Abstract Machine learning algorithms treat credit risk prediction as a binary classification
problem. However, two-way decisions with a single threshold force to make either a default …

Purpose The purpose of this study is to identify the determinants of success in peer-to-peer
(P2P) lending campaigns, especially amid global financial disruptions like the COVID-19 …

Information asymmetry is widespread in the P2P online lending market, creating an
imbalance in the position of lenders and borrowers. This paper aims to expand the process …

Credit risk assessment is one of the most important aspects of financial decisionmaking
processes. This study presents a systematic review of the literature on the application of …

In the past decade, financial institutions have invested significant efforts in the development
of accurate analytical credit scoring models. The evidence suggests that even small …

ABSTRACT Does Peer‐to‐Peer (P2P) lending, a digital FinTech platform, show resilience in
response to COVID‐19 shocks? We examine this question by considering both the …

Credit risk evaluation is a binary classification problem, and machine learning algorithms
have achieved remarkable results in this field. However, traditional two-way decisions …

The purpose of this study was to describe the association between the learning strategy
preference of the learners as identified by" Assessing The Learning Strategies of …

Psychometric-based credit scores measure important personality traits that are characteristic
of good borrowers' behaviors. While such data can potentially improve credit models for …

As the number of thin filers in Korea surpasses 12 million, there is a growing interest in
enhancing the accuracy of assessing their credit default risk to generate additional revenue …