Financial fraud is a problem of increasing complexity as fraudulent activities move with the digital transformation, the rise of real-time payments, and the rapid growth of online financial services. To combat these threats, companies utilize advanced analytics and machine learning models that can identify anomalous patterns within vast amounts of transactional and behavioral data. However, the financial data, including personally identifiable information (PII) and transactional histories, is sensitive and raises serious privacy concerns. Those risks are compounded by robust regulatory environments, such as GDPR, CCPA, and data localization laws globally, which can bind organizations to exacting requirements for collecting, sharing, and processing customer data. Consequently, the demand for secure data pipelines that do not violate privacy in fraud analytics is higher than ever. In this paper, we present a general framework for constructing privacy-preserving data pipelines in the context of financial fraud detection systems. This architecture is designed to maintain data privacy, security, and regulatory compliance at all phases of the pipeline, from data ingestion and transformation to machine learning model training and real-time fraud alert generation. Our solution consolidates several essential privacy-enhancing technologies (PETs), including differential privacy, homomorphic encryption, federated learning, and secure multi-party computation, which enables collective analytics rather than sharing raw or sensitive data with unauthorized entities.
Ravi Kiran Alluri (Sat,) studied this question.