Federated Learning for Privacy-Preserving Credit Risk Assessment

Credit risk assessment is a key part of keeping financial institutions stable and strong, but it is still hard to do because of problems with data protection, sharing, and differences between organisations. Federated Learning (FL) is an interesting idea because it lets people work together to train models without sharing data directly, which keeps private customer data safe. In this study, we show a shared learning system that can evaluate credit risk while protecting privacy. It takes parts of several machine learning approaches, like random forest, logistic regression, and neural networks. The layout that was suggested spreads data across several groups. This way, changes to the model can be shared while raw data stays in one place. This makes sure that the rules are followed and that the success of the forecast gets better. FL-based logistic regression is a nice place to start for credit score, since federated random forests are good at finding connections that don't follow a straight line. Gradient boosting methods like XGBoost and LightGBM help you cope with class mismatch and make it easier to generalise. Federated neural networks improve the structure such that deep representations can be made from complicated financial attributes. We also look at problems that come up because of extra contact, different types of data, and possible security risks like model reversal and poisoning attacks. The results of experiments show that shared methods can be used to make predictions that are very accurate while still protecting data privacy.