Results 21 to 30 of about 744,755 (195)
A practical approach to credit scoring
Expert Systems with Applications, 2008 This paper proposes a DEA-based approach to credit scoring. Compared with conventional models such as multiple discriminant analysis, logistic regression analysis, and neural networks for business failure prediction, which require extra a priori information, this new approach solely requires ex-post information to calculate credit scores.
Jae H. Min, Young-Chan Lee
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A Deep Learning Based Online Credit Scoring Model for P2P Lending
IEEE Access, 2020 Credit scoring models have been widely used in traditional financial institutions for many years. Using these models in P2P Lending have limitations. First, the credit data of P2P usually contains dense numerical features and sparse categorical features.
Zaimei Zhang, Kun Niu, Yan Liu
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Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network
MATEC Web of Conferences, 2016 Credit risk assessment for bank customers has gained increasing attention in recent years. Several models for credit scoring have been proposed in the literature for this purpose.
Hamdy Abeer, Hussein Walid B.
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tripleC: Communication, Capitalism & Critique, 2010 The system of credit scoring has been built up in recent times on the basis of a compromise struck between individuality and surveillance in ways that boosted consumption through consumer debt.
Thomas Fay Ruddy
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Neural Net Credit Scoring Models for Egyptian Banks: An Evaluation of Consumer Loans [PDF]
, 2007 Credit scoring is regarded as a core statistical appraisal tool of financial institutions in general and banks in particular during the last few decades. Furthermore, neural nets have become one of the most important tools using in credit scoring. In
Abdou, Hussein +2 more
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A Soft Intelligent Risk Evaluation Model for Credit Scoring Classification
International Journal of Financial Studies, 2015 Risk management is one of the most important branches of business and finance. Classification models are the most popular and widely used analytical group of data mining approaches that can greatly help financial decision makers and managers to tackle ...
Mehdi Khashei, Akram Mirahmadi
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BUILDING A CREDIT SCORING MODEL BASED ON THE TYPE OF TARGET VARIABLE
Шәкәрім университетінің хабаршысы: Техника ғылымдар, 2023 With the rapid development of big data and Internet technologies, companies engaged in big data financial platforms collect and systematize massive data through their own platforms, improve credit scoring parameters and use machine learning methods to ...
Z. M. Ordabayeva, A. N. Moldagulova
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, 2015 RESUMEN: El credit scoring es un sistema de modelos de decisión a través del cual se calcula la probabilidad de que un sujeto sea capaz de devolver o no un crédito comercial. Este trabajo realiza una simulación de este modelo con la intención de familiarizarse con el funcionamiento del mismo, así como con el marco teórico que este conlleva.
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Remarks on Statistical Measures for Assessing Quality of Scoring Models
Acta Universitatis Lodziensis. Folia Oeconomica, 2019 Granting a credit product has always been at the heart of banking. Simultaneously, banks are obligated to assess the borrower’s credit risk. Apart from creditworthiness, to grant a credit product, banks are using credit scoring more and more often ...
Adam Piotr Idczak
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Predicting Credit Scores with Boosted Decision Trees
Forecasting, 2022 Credit scoring models help lenders decide whether to grant or reject credit to applicants. This paper proposes a credit scoring model based on boosted decision trees, a powerful learning technique that aggregates several decision trees to form a ...
João A. Bastos
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