  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.
