We study a class of classification problems best exemplified by the\emph {bank loan}
problem, where a lender decides whether or not to issue a loan. The lender only observes …

Although data quality is of paramount importance in algorithmic decision–making, most
existing methods for supervised classification use training data without ever questioning …

Forecasting of wheat production is of great importance for farmers and agriculture policy
makers to improve production planning decisions. Numerous studies proved that traditional …

Learning under one-sided feedback (ie, where we only observe the labels for examples we
predicted positively on) is a fundamental problem in machine learning–applications include …

A major financial risk to commercial banks is credit risk, which arises when borrowers default
on their debt. Accurately predicting the credit risk of their clients is a key difficulty faced by …

Most supervised classification methods assume perfect training data, although this is not
usually the case in the real–world. Meanwhile, counterfactual data generation approaches …

The lending sector, which is essential to modern life, sometimes provides a significant
portion of bank revenues. They assist with a variety of requirements, from helping people …

The purpose of the study was to evaluate the performance of neural networks as modern
techniques to classify the risk of default against the traditional Logit statistical method, taking …

The banking sector is an integral part of an economy as it helps in capital formation. One of
the most critical issues of banks is the risk involved in loan applications. Employing machine …

Lending institutions and banks are surrounded by a multi-risk business environment, the
most important of which are those related to clients' default and non-fulfillment of their …