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.

信用风险预测对商业银行和其他金融机构在授予贷款和减少潜在损失方面起着关键作用。本研究调查了联邦学习在信用风险评估中的可行性，并展示了数据不平衡对模型性能的影响。通过使用多层感知机 (MLP)、长短期记忆网络 (LSTM) 和极限梯度提升 (XGBoost) 等不同架构，以及三个不同数据集和不同场景下的数据分布配置，我们证明了联邦模型在数据集较小的非主导客户上始终优于本地模型，在高度不平衡的数据场景中平均提高了 17.92% 的模型性能，然而对于数据更多的主导客户，联邦模型可能表现出较低的性能，因此需要针对这类客户提供特殊的激励以鼓励其参与。