Results 1 to 10 of about 25,439 (153)
Technological and Economic Development of Economy, 2022 Conventional credit scoring models evaluated by predictive accuracy or profitability typically serve the financial institutions and can hardly reflect their contribution on financial stability.
Yufei Xia, Zijun Liao, Jun Xu, Yinguo Li
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Reject inference methods in credit scoring. [PDF]
J Appl Stat, 2021 The granting process is based on the probability that the applicant will refund his/her loan given his/her characteristics. This probability, also called score, is learnt based on a dataset in which rejected applicants are excluded. Thus, the population on which the score is used is different from the learning population.
Ehrhardt A +4 more
europepmc +5 more sources
ANÁLISIS DEL CREDIT SCORING [PDF]
RAE: Revista de Administração de Empresas, 2013 The problem of unpaid bank debts is becoming increasingly important in developed countries. Many empirical works are being published in an attempt to find a model capable of determining as accurately as possible whether an individual requesting a loan ...
Rosa Puertas Medina +1 more
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A credit scoring model based on the Myers–Briggs type indicator in online peer-to-peer lending [PDF]
Financial Innovation, 2022 Although psychometric features have been considered for alternative credit scoring, they have not yet been applied to peer-to-peer (P2P) lending because such information is not available on platforms.
Hyunwoo Woo, So Young Sohn
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Jurnal Teknik Industri, 2014 It is generally easier to predict defaults accurately if a large data set (including defaults) is available for estimating the prediction model. This puts not only small banks, which tend to have smaller data sets, at disadvantage.
Siana Halim, Yuliana Vina Humira
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NOTE: non-parametric oversampling technique for explainable credit scoring [PDF]
Scientific ReportsCredit scoring models are critical for financial institutions to assess borrower risk and maintain profitability. Although machine learning models have improved credit scoring accuracy, imbalanced class distributions remain a major challenge.
Seongil Han +4 more
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NATE: Non-pArameTric approach for Explainable credit scoring on imbalanced class. [PDF]
PLoS ONECredit scoring models play a crucial role for financial institutions in evaluating borrower risk and sustaining profitability. Logistic regression is widely used in credit scoring due to its robustness, interpretability, and computational efficiency ...
Seongil Han, Haemin Jung
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RAE: Revista de Administração de Empresas, 2013 O problema dos atrasos em pagamentos vem adquirindo grande importância nos países desenvolvidos. Neste trabalho, realizamos uma análise da capacidade preditiva de dois modelos paramétricos e de um não paramétrico, abordando, neste último, o problema da ...
Rosa Puertas Medina +1 more
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Extreme Learning Machine Enhanced Gradient Boosting for Credit Scoring
Algorithms, 2022 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 automatic and accurate discrimination of good and bad borrowers.
Yao Zou, Changchun Gao
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IMPROVING CREDIT SCORING MODEL OF MORTGAGE FINANCING WITH SMOTE METHODS IN SHARIA BANKING [PDF]
Russian Journal of Agricultural and Socio-Economic Sciences, 2019 Credit scoring is a feasibility test system to provide financing with the aim of reducing the risk of default on mortgage financing (KPR). This study analyze the characteristics of customers of PT Bank XYZ and design a credit scoring model for mortgage ...
Wibowo H.E., Mulyati H., Saptono I.T.
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