There has been intensive research from academics and practitioners regarding models for
predicting bankruptcy and default events, for credit risk management. Seminal academic …

The valuation of house prices is drawing noteworthy attention due to worldwide financial
and real estate crises in the last decade. Therefore, there is an immediate need to design …

With the unprecedented increase in data all over the world, financial sector such as
companies and industries try to remain competitive by transforming themselves into data …

Banks have a vital role in the financial system and its survival is crucial for the stability of the
economy. This research paper attempts to create an efficient and appropriate predictive …

Financial data classification plays an important role in investment and banking industry with
the purpose to control default risk, improve cash and select the best customers. Ensemble …

This paper describes novel approaches to predict default for SMEs. Multivariate outlier
detection techniques based on Local Outlier Factor are proposed to improve the out of …

This study aims to verify the potential of combining corporate prior payment behavior and
Kohonen maps for small enterprise default prediction. Logistic regression, discrete-time …

In this paper, the forest algorithm and the decision tree algorithm are mainly used to analyze
students' physical education information, course exam results, and student learning data …

Financial institutions, by and large, rely on the use of machine learning techniques to
improve the classic credit risk assessment model for reduction of costs, delivery of faster …

This study aims to analyse whether the characteristics of a company's corporate social
responsibility (CSR) orientation could improve the accuracy rates of small enterprise (SE) …