Federated embedding models for privacy-preserving anomaly detection in bank transactions

用于银行交易中隐私保护异常检测的联合嵌入模型

• 批准号: 10048934
• 金额: $ 7.54万
• 依托单位: FEATURESPACE LIMITED
• 依托单位国家: 英国
• 项目类别: CR&D Bilateral
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10048934
• 关键词: Federated; embedding; models; privacy; preserving

The Featurespace project team will develop a transformational federated solution for financial crime prevention that leverages insights across financial institutions (FI), whilst guaranteeing that personal information of individuals and accounts stays private to their trusted FI. Current dominant approaches in the industry rely on machine learning models that are trained on datasets private to each bank, or consortium models that generalise insights derived from dominant FIs. These approaches are limited in missing the bigger picture in increasingly faster and cross-border payments, or may be vulnerable to privacy attacks.The project will deliver an innovative collaborative deep learning solution that builds over existing in-house technologies, currently protecting major banks, payment processors and payment infrastructure providers worldwide. The system will take a hybrid approach to input and output privacy preservation, using a protective architectural design in combination with de-facto private data publication technologies, including de-identification and differential privacy. In this setting, data processing pipelines will be distributed across participants in a federated system, avoiding parameter-pooling or score-averaging, and producing unified outputs that jointly contribute towards combining originator and beneficiary insights in financial transactions. The proposed system significantly reduces the risk of malicious actors being able to reverse-engineer privately identifiable information about individual account holders. The result is a flexible collaborative learning system, which effectively balances confidentiality requirements and privacy preferences of banking institutions, along with effective financial crime detection.The project team will place special focus on developing a system that reduces barriers for adoption across a broad spectrum of institutions and is able to deal with the large variety in real-world data formats and data quality observed across FIs. The solution will have limited expectations on the format of data inputs and is well-positioned to be productionised in modern enterprise financial crime management platforms.This project builds on Featurespace's experience in delivering fraud and anti-money laundering solutions to major banks and payment infrastructure providers in the UK and around the world, as well as the research of Prof Derek McAuley (University of Nottingham) and Prof Richard Mortier (University of Cambridge) on user-centric systems and security undertaken at the Horizon Digital Economy Research centre.

Featurespace 项目团队将开发一种用于预防金融犯罪的变革性联合解决方案，该解决方案利用跨金融机构 (FI) 的见解，同时保证个人和账户的个人信息对其可信的 FI 保密。目前该行业的主导方法依赖于机器学习模型，这些模型是在每家银行私有的数据集上进行训练的，或者是联盟模型，该模型概括了来自主导金融机构的见解。这些方法在日益加快的跨境支付中无法把握全局，或者可能容易受到隐私攻击。该项目将提供一种创新的协作深度学习解决方案，该解决方案建立在现有的内部技术之上，目前正在保护全球主要银行、支付处理商和支付基础设施提供商。该系统将采用混合方法来保护输入和输出隐私，使用保护性架构设计并结合事实上的隐私数据发布技术，包括去标识化和差异隐私。在这种情况下，数据处理管道将分布在联合系统中的参与者之间，避免参数池或分数平均，并产生统一的输出，共同有助于将金融交易中的发起人和受益人的见解相结合。所提出的系统显着降低了恶意行为者能够对个人账户持有人的私人可识别信息进行逆向工程的风险。其结果是一个灵活的协作学习系统，它有效地平衡了银行机构的保密要求和隐私偏好，以及有效的金融犯罪检测。项目团队将特别注重开发一个系统，以减少广泛机构采用的障碍，并能够处理在金融机构中观察到的各种实际数据格式和数据质量。该解决方案对数据输入格式的期望有限，并且非常适合在现代企业金融犯罪管理平台中进行生产。该项目建立在Featurespace为英国和世界各地的主要银行和支付基础设施提供商提供欺诈和反洗钱解决方案的经验以及Derek McAuley教授（诺丁汉大学）和Richard Mortier教授（剑桥大学）的研究基础上。 地平线数字经济研究中心开展的以用户为中心的系统和安全性研究。