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Credit risk evaluation: a comprehensive study

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To date, there has been relatively little research in the field of credit risk analysis that compares all of the well known statistical, optimization technique (heuristic methods) and machine learning based approaches in a single article. Review on credit risk assessment using sixteen well-known approaches has been conducted in this work. The accuracy of the machine learning approaches in dealing with financial difficulties is superior to that of traditional statistical methods, especially when dealing with nonlinear patterns, according to the findings. Hybrid or Ensemble algorithms, on the other hand have been found to outperform their traditional counterparts – standalone classifiers in the vast majority of situations. Finally, the paper compares the models with nine machine learning classifiers utilizing two benchmark datasets. In this study, we have encountered with 46 datasets, among them 35 datasets have been utilized for once; whereas among the other 11 datasets, Australian, German and Japanese are the three most frequently utilized datasets by the researchers. The study showed that the performance of ensemble classifiers were very much significant. As per the experimental result, for both datasets ensemble classifiers outperformed other standalone classifiers which validate with the prior research also. Although some of these approaches have a high level of accuracy, additional study is required to discover the right parameters and procedures for better outcomes in a transparent manner. Additionally this study is a valuable reference source for analyzing credit risk for both academic and practical domains, since it contains relevant information on the most major machine learning approaches employed so far.

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Adaptive Boosting


Adaptive Neuro-Fuzzy Inference System


Artificial Neural Network


Area Under Curve


Back-Propogation Neural Network


Classification And Regreesion Tree


Candidate Classifier Repository


Conjugate Gradient Desecent


Convolutional Neural Network


Consensus Approach


Cycle Reservoir with Regular Jump


Clustered Suport Vector Machine


Discriminant Networks


Directed Acylic Graph


Deep Neural Network


Discriminate Power


Decision Tree


Exposure At Default


Emotional Neural Network


Enhanced Multi-Population Niche Genetic Algorithm


Fuzzy K-Nearest Neighbour


Feedforward Neural Network


Genetic Algorithm


Gradient Boosting Decision Tree


Gradient Descent


Gabriel Neighbourhood Graph


General Regession Neural Network


Grey Wolf Optimization


Hidden Markov Model


Improved Fruit Fly Optimization Algorithm


International Monetary Fund


Knowledge Discovery in Data


K- Nearest Neighbour


Linear Discriminant Anaysis


Loss Given Default


Levenberg – Marquadt


Logistic Regression


Multivariate Adaptive Regression Splines


Multilayer Perception


Multilayer Perception Neural Network


Multi-Objective Evolutionary Algorithm


Multiple Population Genetic Algorithm


Mean Squared Error


Naïve Bayes


Neural Network


One-step Secant


Peer To Peer

PD :

Probability of Default


Probalistic Neural Network


Particle Swarm Optimization


Parallel TVPSO


Radial Basis Function


Random Forest


Random Forest optimized by genetic algorithm with profit score


Recurrent Neural Network


Receiver operating Characteristic


Random Over Sampling


Small- and Medium-sized Enterprises


Synthetic Minority Over-Sampling Technique


Support vector Machine


Traditional Linear Programming


Time Variant Particle Swarm Optimization


UN Conference on Trade and Development


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Bhattacharya, A., Biswas, S.K. & Mandal, A. Credit risk evaluation: a comprehensive study. Multimed Tools Appl 82, 18217–18267 (2023).

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