  Fraudulent transactions and how to detect them remain a significant problem
for financial institutions around the world. The need for advanced fraud
detection systems to safeguard assets and maintain customer trust is paramount
for financial institutions, but some factors make the development of effective
and efficient fraud detection systems a challenge. One of such factors is the
fact that fraudulent transactions are rare and that many transaction datasets
are imbalanced; that is, there are fewer significant samples of fraudulent
transactions than legitimate ones. This data imbalance can affect the
performance or reliability of the fraud detection model. Moreover, due to the
data privacy laws that all financial institutions are subject to follow,
sharing customer data to facilitate a higher-performing centralized model is
impossible. Furthermore, the fraud detection technique should be transparent so
that it does not affect the user experience. Hence, this research introduces a
novel approach using Federated Learning (FL) and Explainable AI (XAI) to
address these challenges. FL enables financial institutions to collaboratively
train a model to detect fraudulent transactions without directly sharing
customer data, thereby preserving data privacy and confidentiality. Meanwhile,
the integration of XAI ensures that the predictions made by the model can be
understood and interpreted by human experts, adding a layer of transparency and
trust to the system. Experimental results, based on realistic transaction
datasets, reveal that the FL-based fraud detection system consistently
demonstrates high performance metrics. This study grounds FL's potential as an
effective and privacy-preserving tool in the fight against fraud.
