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Enterprise Credit Risk Evaluation models: A Review of Current Research Trends

Ming-Chang Lee
2012 International Journal of Computer Applications  
Enterprise Credit Risk becomes important issue in financial and accounting.  ...  It includes bankruptcy prediction, financial distress, corporate performance clustering / prediction and credit risk estimation.  ...  The support vectors are then used to construct an optimal linear separating hyperplane or a linear regression function in this feature space.  ... 
doi:10.5120/6311-8643 fatcat:n4gqs53qqzdg7g3hjrzhlmjd5y

Credit evaluation model of loan proposals for Indian Banks

Seema Purohit, Anjali Kulkarni
2011 2011 World Congress on Information and Communication Technologies  
The integrated model is a combination model based on the techniques of Logistic Regression, Multilayer Perceptron Model, Radial Basis Neural Network, Support Vector Machine and Decision tree (C4.5) and  ...  The failure and success of the Banking Industry depends largely on industry's ability to properly evaluate credit risk.  ...  ACKNOWLEDGMENT In this study many persons and Institution assisted us and without their help this study would not have been completed. A.  ... 
doi:10.1109/wict.2011.6141362 fatcat:7x6k4ndbnrd7lculfacezbwo4m

Electronic Credit Card Fraud Detection System by Collaboration of Machine Learning Models

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
This presented paper focuses on fraud activities that cannot be detected manually by carrying out research and examine the results of logistic regression, decision tree and support vector machine.  ...  So to overcome this problem of fraud, hoax, cheat in the financial sector a fraud identification system is needed to identify the cheating, fraud and alike activities in internet-based money transactions  ...  Regression of logistics is alike to linear regression, as the direct row is acquired in the linear regression, logistic regression indicates a curve.  ... 
doi:10.35940/ijitee.l1028.10812s19 fatcat:qrmzqnwvavbspmfxh4londkcpy

Traditional Models Used in Evaluation of Requests for Credit Card and Alternatives (AHP)

Agerti Galo
2017 Journal of Educational and Social Research  
Therefore they used automatic procedures based on computer and statistical models to assess the credit card requirements.  ...  Using AHP by banks in their decision-making needs would bring them a low cost and it would be highly efficient.  ...  Logistic regression method is equally difficult to use as a linear regression model and requires people with a certain level of education to use.  ... 
doi:10.5901/jesr.2017.v7n1p131 fatcat:speo3776ejbo3nphd53ybfyjom

Performance Comparison of Data Mining Algorithm to Predict Approval of Credit Card

Ipin Sugiyarto, Bibit Sudarsono, Umi Faddillah
2019 SinkrOn  
The purpose of this study is to implement and compare Support Vector Machine, Logistic Regression and Neural Network algorithms based on PCA and optimize PSO (Particle Swarm Optimization) to improve accuracy  ...  Accurate measurement and good management ability in dealing with credit risk is an effort to save the economic operations unit and be beneficial for a stable and healthy financial system.  ...  , support vector machines and logistic regression to conduct data experiments.  ... 
doi:10.33395/sinkron.v4i1.10181 fatcat:44zo5fw3ibglhfzl6i2xcuulsa

Intelligent credit scoring model using soft computing approach

A. Lahsasna, R.N. Ainon, Teh Ying Wah
2008 2008 International Conference on Computer and Communication Engineering  
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  ...  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 classification methods were linear regression (and its quadratic variant), logistic regression, linear programming, four variants of vector support machines, four variants of classification trees,  ... 
doi:10.1109/iccce.2008.4580635 fatcat:qgj42ys4szgqbga3ny32kuckem

Credit scoring in banks and financial institutions via data mining techniques: A literature review

Seyed Mahdi sadatrasoul, Mohammadreza gholamian, Mohammad Siami, Zeynab Hajimohammadi
2013 Journal of Artificial Intelligence and Data Mining  
Also ensemble methods, support vector machines and neural network methods are the most favorite techniques used recently.  ...  The findings of the review reveals that data mining techniques are mostly applied to individual credit score and there are a few researches on enterprise and SME credit scoring.  ...  Thomas surveys the statistical and operational research techniques used to support credit and behavioral scoring decisions.  ... 
doi:10.22044/jadm.2013.124 doaj:8e8e42083d7c4db9a5eef786a0f2eaa9 fatcat:rlic3qipxvdubh5stt6xokdczy

LOAN APPROVAL PREDICTION BASED ON MACHINE LEARNING APPROACH

Azeez A. Nureni, O. E. Adekola
2022 FUDMA Journal of Sciences  
Eight different algorithms were used to train the models, these are: the Logistic Regression algorithm, Random forest, Decision trees, Linear Regression, Support Vector Machine (SVM), Naïve Bayes, K-means  ...  Banks have various goods to sell in the banking system. The major source of income and profit, however, is their credit lines. As a result, they can profit from the interest on the loans they credit.  ...  Bekhet and Eletter in 2012 attempted to develop a model with Artificial Neural Networks (ANN) as a decision support system for Jordanian commercial banks to assist in credit approval evaluation.  ... 
doi:10.33003/fjs-2022-0603-830 fatcat:poqjulztvrcq5joszimvuxm5pa

