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

这个I-Corps项目的更广泛的影响/商业潜力是可能开发用于金融服务的欺诈检测软件，如银行和信用卡公司。欺诈检测是一个代价高昂的问题，每年给银行、商家和客户造成数十亿美元的损失。另一个成本是声誉，因为持卡人在交易减少后会减少对商家的赞助。该团队旨在为商业银行提供准确性超过行业标准的欺诈检测人工智能（AI）软件，可能为银行节省数百万美元。该技术基于学习决策树和森林的基本创新，这使得在几个方面提高欺诈检测的最新水平成为可能：更高的预测准确性和更少的误报；更快的预测时间，这是每秒处理许多事务所必需的；以及可解释和可信赖的模型。这个I-Corps项目是基于欺诈检测的，它可以被定义为一个二元分类问题，其中交易被分类为合法或欺诈。目前实际的欺诈检测系统经常使用决策树和森林，这些决策树和森林是在过去交易的大型数据集上训练出来的。然而，从数据中学习树需要解决一个非常困难的数学优化问题。直到最近，这是使用20世纪80年代的启发式算法进行近似的，该算法提供了次优模型。提出的算法，基于现代优化原则，寻求找到更好的解决方案，同时扩展到大型数据集。这项技术可能会产生更准确的模型，但也可以用于较浅的树，这更容易解释和审计，并且可以更快地计算预测。该技术还可以学习更一般类型的基于树的模型，这可能会进一步提高信用评分等应用以及法律、政府和公共卫生部门的性能。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.