Article2010

Credit Scoring Models using Soft Computing Methods: A Survey

Adel Lahsasna · Raja Noor Ainon · Teh Ying Wah

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Financial Distress and Bankruptcy Prediction Data Mining and Machine Learning Applications Imbalanced Data Classification Techniques

Abstract

Abstract: During the last fifteen years, soft computing methods have been successfully applied in building powerful and flexible credit scoring models and have been suggested to be a possible alternative to statistical methods. In this survey, the main soft computing methods applied in credit scoring models are presented and the advantages as well as the limitations of each method are outlined. The main modelling issues are discussed especially from the data mining point of view. The study concludes with a series of suggestions of other methods to be investigated for credit scoring modelling.

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References

1997Statistical Classification Methods in Consumer Credit Scoring: A Review 2000Neural network credit scoring models 2003Benchmarking state-of-the-art classification algorithms for credit scoring 2006Credit scoring with a data mining approach based on support vector machines 1996A comparison of neural networks and linear scoring models in the credit union environment 2005A new fuzzy support vector machine to evaluate credit risk 2005Building credit scoring models using genetic programming 2004Neural network ensemble strategies for financial decision applications

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2022Credit scoring methods: Latest trends and points to consider 2016Classification methods applied to credit scoring: Systematic review and overall comparison 2019Machine learning and decision support system on credit scoring 2018Predicting credit risk on the basis of financial and non-financial variables and data mining 2020Artificial neural network and Bayesian network models for credit risk prediction

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