In practice, as a well-known statistical method, the logistic regression model is used to
evaluate the credit-worthiness of borrowers due to its simplicity and transparency in …

Credit risk assessment is at the core of modern economies. Traditionally, it is measured by
statistical methods and manual auditing. Recent advances in financial artificial intelligence …

The number of academic papers in the area of Artificial Intelligence (AI) and its applications
across business and management domains has risen significantly in the last decade, and …

Knowing which factors are significant in credit rating assessments leads to better decision-
making. However, the focus of the literature thus far has been mostly on structured data, and …

As there is rapid growth in the banking and financial sector every individual is relying on the
banks and the loans provided by global and national banking sectors. In general, bank will …

With the rapid development of China's Internet finance industry and the continuous growth of
transaction amount in recent years, a variety of financial risks have increased, especially …

Creditworthiness is acknowledged worldwide as focal point of debt processing. Credit Rate,
an output of credit-rating process, reflects such creditworthiness. We reviewed literature on …

Predicting a customer's propensity-to-pay at an early point in the revenue cycle can provide
organisations with many opportunities to improve the customer experience, reduce hardship …

Measuring credit risk is essential for financial institutions because there is a high risk level
associated with incorrect credit decisions. The Basel II agreement recommended the use of …

There is consensus that the best way for reducing insolvency situations in financial
institutions is through good risk management, which involves a good client selection …