A state of the art survey of data mining-based fraud detection and credit scoring

Xun Zhou, Sicong Cheng, Meng Zhu, Chengkun Guo, Sida Zhou, Peng Xu, Zhenghua Xue, Weishi Zhang, Nader Asnafi
2018 MATEC Web of Conferences  
society, have fraud detection, credit scoring and other risk management systems become so important not only to some specific firms, but to industries and governments worldwide.  ...  In the past twenty years, amounts of studies have proposed the use of data mining techniques to detect frauds, score credits and manage risks, but issues such as data selection, algorithm design, and hyperparameter  ...  Unlike decision trees, logistic regression is a linear model, hence unable to solve complex non-linear problems.  ... 
doi:10.1051/matecconf/201818903002 fatcat:iri4vifvjzfpbgvof6qgqlfyvu

Various Methods for Fraud Transaction Detection in Credit Cards

Hardik Manek, Nikhil Kataria, Sujai Jain, Chitra Bhole
2019 Journal of Ubiquitous Systems and Pervasive Networks  
Moreover, Logistic Regression is implemented, and the results obtained are highlighted.  ...  Supervised algorithms such as Logistic Regression can be used to build a model that will predict the output in the form of binary classes i.e. 0 for a valid transaction and 1 for a fraudulent transaction  ...  Duman [5] proposed an approach to identify credit card fraud using the decision tree and the Support Vector Machine.  ... 
doi:10.5383/juspn.12.01.004 fatcat:4qj5vra6prhk5h4p3bwxyixoiy

A Benchmark to Select Data Mining Based Classification Algorithms for Business Intelligence and Decision Support Systems

Pardeep Kumar
2012 International Journal of Data Mining & Knowledge Management Process  
The objective of this paper is to compare various classification algorithms that have been frequently used in data mining for decision support systems.  ...  Three decision trees based algorithms, one artificial neural network, one statistical, one support vector machines with and without ada boost and one clustering algorithm are tested and compared on four  ...  Such programs are named as decision support systems (DSSs).  ... 
doi:10.5121/ijdkp.2012.2503 fatcat:lafcznbgrzgcnb3jys4rh27x5e

Optimized algorithm for Credit Scoring

Annie Chacko
2020 International Journal of Advanced Trends in Computer Science and Engineering  
Linear discriminant analysis and logistic regression are the two most commonly used statistical techniques in credit scoring.  ...  Machine learning techniques include K-nearest neighbor, support vector machine, decision tree and neural network. Use different best algorithms for classify the credit scoring data sets.  ...  Some statistical techniques widely used are Linear Discriminant Analysis, Logistic Regression Analysis, and Multivariate Adaptive Regression Splines etc.  ... 
doi:10.30534/ijatcse/2020/5691.32020 fatcat:rptoqh5ed5bjfh5pvposvkjlxe

Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques

Md. Golam Kibria, College of Business and Innovation, The University of Toledo, Toledo, OH 43606-3390 USA, Mehmet Sevkli
2021 International Journal of Machine Learning and Computing  
Secondly, the performance of the built model is compared with the other two traditional machine learning algorithms: logistic regression (LR) and support vector machine (SVM).  ...  Some machine learning algorithms have also been used to support the decision.  ...  The built model is then applied for the credit card data set and compared the results with logistic regression and support vector machine models.  ... 
doi:10.18178/ijmlc.2021.11.4.1049 fatcat:bmruoznnqnda5bg3ae6tapfajy

Comparative Evaluation of Predictive Modeling Techniques on Credit Card Data

Ravinder Singh, Rinkle Rani Aggarwal
2011 Journal of clean energy technologies  
All the tools such as LDA (Linear Discriminant Analysis), SVM (support vector machines), Kernel density estimation, LR (logistic regression), GP(genetic programming), K neighborhood, which are available  ...  Credit Scoring studies are very important for any financial house. Both traditional statistical and modern data mining/machine learning tools have been evaluated in the credit scoring problem.  ...  Parametric techniques encompass linear regression, generalized linear regression, logistic regression and discriminant analysis.  ... 
doi:10.7763/ijcte.2011.v3.377 fatcat:nc4lmm7zhjexto2hd4pqahil2m

CREDIT SCORING, STATISTICAL TECHNIQUES AND EVALUATION CRITERIA: A REVIEW OF THE LITERATURE

Hussein A. Abdou, John Pointon
2011 International Journal of Intelligent Systems in Accounting, Finance & Management  
Moreover, the idea of reducing the probability of a customer defaulting, which predicts customer risk, is a new role for credit scoring, which can support and help maximize the expected profit from that  ...  Besides, the high capabilities of computing technology make the use of credit scoring much easier than before. Consequently the history of credit scoring is short, and the literature is very limited.  ...  Acknowledgement The authors would like to thank the editor and anonymous referees for helpful comments, which have been useful in revising the manuscript.  ... 
doi:10.1002/isaf.325 fatcat:wxi3lpjoyvbf3pjvfl5i7ciu7q
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