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

Featuspace项目团队将开发一种针对金融犯罪预防的变革性联合解决方案，该解决方案利用金融机构(FI)的洞察力，同时保证个人和账户的个人信息对其信任的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的私人数据集上训练的，或者是联盟模型，这些模型概括了从占主导地位的FIS得出的见解。在日益快速和跨境支付方面，这些方法的局限性在于错过了更大的图景，或者可能容易受到隐私攻击。该项目将提供一个创新的协作深度学习解决方案，建立在现有内部技术的基础上，目前保护全球主要银行、支付处理器和支付基础设施提供商。该系统将采取混合方法来保护输入和输出隐私，使用保护性的建筑设计，并结合事实上的私人数据发布技术，包括去身份识别和差别隐私。在这种情况下，数据处理管道将分布在一个联合系统的参与者之间，避免参数池或得分平均，并产生统一的输出，共同促进金融交易中发起人和受益者的见解相结合。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反向工程的风险。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侦查。项目团队将特别专注于开发一个系统，该系统将减少广泛机构采用的障碍，并能够处理在FIS观察到的真实世界数据格式和数据质量的巨大变化。该解决方案对数据输入格式的期望将是有限的，并处于有利地位，可用于现代企业金融犯罪管理平台。该项目基于Featuepace向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，以及诺丁汉大学的Derek McAuley教授和剑桥大学的Richard Mortier教授在Horizon数字经济研究中心进行的以用户为中心的系统和安全方面的研究。

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.