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 …

The misclassification of loan applicants by credit scoring model is one of the main factors
causing the loss of investors' profits in P2P lending. Class imbalance of credit data is a main …

The financial credibility of a person is a factor used to determine whether a loan should be
approved or not, and this is quantified by a 'credit score,'which is calculated using a variety …

At present several vectorization approaches are used to transform text documents into a
numerical format. A huge number of features converted from text data from a single …

As an important network service in the big data era, cloud computing is widely used in
storage and computing outsourcing tasks. The ensuing privacy issues make privacy …

Commercial banks are required to explain the credit evaluation results to their customers.
Therefore, banks attempt to improve the performance of their credit scoring models while …

The role of infrastructure development in the manufacturing growth process is recognized in
the literature and policy cycles, therefore, what determines it is also worthy of attention. This …

Predicting the probability of loan defaults is essential for financial institutes and banks, as a
major part of their income is dependent on the interest & EMIs generated on the repayment …

Credit scoring is an effective tool for banks and lending companies to manage the potential
credit risk of borrowers. Machine learning algorithms have made grand progress in …

Requirement engineering practice (REP) are developed from requirement engineering
processes to guide the engineers in requirement definition. Even though, the practices in …