Financial crime prevention using privacy preserving Federated Learning

点击翻译按钮获取中文摘要

Financial crime and financial fraud cost billions of dollars to banks and related firms every year. It is possible to build a fraud detection machine learning model to prevent such fraudulent transactions, but due to a lack of relevant training data, its use is limited. There is a wealth of transactional data that financial firms possess, but due to privacy and security concerns, the data cannot be utilized. Federated learning(FL) is a machine learning (ML) technique that can train an algorithm from the private data of these financial firms without compromising user/client's privacy. FL naturally offers privacy advantages compared to the traditional ML approaches. However for highly sensitive data, privacy at each step of FL cycle is required to ensure that private data is not leaked through trained ML models.In this paper, we proposed a privacy-preserving federated learning (FL) solution to tackle fraud detection in highly sensitive bank transactional data. Our federated learning privacy technology stack consists of **SSL-enabled server-client** connection and a **SecAgg+** (secure aggregation plus) strategy to mask local client models. Our secure aggregation strategy is robust client dropouts. We further strengthened our privacy stack with **differential privacy** by introducing noise in the training gradients. Differential privacy can be optionally added or removed depending on the privacy-utility trade-off of the required task. For our fraud/anomaly detection task, we have proposed a **FT-transformer** which is compatible with FL framework and performs at par with tabular models such as XGBoost and random forest. By combining state-of-the-art privacy algorithms and deep learning architecture, we created a unique, scalable, customisable, machine learning task-agnostic, ML framework agnostic and privacy-preserving federated learning solution.