During the last decade, the increase in computational capacity, the consolidation of new data processing methodologies and the availability of access to new information concerning both individuals and organizations, aided by the widespread internet usage, has increased the development and...

Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have...

We introduce the Credit Risk Database (CRD) and its contribution to financial inclusion efforts in Japan. By collecting financial data about small and medium-sized enterprises (SMEs), the CRD contributes to the overall understanding of the SME sector, to the adaptation of risk-based lending and...

This study explores whether financial literacy can enhance the ability to predict credit default by farmers using machine-learning models. It introduces a hybrid model combining k-means clustering and Adaboost to predict loan default using data on 10,396 farmers who obtained credit from Chinese...

The paper proposes an explainable AI model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing credit scoring platforms. The model applies similarity networks to Shapley values, so that AI predictions are grouped...

Risk management is one of the most important branches of business and finance. Classification models are the most popular and widely used analytical group of data mining approaches that can greatly help financial decision makers and managers to tackle credit risk problems. However, the...