Credit risk assessment is a crucial element in credit risk management. With the extensive
research on consumer credit risk assessment in recent decades, the abundance of literature …

Credit risk is the most significant risk by impact for any bank and financial institution.
Accurate credit risk assessment affects an organisation's balance sheet and income …

Developing accurate analytical credit scoring models has become a major focus for financial
institutions. For this purpose, numerous classification algorithms have been proposed for …

Most credit scoring algorithms are designed with the assumption to be executed in an
environment characterized by an automatic processing of credit applications, without …

There has been significant research into reject inference, with several statistical methods
and machine learning techniques having been employed to infer the possible repayment …

Algorithmic risk assessments are used to inform decisions in a wide variety of high-stakes
settings. Often multiple predictive models deliver similar overall performance but differ …

Artificial intelligence (AI) can significantly contribute to the implementation of the United
Nations Sustainable Development Goals (SDGs) by offering innovative solutions and …

With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted
enthusiasm in Financial Technology (FinTech), applications such as credit scoring have …

Ensuring financial stability necessitates responsible credit granting, so lending institutions
maintain sufficient regulatory capital to withstand losses from defaults. Classification …

Reject inference is a credit scoring technique that can resolve sample selection bias, with
several statistical and machine learning methods having been recently employed to infer the …