Differentiating between good credits and bad credits using neuro-fuzzy systems

DOI10.1016/S0377-2217(01)00052-2zbMath1087.91515OpenAlexW1964074981MaRDI QIDQ5955100

D. K. Malhotra, Rashmi Malhotra

Publication date: 2002

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0377-2217(01)00052-2

zbMATH Keywords

Neural networksConsumer loansNeuro-fuzzy systems

Mathematics Subject Classification ID

Related Items (12)

Credit rating analysis using adaptive fuzzy rule-based systems: an industry-specific approach ⋮ An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring ⋮ Instance-based credit risk assessment for investment decisions in P2P lending ⋮ A data analytic approach to forecasting daily stock returns in an emerging market ⋮ Robust cost-sensitive kernel method with Blinex loss and its applications in credit risk evaluation ⋮ Cost-sensitive thresholding over a two-dimensional decision region for fraud detection ⋮ Multistage decision-making fuzzy methodology for optimal investments based on experts' evaluations ⋮ Forecasting business profitability by using classification techniques: a comparative analysis based on a Spanish case ⋮ Designing a hybrid intelligent mining system for credit risk evaluation ⋮ Credit risk evaluation with a least squares fuzzy support vector machines classifier ⋮ Support vector machines for default prediction of SMEs based on technology credit ⋮ Cutoff Threshold Decisions for Classification Algorithms with Risk Aversion

Uses Software

• Fuzzy Logic Toolbox
• ANFIS

Cites Work

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• Structure identification of fuzzy model
• A neural network for classifying the financial health of a firm
• Wrappers for feature subset selection
• A comparison of neural networks and linear scoring models in the credit union environment
• Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis
• Fuzzy identification of systems and its applications to modeling and control
• Managerial Applications of Neural Networks: The Case of Bank Failure Predictions

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