Editing Federated Learning (section)

=== 5.7.4 Finance ===

The financial industry handles highly sensitive data including transaction histories, credit scores, customer profiles, and fraud patterns—making privacy, security, and regulatory compliance central concerns. Federated Learning (FL) offers a transformative solution by enabling multiple financial institutions, mobile banking apps, and edge payment systems to collaboratively train machine learning models without exposing raw data to third parties. This helps meet stringent legal requirements such as GDPR, PCI-DSS, and national data localization laws while unlocking the power of cross-institutional intelligence<sup>[1][2]</sup>.

A primary use case in finance is *fraud detection*. Banks and payment processors continuously monitor transaction streams to detect anomalies indicative of fraud or money laundering. Using FL, multiple banks can train a shared fraud detection model by learning from their own customer behaviors locally and contributing encrypted updates to a global model. This collaborative approach leads to more accurate and timely fraud detection without disclosing customer data across organizational boundaries<sup>[2][4]</sup>. Moreover, FL allows for faster adaptation to emerging threats by integrating insights from diverse geographies and transaction patterns.

Another important application is *credit scoring and risk assessment*. Traditional credit scoring models often rely on centralized bureaus, which can be limited in scope and biased by incomplete data. FL enables lenders, fintech platforms, and credit unions to collectively build more representative models by incorporating local, decentralized data such as mobile usage behavior, alternative credit signals, or small business activity—all while preserving customer privacy<sup>[3][5]</sup>.

FL also plays a role in *personalized financial services*. Mobile banking apps and robo-advisors can fine-tune investment recommendations or budgeting tools based on individual behavior, device-resident models, and FL-based collaboration across user groups. This enables banks to offer adaptive, privacy-preserving services without continuous cloud access<sup>[4]</sup>.

Challenges in this domain include heterogeneity in financial institutions’ infrastructure, non-IID data across customer segments, and real-time latency requirements for high-frequency trading environments. Secure Aggregation, Differential Privacy, and adaptive client selection techniques are commonly used to balance accuracy, security, and compliance<sup>[1][3]</sup>. As the financial sector continues to embrace digital transformation, FL is becoming a cornerstone for secure, personalized, and collaborative AI in banking and financial technology.