  Credit risk forecasting plays a crucial role for commercial banks and other
financial institutions in granting loans to customers and minimise the
potential loss. However, traditional machine learning methods require the
sharing of sensitive client information with an external server to build a
global model, potentially posing a risk of security threats and privacy
leakage. A newly developed privacy-preserving distributed machine learning
technique known as Federated Learning (FL) allows the training of a global
model without the necessity of accessing private local data directly. This
investigation examined the feasibility of federated learning in credit risk
assessment and showed the effects of data imbalance on model performance. Two
neural network architectures, Multilayer Perceptron (MLP) and Long Short-Term
Memory (LSTM), and one tree ensemble architecture, Extreme Gradient Boosting
(XGBoost), were explored across three different datasets under various
scenarios involving different numbers of clients and data distribution
configurations. We demonstrate that federated models consistently outperform
local models on non-dominant clients with smaller datasets. This trend is
especially pronounced in highly imbalanced data scenarios, yielding a
remarkable average improvement of 17.92% in model performance. However, for
dominant clients (clients with more data), federated models may not exhibit
superior performance, suggesting the need for special incentives for this type
of clients to encourage their participation.
