I-Corps: Mobile Application for Preventing Credit/Debit Card Fraud in Real Time

这个i-Corps项目的更广泛的影响/商业潜力是为信用卡/借记卡的消费者提供工具，以便在接触点，即在向账户过帐之前，实时防止欺诈。信用卡/借记卡交易涉及五个关键参与者，即：支付网络提供商、发卡银行(发卡机构)、收单银行、商户和持卡人(消费者)。支付网络提供商是该项目的目标客户。它们为交易提供便利，并向发行人和收购人提供服务。该项目提出了一种支付防御策略，通过将消费者置于商家和发行商之间，从而使客户的资金免于欺诈损失，从而为通信链增加了另一层安全。该项目通过通知消费者即将发生的欺诈并提供必要的技术来阻止欺诈，从而将防止欺诈的责任从银行转移到消费者身上，从而减少了发动成功攻击的时间。市场上的解决方案本质上是反动的，依赖于消费者发现并向发行人报告欺诈行为。然后，发行人进行法医分析，以验证这一说法的合法性。这些解决方案旨在防止未来的攻击，而拟议的解决方案旨在防止手头的攻击。这个i-Corps项目提供了一个实时的、主动的解决方案，利用人类的智能定制机器学习(ML)，以防止欺诈的发生。该项目实现了一种对抗性ML技术来模拟卡的使用行为，以真实地预测变化的行为并将它们与潜在的欺诈区分开来。ML模块托管在后端服务器上，通过API(应用程序编程接口)与支付网络通信，以从商家获得实时交易数据。同样的数据被发送给发行人，以验证资金的可用性。对所获取的数据进行评分以确定其是否具有欺诈性。分数与一组阈值进行比较，低于阈值的消费者只需在手机上点击一下，就会被通知手动批准/拒绝交易。超过这一阈值，交易将被自动批准或不被批准，分别表示合法或欺诈性交易。在消费者手动输入以批准或拒绝交易的情况下，迁移学习被用于学习新的行为，以改进现有模型的性能。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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 to provide the consumers of credit/debit cards with tools for preventing fraud in real-time at the point of contact, that is, before it posts to the account. Credit/debit card transactions involve five key players, namely: payment network provider, issuing bank (issuer), acquiring bank, merchant and cardholder (consumer). The payment network providers are the target customers of this project. They facilitate transactions and provide services to issuers and acquirers. The project proposes a payment defense strategy with the potential to save customers' money from fraud losses by placing the consumer between the merchant and the issuer, thereby adding another layer of security to the chain of communication. The project shifts the responsibility of fraud prevention from banks to the consumers by notifying consumers of impending fraud and providing the requisite techniques to stop the fraud, thereby reducing the time to launch a successful attack. Solutions on the market are reactionary in nature and rely on consumers to detect and report fraud to issuers. Issuers then perform forensic analysis to verify the legitimacy of the claim. These solutions aim to prevent future attacks whereas the proposed solution aims to prevent the attacks at hand.This I-Corps project presents a real-time, proactive solution that tailors machine learning (ML) with human intelligence to prevent the fraud from occurring. The project implements an adversarial ML technique to simulate card usage behaviors to realistically predict changing behaviors and distinguish them from potential fraud. The ML module is hosted on the back-end server and communicates with payment networks via APIs (application programming interfaces) to obtain real-time transactional data from merchants. This same data is sent to issuers to verify the availability of funds. The acquired data is scored to determine whether it is fraudulent. Scores are compared to a set of threshold values, beneath which consumers are notified for manual approval/declining of the transaction by a single click on their mobile phone. Above this threshold, the transaction is automatically approved or disapproved, signifying a legitimate or fraudulent transaction, respectively. In the case where consumers make manual inputs to approve or decline transactions, transfer learning is used to learn new behaviors to improve the performance of the existing model.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.