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An Improved Bank Credit Scoring Model: A Naïve Bayesian Approach

Olatunji Okesola · Kennedy Okokpujie · Adeyinka Ajao Adewale · Samuel Ndueso John · Omoruyi Osemwegie

27 citations0/0 files storedInternational Conference on Computational Science and Computational Intelligence (CSCI)

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

Abstract

Credit scoring is a decision tool used by organizations to grant or reject credit requests from their customers. Series of artificial intelligent and traditional approaches have been used to building credit scoring model and credit risk evaluation. Despite being ranked amongst the top 10 algorithm in Data mining, Naïve Bayesian algorithm has not been extensively used in building credit score cards. Using demographic and material indicators as input variables, this paper investigate the ability of Bayesian classifier towards building credit scoring model in banking sector.

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References

2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research 2006Credit scoring with a data mining approach based on support vector machines 2000A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers 2004Neural network ensemble strategies for financial decision applications 1996A k-Nearest-Neighbour Classifier for Assessing Consumer Credit Risk 2011Using data mining to improve assessment of credit worthiness via credit scoring models 2007Neural nets versus conventional techniques in credit scoring in Egyptian banking 2009Genetic programming for credit scoring: The case of Egyptian public sector banks

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2021Swindle: Predicting the Probability of Loan Defaults using CatBoost Algorithm 2022Extreme Learning Machine Enhanced Gradient Boosting for Credit Scoring

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