tree from data requires solving a very difficult mathematical optimization problem. Until recently, this was approximated using heuristic algorithms dating from the 1980s which provide suboptimal models. The proposed algorithm, based on modern optimization principles, seeks to find much better solutions while scaling to large datasets. The technology may result in more accurate models, but also in shallower trees, which are easier to explain and audit and which can compute predictions faster. The technology can also learn more general types of tree-based models, which may lead to further improvements in performance in applications such as credit scoring, and in the legal, government and public health sectors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.