Prediction of loan status in commercial bank using machine learning classifier

Arutjothi, G.; Senthamarai, C.

doi:10.1109/iss1.2017.8389442

Cited by 37 publications

(10 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is done with a combination of K -NN classifiers and the final objective is derived from R. The authors believe this model can help to build a tool that can predict loan status in commercial banks. The authors conclude that the proposed model provides an accuracy of 75% and can be improved with further study [15].…”

Section: Genetic Programming Was Used As It Is Flexible and Robust While Deep Learning Andmentioning

confidence: 90%

Review of Machine Learning models for Credit Scoring Analysis

Kumar¹,

Gunjan

2020

ing. Solidar

View full text Add to dashboard Cite

Introduction:Increase in computing power and the deeper usage of the robust computing systems in the financial system is propelling the business growth, improving the operational efficiency of the financial institutions, and increasing the effectiveness of the transaction processing solutions used by the organizations. Problem:Despite that the financial institutions are relying on the credit scoring patterns for analyzing the credit worthiness of the clients, still there are many factors that are imminent for improvement in the credit score evaluation patterns. Objective:Machine learning is offering immense potential in Fintech space and determining a personal credit score. Organizations by applying deep learning and machine learning techniques can tap individuals who are not being serviced by traditional financial institutions. Methodology:One of the major insights into the system is that the traditional models of banking intelligence solutions are predominantly the programmed models that can align with the information and banking systems that are used by the banks. But in the case of the machine-learning models that rely on algorithmic systems require more integral computation which is intrinsic. Results:The test analysis of the proposed machine learning model indicates effective and enhanced analysis process compared to the non-machine learning solutions. The model in terms of using various classifiers indicate potential ways in which the solution can be significant. Conclusion: If the systems can be developed to align with more pragmatic terms for analysis, it can help in improving the process conditions of customer profile analysis, wherein the process models have to be developed for comprehensive analysis and the ones that can make a sustainable solution for the credit system management. Originality:The proposed solution is effective and the one conceptualized to improve the credit scoring system patterns. Limitations: The model is tested in isolation and not in comparison to any of the existing credit scoring patterns.

show abstract

Section: Genetic Programming Was Used As It Is Flexible and Robust While Deep Learning Andmentioning

confidence: 90%

Review of Machine Learning models for Credit Scoring Analysis

Kumar¹,

Gunjan

2020

ing. Solidar

View full text Add to dashboard Cite

show abstract

“…The objective of this paper was to create a credit scoring model using machine learning approach. A prediction loan status for commercial banks using machine learning classifier was proposed [2], by analyzing model for credit data, they used a min-max normalization and KNN classifiers in their method. The model designed provided important information with efficient accuracy of 75%.…”

Section: Related Workmentioning

confidence: 99%

“…Nowadays, it helps in evaluating the loyalty of customers and helps the bank in minimizing possible loses and maximize the volume of credits [1]. Credit risk contains the peril taken by the bank, because a customer may fail to meet their loan obligations requiring prediction of the customer by using previous records and information for proper decisions [2]. Divers technology innovations have made it possible for banking sectors to incorporate efficient delivery channels, and deal with several challenges posed by the economy.…”

Section: Introductionmentioning

confidence: 99%

Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF

Alabi

Abdulsalam

Ogundokun

et al. 2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

Financial credit risk analysis management has been a foremost influence with a lot of challenges, especially for banks reducing their principal loss. In this study, the machine learning technique is a promising area used for credit scoring for analyzing risks in banks. It has become critical to extract beneficial knowledge for a great number of complex datasets. In this study, a machine learning approach using Chi-Square with SVM-RBF classifier was analyzed for Taiwan bank credit data. The model provides important information with enhanced accuracy that will help in predicting loan status. The experiment achieves 93% accuracy compared to the state-of-the-art.

show abstract

“…The aim of this paper [29] is to create a credit scoring model by using the loan status as the credit scoring model is used for accurate analysis of credit data to find defaulters and valid customers. Here, the machine learning classifier based analysis model for credit data is created using the combination of Min-Max normalization and K Nearest Neighbor (K-NN) classifier and is implemented using the software package R tool.…”

Section: Loan Defaulter Detectionmentioning

confidence: 99%

“…The K-NN classifier has been used for the prediction and to predict its performance they have calculated the accuracy, Root Mean Squared Error and Correlation.Then after comparing with other classifiers it has been concluded that the K-NN based credit scoring system provides higher accuracy than other classifiers which can be effectively used by commercial loan lenders to predict the loan applicant. The dataset is taken from Lending Club [29].…”

Section: Loan Defaulter Detectionmentioning

confidence: 99%

Emerging Approach for Detection of Financial Frauds Using Machine Learning

Bandyopadhyay¹,

Thakkar²,

Mukherjee³

et al. 2021

Preprint

View full text Add to dashboard Cite

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modeling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many other financial models can be modeled by implementing machine learning models. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, recall, precision, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of 97% and 96.84% respectively.

show abstract

Prediction of loan status in commercial bank using machine learning classifier

Cited by 37 publications

References 5 publications

Review of Machine Learning models for Credit Scoring Analysis

Review of Machine Learning models for Credit Scoring Analysis

Credit Risk Prediction in Commercial Bank Using Chi-Square with SVM-RBF

Emerging Approach for Detection of Financial Frauds Using Machine Learning

Contact Info

Product

Resources

About