Credit Risk Assessment Using Machine Learning Algorithms

Attigeri, Girija; Pai, M. M. Manohara; Pai, Radhika M.

doi:10.1166/asl.2017.9018

Cited by 17 publications

(8 citation statements)

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Section: Literature Reviewmentioning

confidence: 99%

“…While selecting credit risk assessment models for SMEs, traditional credit assessment methods, such as blind number assessment, support vector machine (SVM), Logistic, back propagation neural network, 22 and Decision tree are commonly used. [23][24][25][26] Some scholars used the integrated algorithm for credit risk assessment and found it is more effective than the traditional credit assessment model. [27][28][29][30] Kalayci and Arslan 29 used the RF algorithm to detect the nonperforming loan status of SMEs and found that the accuracy results of the RF algorithm were better than those of the Logistic, SVM, and Decision tree algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Financial credit risk assessment of online supply chain in construction industry with a hybrid model chain

Liu

et al. 2022

Int J of Intelligent Sys

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Financial credit risk assessment of online supply chain in construction industry with a hybrid model chain

Liu

et al. 2022

Int J of Intelligent Sys

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“…It was seen that the regression model was more accurate in properly classifying accepted applications and the neural network has the edge in classifying rejected cases. A similar comparison between these two models was also made in [6] where the authors used the chi square test to evaluate the bad customers. A thousand instances were used for the data set where the logistic regression outperformed the neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Machine Learning in Credit Risk Assessment: A Prelude to Smart Banking

Shoumo

Dhruba

Hossain

et al. 2019

TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)

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AcknowledgementsAs this meticulous journey comes to end, we have come across many well-wishers whose invaluable contribution and assistance made this project successful. Our beloved Professor, Mr. Hossain Arif is one such person. His continuous support and guidance has kept us motivated and enabled us to present our best in all cases. We would also like to thank Mr. Samiul Islam and Dr. Iftekharul Mobin for their continuous encouragement and expertise in this field. Last but certainly not the least; we would like to show our appreciation towards our families without whose unconditional support none of this would have been possible. AbstractA precise credit risk assessment system is vital to a financial institution for its proper and impeccable functioning. Accurate estimations of credit risk will allow them to continue their operation in a gainful and transparent way. As the rate of loan defaults are gradually increasing, bank authorities are finding it more and more difficult to correctly assess loan requests. Thus the subject of credit risk has become a highly conferred and examined topic throughout the world. Numerous solutions have been given, one being more efficient than the other and several studies are still being made for solving this difficult predicament. Thus keeping the implications of such a problematic matter in mind this paper proposes to build a machine learning model which can precisely assess credit risk and predict possible loan defaulters for any credit lending institution. Taking into account a borrower's financial and social history this paper proposes a way to accurately define whether a customer's loan request should be accepted or not which in turn can steadily save the creditor from incurring further loss. Evaluating data from previous successful borrowers and loan defaulters, a comparative analysis have been made using our supervised learning model and the results obtained can be used to predict the behavior of future borrowers. This model can assist a financial institution in assessing whether it should accept a loan request or not. Different combinations of feature selection algorithm and classifiers have been made and based upon metrics such as accuracy, AUC score, F1 score etc. the best model has been selected. Recursive feature elimination with cross validation (RFECV) and Principal Component Analysis (PCA) have been used to find the optimum number of features needed to make an accurate prediction. This allows us to make more efficient and optimal use of the limited available resources. The assessment will be performed in a supervised environment and so Support Vector Machines (SVM), Random Forest, Extreme Gradient Boosting and Logistic Regression have been used as the classifiers. In order to ensure all possible combinations have been properly tested k folds cross validation has been used to bring out a more balanced result. Furthermore, GridSearchCV has been used to tune the selected hyperparameters for each model in order to obtain the best result possible. And based upon this ...

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“…The χ 2 statistic is chosen as it is a well established technique for measuring independence. For example, it has been successfully used in text categorisation [27,28], credit risk assessments [29], several medical studies regarding Ebola patients [30] and Dementia [31] etc. Other techniques are also available for measuring independence, such as Paired t-test [32] and Pearson correlation [33].…”

Section: Test Of Independencementioning

confidence: 99%

Identifying irregularities in security event logs through an object-based Chi-squared test of independence

Parkinson

Khan

2018

Journal of Information Security and Applications

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A novel technique for identifying irregular event log entries is presented in this paper along with the implementation in a Microsoft Windows-based environment. The motivation behind this research is to identify irregular activity in a system whilst minimising any requirement on expert knowledge, in addition to saving investigative time and computing resources. As the developed solution utilises the standard Microsoft format for event logs, it can work with both live systems, as well as events extracted and stored for off-site analysis. The solution consists of two major steps: first, convert the event logs into objects-based model and second, perform statistical analysis using the Chi-squared (χ 2 ) test of independence and classify mean χ 2 values into discrete categories using Jenks natural breaks method. The event logs entries, which failed the test of dependence are considered as irregular events. It is also shown that the proposed solution poses an advantage over primitive frequency analysis methods as it uses object relationships among event log entries to determine irregularities for locating anomalous activities. Empirical analysis of the solution is performed using event logs data from 20 machines and shows promising results by correctly identifying irregular events. Further experimental analysis involving the insertion of synthetic irregular events results in an average accuracy of 85%.

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Credit Risk Assessment Using Machine Learning Algorithms

Cited by 17 publications

References 0 publications

Financial credit risk assessment of online supply chain in construction industry with a hybrid model chain

Financial credit risk assessment of online supply chain in construction industry with a hybrid model chain

Application of Machine Learning in Credit Risk Assessment: A Prelude to Smart Banking

Identifying irregularities in security event logs through an object-based Chi-squared test of independence

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