FEDERATED LEARNING TECHNIQUES APPLIED TO CREDIT RISK MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW

Abstract

The control of credit risk is a crucial task for financial institutions. Various subjective and quantitative indicators are used to forecast credit risks. Machine learning technology uses customer data to create accurate predictive models due to current AI/ML breakthroughs. Data from many institutions can be used to improve models. Nevertheless, exchanging data across numerous organizations incurs significant communication costs and compromises the privacy of client data. Credit institutions and financing solution providers benefit from expanded data sources via the arrival of technological developments (AI and Machine Learning, Deep learning). The possibilities to increase customer knowledge and improve their credit risk assessment and management system are increased. This research study explores federated learning techniques for credit risk management. The literature review covers research papers published between 2018 and 2022 and presents different federated learning techniques and their applications in credit risk assessment. The study shows that the choice of technique depends on the application’s specific requirements.