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Credit risk evaluation: a comprehensive study

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Abstract

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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Data availability

All data generated or analysed during this study are included in this article.

Abbreviations

AdaBoost:

Adaptive Boosting

ANFIS:

Adaptive Neuro-Fuzzy Inference System

ANN:

Artificial Neural Network

AUC:

Area Under Curve

BPNN:

Back-Propogation Neural Network

CART:

Classification And Regreesion Tree

CCR:

Candidate Classifier Repository

CGD:

Conjugate Gradient Desecent

CNN:

Convolutional Neural Network

ConsA:

Consensus Approach

CRJ:

Cycle Reservoir with Regular Jump

CSVM:

Clustered Suport Vector Machine

DA:

Discriminant Networks

DAG:

Directed Acylic Graph

DNN:

Deep Neural Network

DP:

Discriminate Power

DT:

Decision Tree

EAD:

Exposure At Default

EmNN:

Emotional Neural Network

EMPNGA:

Enhanced Multi-Population Niche Genetic Algorithm

FKNN:

Fuzzy K-Nearest Neighbour

FNN:

Feedforward Neural Network

GA:

Genetic Algorithm

GBDT:

Gradient Boosting Decision Tree

GD:

Gradient Descent

GNG:

Gabriel Neighbourhood Graph

GRNN:

General Regession Neural Network

GWO:

Grey Wolf Optimization

HMM:

Hidden Markov Model

IFOA:

Improved Fruit Fly Optimization Algorithm

IMF:

International Monetary Fund

KDD:

Knowledge Discovery in Data

KNN:

K- Nearest Neighbour

LDA:

Linear Discriminant Anaysis

LGD:

Loss Given Default

LM:

Levenberg – Marquadt

LR:

Logistic Regression

MARS:

Multivariate Adaptive Regression Splines

MLP:

Multilayer Perception

MLPNN:

Multilayer Perception Neural Network

MODE-GL:

Multi-Objective Evolutionary Algorithm

MPGA:

Multiple Population Genetic Algorithm

MSE:

Mean Squared Error

NB:

Naïve Bayes

NN:

Neural Network

OS:

One-step Secant

P2P:

Peer To Peer

PD :

Probability of Default

PNN:

Probalistic Neural Network

PSO:

Particle Swarm Optimization

PTVPSO:

Parallel TVPSO

RBF:

Radial Basis Function

RF:

Random Forest

RFoGAPS:

Random Forest optimized by genetic algorithm with profit score

RNN:

Recurrent Neural Network

ROC:

Receiver operating Characteristic

RoS:

Random Over Sampling

SME:

Small- and Medium-sized Enterprises

SMOTE:

Synthetic Minority Over-Sampling Technique

SVM:

Support vector Machine

TLP:

Traditional Linear Programming

TVPSO:

Time Variant Particle Swarm Optimization

UNCTAD:

UN Conference on Trade and Development

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Computer Science, Gour Mahavidyalaya, Malda, West Bengal, India

    Arijit Bhattacharya

  2. Department of Computer Science & Engineering, National Institute of Technology, Silchar, Assam, India

    Saroj Kr. Biswas

  3. Department of Computer Science & Technology, University of North Bengal, Darjeeling, West Bengal, India

    Ardhendu Mandal

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  1. Arijit Bhattacharya
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  2. Saroj Kr. Biswas
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  3. Ardhendu Mandal
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Correspondence to Arijit Bhattacharya.

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Bhattacharya, A., Biswas, S.K. & Mandal, A. Credit risk evaluation: a comprehensive study. Multimed Tools Appl 82, 18217–18267 (2023). https://doi.org/10.1007/s11042-022-13952-3

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