Three-stage reject inference learning framework for credit scoring using unsupervised transfer learning and three-way decision theory

To solve the high-dimensionality issue and improve its accuracy in credit risk assessment, a high-dimensionality-trait-driven learning paradigm is proposed for feature extraction and classifier selection. The proposed paradigm consists of three main stages: categorization of high dimensional data, high-dimensionality-trait-driven feature extraction, and high-dimensionality-trait-driven classifier selection. In the first stage, according to the definition of high-dimensionality and the relationship between sample size and feature dimensions, the high-dimensionality traits of credit dataset are further categorized into two types: 100 < feature dimensions < sample size, and feature dimensions ≥ sample size. In the second stage, some typical feature extraction methods are tested regarding the two categories of high dimensionality. In the final stage, four types of classifiers are performed to evaluate credit risk considering different high-dimensionality traits. For the purpose of illustration and verification, credit classification experiments are performed on two publicly available credit risk datasets, and the results show that the proposed high-dimensionality-trait-driven learning paradigm for feature extraction and classifier selection is effective in handling high-dimensional credit classification issues and improving credit classification accuracy relative to the benchmark models listed in this study.

Section: China Unionpay Credit Datasetmentioning

confidence: 99%

A high-dimensionality-trait-driven learning paradigm for high dimensional credit classification

2021

“…Over the past four decades, uncertainty and financial shocks have been examined by many researchers, who have found that these exogenous shocks have played a significant role in business cycle fluctuations (Kou et al 2014;Caldara et al 2016;Cesa-Bianchi et al 2018;Ludvigson et al 2018;Chao et al 2019;Kou et al 2019: Wen et al 2019Shen et al 2020;Wang et al 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

The macroeconomic effects of COVID-19 in Montenegro: a Bayesian VARX approach

Djurovic

Bojaj

2020

This study examines, diagnoses, and assesses appropriate macroeconomic policy responses of the Montenegrin Government to the outbreak of COVID-19. The model econometrically measures the macroeconomic costs using a Bayesian VARX Litterman/Minessota prior to the pandemic disease in terms of demand and supply loss due to illness and closed activities and their effects on GDP growth in various pandemic scenarios. We explore five economic scenarios—shocks—using the available data from January 2006 to December 2019, following real out-of-sample forecasts generated from January 2020 to December 2020. Sensitivity scenarios spanning January 2020 to June 2020 from ± 10 to ± 60% were analyzed. We observed what happens to the supply and demand sides, namely, GDP, tourism, capital stock, human capital, health expenditures, economic freedom, and unemployment. The results show a toll on the GDP, tourism, unemployment, capital stock, and especially human capital for 2020. The recommended policy measures are public finance spending initiatives focused on securing employment and keeping highly qualified staff in Montenegrin companies. Considering all uncertainties, the rebound of the Montenegrin economy could take a few years to reach pre-COVID 19 output levels.

“…Usually, offline trust is the basis, for the online trust since customers will not use the online services of a bank whose physical services they do not trust (but may be prepared to use the online services of a bank they do trust). It means customers' experience with a bank can let customers accept the E-Banking services of the bank (Chaouali et al 2016;Shen et al 2020).…”

Section: Moderating Effect Of Trustmentioning

confidence: 99%

The effect of individual factors on user behaviour and the moderating role of trust: an empirical investigation of consumers’ acceptance of electronic banking in the Kurdistan Region of Iraq

Hamakhan

2020

The popularity of self-service technologies, particularly in the banking industry, more precisely with electronic banking channel services, has undergone a major change as individuals' lifestyles develop. This change has affected individuals’ decisions about accepting any new Information Technology, and Information Communications Technology services that are electronically mediated, for example, E-Banking channel services. This study investigates the effect of Individual Factors on User Behaviour, and the moderating role of Trust in the relationship between Individual Factors, and User Behaviour based on the Unified Theory of Acceptance and Use of Technology. This research proposes a model, with a second-order components research framework. It improves current explanations of the acceptance of electronic banking channel services. Furthermore, this study highlights the role of trust on the acceptance of electronic banking channel services, which is the most crucial consideration in customers’ decisions to accept electronic banking channels services. Thus, trust is the spine of the system in the Kurdistan Region of Iraq. Data were collected using an online questionnaire that received 476 valid responses from academic staff who work at the University of Sulaimani. The model tested data using the Partial Least Squares-Structural Equation Modelling approach. The results show that Individual Factors have a positive effect on User Behaviour. Besides, results show that trust moderates the relationship between Individual Factors and User Behaviour.