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A Soft Intelligent Risk Evaluation Model for Credit Scoring Classification
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/int.20410openalex-snapshot
Credit risk modeling using bayesian networks
inferredcites →https://doi.org/10.1002/isaf.325openalex-snapshot
CREDIT SCORING, STATISTICAL TECHNIQUES AND EVALUATION CRITERIA: A REVIEW OF THE LITERATURE
inferredcites →https://doi.org/10.1007/s00500-012-0805-9openalex-snapshot
Combining seasonal ARIMA models with computational intelligence techniques for time series forecasting
inferredcites →https://doi.org/10.1016/j.aei.2006.12.004openalex-snapshot
Building contextual classifiers by integrating fuzzy rule based classification technique and k-nn method for credit scoring
inferredcites →https://doi.org/10.1016/j.asoc.2008.08.002openalex-snapshot
Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach
inferredcites →https://doi.org/10.1016/j.csda.2011.06.036openalex-snapshot
Credit scoring analysis using a fuzzy probabilistic rough set model
inferredcites →https://doi.org/10.1016/j.dss.2010.11.022openalex-snapshot
An evolution strategy-based multiple kernels multi-criteria programming approach: The case of credit decision making
inferredcites →https://doi.org/10.1016/j.ejor.2012.04.009openalex-snapshot
An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data
inferredcites →https://doi.org/10.1016/j.eswa.2004.12.022openalex-snapshot
Hybrid mining approach in the design of credit scoring models
inferredcites →https://doi.org/10.1016/j.eswa.2004.12.031openalex-snapshot
A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines
inferredcites →https://doi.org/10.1016/j.eswa.2005.01.004openalex-snapshot
A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms
inferredcites →https://doi.org/10.1016/j.eswa.2006.07.007openalex-snapshot
Credit scoring with a data mining approach based on support vector machines
inferredcites →https://doi.org/10.1016/j.eswa.2007.10.005openalex-snapshot
Neural networks and statistical techniques: A review of applications
inferredcites →https://doi.org/10.1016/j.eswa.2008.01.005openalex-snapshot
Support vector machines for credit scoring and discovery of significant features
inferredcites →https://doi.org/10.1016/j.eswa.2008.09.028openalex-snapshot
Prediction model building with clustering-launched classification and support vector machines in credit scoring
inferredcites →https://doi.org/10.1016/j.eswa.2009.05.044openalex-snapshot
An artificial neural network (p,d,q) model for timeseries forecasting
inferredcites →https://doi.org/10.1016/j.eswa.2009.12.025openalex-snapshot
Combination of feature selection approaches with SVM in credit scoring
inferredcites →https://doi.org/10.1016/j.eswa.2011.02.179openalex-snapshot
Neighborhood rough set and SVM based hybrid credit scoring classifier
inferredcites →https://doi.org/10.1016/j.eswa.2011.08.093openalex-snapshot
Does segmentation always improve model performance in credit scoring?
inferredcites →https://doi.org/10.1016/j.eswa.2011.08.116openalex-snapshot
A novel hybrid classification model of artificial neural networks and multiple linear regression models
inferredcites →https://doi.org/10.1016/j.eswa.2011.08.120openalex-snapshot
A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model
inferredcites →https://doi.org/10.1016/j.eswa.2014.08.029openalex-snapshot
Credit scoring using the clustered support vector machine
inferredcites →https://doi.org/10.1016/j.fss.2007.10.011openalex-snapshot
A new hybrid artificial neural networks and fuzzy regression model for time series forecasting
inferredcites →https://doi.org/10.1016/j.neucom.2008.04.017openalex-snapshot
Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs)
inferredcites →https://doi.org/10.1016/s0305-0548(99)00149-5openalex-snapshot
Neural network credit scoring models
inferredcites →https://doi.org/10.1016/s0957-4174(01)00036-7openalex-snapshot
The cluster-indexing method for case-based reasoning using self-organizing maps and learning vector quantization for bond rating cases
inferredcites →https://doi.org/10.1016/s0957-4174(02)00044-1openalex-snapshot
Credit scoring using the hybrid neural discriminant technique
inferredcites →https://doi.org/10.1016/s0957-4174(02)00045-3openalex-snapshot
A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction
inferredcites →https://doi.org/10.1080/07421222.1999.11518234openalex-snapshot
Dynamics of Modeling in Data Mining: Interpretive Approach to Bankruptcy Prediction
inferredcites →https://doi.org/10.1109/icpr.2002.1047792openalex-snapshot
Learning Bayesian network classifiers for credit scoring using Markov chain Monte Carlo search
inferredcites →https://doi.org/10.1109/ihmsc.2009.221openalex-snapshot
Hybrid Fuzzy SVM Model Using CART and MARS for Credit Scoring
inferredcites →https://doi.org/10.1109/tsmcb.2008.924587openalex-snapshot
Uncertainty Modeling of Improved Fuzzy Functions With Evolutionary Systems
inferredcites →https://doi.org/10.1137/1.9780898718317openalex-snapshot
Credit Scoring and Its Applications
inferredcites →https://doi.org/10.2307/2330408openalex-snapshot
A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior
inferredcites →https://doi.org/10.2307/2348414openalex-snapshot
A k-Nearest-Neighbour Classifier for Assessing Consumer Credit Risk
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.3206607openalex-extracted
Soft Data Modeling Via Type 2 Fuzzy Distributions for Corporate Credit Risk Assessment in Commercial Banking
inferred← citeshttps://doi.org/10.1007/978-3-030-02574-8_36openalex-snapshot
Soft Data Modeling via Type 2 Fuzzy Distributions for Corporate Credit Risk Assessment in Commercial Banking
inferred← citeshttps://doi.org/10.1007/978-3-032-06179-9_17openalex-snapshot
Big Data Analytics for Financial Decisions of Companies: A Systematic Literature Review
inferred← citeshttps://doi.org/10.1007/s00521-019-04537-7openalex-snapshot
Machine learning and decision support system on credit scoring
inferred← citeshttps://doi.org/10.1016/j.aei.2020.101130openalex-snapshot
New hybrid data mining model for credit scoring based on feature selection algorithm and ensemble classifiers
inferred← citeshttps://doi.org/10.1016/j.procs.2025.09.215openalex-snapshot
Binary transformer-based classifier
inferred← citeshttps://doi.org/10.1109/rteict42901.2018.9012366openalex-snapshot
Predictive Risk Analysis For Loan Repayment of Credit Card Clients
inferred← citeshttps://doi.org/10.1117/12.2631591openalex-snapshot
Big data analysis of credit card default detection
inferred← citeshttps://doi.org/10.1155/2018/9390410openalex-snapshot
A Pruning Neural Network Model in Credit Classification Analysis
inferred← citeshttps://doi.org/10.1155/acis/4363446openalex-snapshot
Unlocking Nonlinear Dynamics in Fuzzy Cognitive Maps: A Quantum Kernel Approach
inferred← citeshttps://doi.org/10.1186/s40537-022-00665-5openalex-snapshot
Two-stage credit scoring using Bayesian approach
inferred← citeshttps://doi.org/10.14254/2071-789x.2019/12-1/15openalex-snapshot
Impact of baseline population on credit score’s predictive power
inferred← citeshttps://doi.org/10.3390/su14010130openalex-snapshot
Can System Log Data Enhance the Performance of Credit Scoring?—Evidence from an Internet Bank in Korea
inferred← citeshttps://doi.org/10.36948/ijfmr.2023.v05i03.3298openalex-snapshot
Credit Risk Analysis Using Fuzzy Logic with Machine Learning Models
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