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

该项目团队将开发一个用于预防金融犯罪的转型联邦解决方案，该解决方案利用了对金融机构（FI）的洞察力，同时确保个人和账户的个人信息对他们信任的FI保持私密。目前行业中的主流方法依赖于在每家银行私有数据集上训练的机器学习模型，或概括来自主流金融机构的见解的联盟模型。该项目将提供一个创新的协作式深度学习解决方案，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、支付处理器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与事实上的私人数据发布技术相结合，包括去识别和差异隐私。在这种情况下，数据处理管道将分布在联合系统的参与者之间，避免参数池或平均得分，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结合起来。拟议的系统大大降低了恶意行为者能够对个人账户持有人的私人身份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，同时沿着有效的金融犯罪检测。项目团队将特别关注开发一个系统，以减少在广泛的机构中采用的障碍，并能够处理在金融机构中观察到的大量不同的真实世界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，并且能够在现代企业金融犯罪管理平台中生产。该项目建立在Chuerespace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基础上，以及Derek McAuley教授（诺丁汉大学）和Richard Mortier教授（剑桥大学）在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。

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.