Privacy-Preserving Financial Anomaly Detection via Federated Learning & Multi-Party Computation

TL;DR

This paper presents a privacy-preserving framework combining federated learning, multi-party computation, and differential privacy to enable financial institutions to collaboratively train accurate anomaly detection models without sharing sensitive customer data.

Contribution

It introduces a novel framework that allows secure joint training of anomaly detection models across multiple financial institutions, addressing privacy and regulatory challenges.

Findings

Model's AUPRC improved from 0.6 to 0.7 with additional data

Framework successfully preserves customer data privacy during training

Won the US/UK PETs Challenge for financial crime detection

Abstract

One of the main goals of financial institutions (FIs) today is combating fraud and financial crime. To this end, FIs use sophisticated machine-learning models trained using data collected from their customers. The output of machine learning models may be manually reviewed for critical use cases, e.g., determining the likelihood of a transaction being anomalous and the subsequent course of action. While advanced machine learning models greatly aid an FI in anomaly detection, model performance could be significantly improved using additional customer data from other FIs. In practice, however, an FI may not have appropriate consent from customers to share their data with other FIs. Additionally, data privacy regulations may prohibit FIs from sharing clients' sensitive data in certain geographies. Combining customer data to jointly train highly accurate anomaly detection models is therefore challenging for FIs in operational settings. In this paper, we describe a privacy-preserving framework that allows FIs to jointly train highly accurate anomaly detection models. The framework combines the concept of federated learning with efficient multi-party computation and noisy aggregates inspired by differential privacy. The presented framework was submitted as a winning entry to the financial crime detection track of the US/UK PETs Challenge. The challenge considered an architecture where banks hold customer data and execute transactions through a central network. We show that our solution enables the network to train a highly accurate anomaly detection model while preserving privacy of customer data. Experimental results demonstrate that use of additional customer data using the proposed approach results in improvement of our anomaly detection model's AUPRC from 0.6 to 0.7. We discuss how our framework, can be generalized to other similar scenarios.