A Better Comparison Summary of Credit Scoring Classification

Sharjeel Imtiaz · James Allan

18 citations0/0 files storedInternational Journal of Advanced Computer Science and Applications

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Financial Distress and Bankruptcy Prediction Customer churn and segmentation Imbalanced Data Classification Techniques

Abstract

The credit scoring aim is to classify the customer credit as defaulter or non-defaulter. The credit risk analysis is more effective with further boosting and smoothing of the parameters of models. The objective of this paper is to explore the credit score classification models with an imputation technique and without imputation technique. However, data availability is low in case of without imputation because of missing values depletion from the large dataset. On the other hand, imputation based dataset classification accuracy with linear method of ANN is better than other models. The comparison of models with boosting and smoothing shows that error rate is better metric than area under curve (AUC) ratio. It is concluded that artificial neural network (ANN) is better alternative than decision tree and logistic regression when data availability is high in dataset.

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References

2000Neural network credit scoring models 2008The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients 1985Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress 2002Credit Scoring and Its Applications 1996A comparison of neural networks and linear scoring models in the credit union environment 1980A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior 2008Support vector machines for credit scoring and discovery of significant features 2004Neural network ensemble strategies for financial decision applications

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2018Using data mining approaches to build credit scoring model: Case study — Implementation of credit scoring model in microfinance institution 2021An implementation of ensemble methods, logistic regression, and neural network for default prediction in Peer-to-Peer lending 2017Deep Learning for Distribution Channels' Management

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