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...

Knowledge generated using data mining techniques is of great interest for organizations, as it facilitates tactical and strategic decision making, generating a competitive advantage. In the special case of credit granting organizations, it is important to clearly define rejection/approval...

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...

Corporate distress models typically only employ the numerical financial variables in the firms' annual reports. We develop a model that employs the unstructured textual data in the reports as well, namely the auditors' reports and managements' statements. Our model consists of a convolutional...

We address the problem of minimizing the risk of an exposure (e.g., cash holdings) to a small number of defaultable counterparties based on spectral risk measures, in particular the expected shortfall. The resulting risk-minimal allocation turns out to be economically implausible in a number of...

Purpose – The purpose of this paper is to discuss important aspects concerned with credit risk measurement of SMEs.Methodology - Paper presents theoretical study, based on literature review and summary of findings of similar research papers, which have focused on credit risk assessment of...

We consider the basic problem of refi tting a time series over a finite period of time and formulate it as a stochastic dynamic program. By changing the underlying Markov decision process we are able to obtain a model that at optimality considers historical data as well as forecasts of future...

Purpose This paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs). Design/methodology/approach The research methodology employed technical procedures based on bibliographic and exploratory analyses. A traditional investigation...

In this paper we study the performance of several machine learning (ML) models for credit default prediction. We do so by using a unique and anonymized database from a major Spanish bank. We compare the statistical performance of a simple and traditionally used model like the Logistic Regression...