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How Secure Are Good Loans: Validating Loan-Granting Decisions And Predicting Default Rates On Consumer Loans

Jozef Zurada, Martin Zurada
2011 Review of Business Information Systems (RBIS)  
The paper uses three different data mining techniques (decision trees, neural networks, logit regression) and the ensemble model, which combines the three techniques, to predict whether a particular customer  ...  The paper then compares the effectiveness of each technique and analyzes the risk of default inherent in each loan and group of loans.  ...  This paper examines and compares the effectiveness of three data mining techniques and the ensemble model used in two scenarios to predict whether a consumer defaulted or paid off a loan.  ... 
doi:10.19030/rbis.v6i3.4563 fatcat:zedakioi5bcqldngdywto7pmuq

Credit Risk Assessment of Loan Defaulters in Commercial Banks Using Voting Classifier Ensemble Learner Machine Learning Model

Shrikant Kokate, Manna Sheela Rani Chetty
2021 International Journal of Safety and Security Engineering  
Based on this credit score of customers the bank will decide whether to approve loan or not. In banks there are major failures due to credit risks.  ...  To classify and predict the customers here various Machine learning techniques like gradient boosting, random forest and Feature Selection technique along with Decision Tree are used.  ...  The cross-fold validation technique is used split the data into train-test split. Test set data is then used for prediction and classification of loan defaulters.  ... 
doi:10.18280/ijsse.110508 fatcat:grdy77wuvvgg7foi3m5n3hputy

Predicting bad utility consumers in Malaysia

Alan Cheah Kah Hoe, Jaspaljeet Singh Dhillon
2014 Proceedings of the 6th International Conference on Information Technology and Multimedia  
The CRISP-DM (Cross-Industry Standard Process for data mining) model was employed in conducting the study.  ...  The study is conducted to identify the factors of customers who would default payment of their bills.  ...  In a similar work, the authors in [4] studied the data mining technique to predict non-paying customers of competitive local exchange carriers.  ... 
doi:10.1109/icimu.2014.7066636 fatcat:an5phnxzxvbhlkbkdbosrgznqu

Developing Prediction Model of Loan Risk in Banks Using Data Mining

Aboobyda Jafar Hamid, Tarig Mohammed Ahmed
2016 Machine Learning and Applications An International Journal  
In this paper a new model for classifying loan risk in banking sector by using data mining. The model has been built using data form banking sector to predict the status of loans.  ...  In addition, the number of transactions in banking sector is rapidly growing and huge data volumes are available which represent the customers behavior and the risks around loan are increased.  ...  INTRODUCTION There are various areas in which data mining can be used in financial sectors like customer segmentation and profitability, high risk loan applicants, predicting payment default, marketing  ... 
doi:10.5121/mlaij.2016.3101 fatcat:ojkvtjf7mja2loeoybdbfxdoqi

Loan Credibility Prediction System Based on Decision Tree Algorithm

Sivasree M S, Rekha Sunny T
2015 International Journal of Engineering Research and  
In this paper we introduce an effective prediction model for the bankers that help them predict the credible customers who have applied for loan.  ...  Data mining techniques can also be used in the banking industry which help them compete in the market well equipped.  ...  By using data mining techniques to analyze patterns and trends, bank executives can predict, with increased accuracy, how customers will react to adjustments in interest rates, which customers are likely  ... 
doi:10.17577/ijertv4is090708 fatcat:b7q3it3nxjbrtnvss5p677dqdy

Credit Risk Analysis and Prediction Modelling of Bank Loans Using R

Sudhamathy G.
2016 International Journal of Engineering and Technology  
The work in [5] proposes two credit scoring models using data mining techniques to support loan decisions for the Jordanian commercial banks. Considering the rate of accuracy, the results ISSN (  ...  RELATED WORK In [1] the author introduces an effective prediction model for predicting the credible customers who have applied for bank loan.  ...  Bank loan default risk analysis, Type of scoring and different data mining techniques like Decision Tree, Random forest, Boosting, Bayes classification, Bagging algorithm and other techniques used in financial  ... 
doi:10.21817/ijet/2016/v8i5/160805414 fatcat:jsmr5l5j6fbphf423uaccpoj7i


2013 American Journal of Applied Sciences  
Banking industries adopt the data mining technologies in various areas especially in customer segmentation and profitability, Predictions on Prices/Values of different investment products, money market  ...  business, fraudulent transaction detections, risk predictions, default prediction on pricing.  ...  They uses data mining techniques on existing customers to sell credit cards or increase customers credits or top up on credit card loans (Bhattacharya et al., 2011) .  ... 
doi:10.3844/ajassp.2013.1160.1165 fatcat:qmr3upclkvftlbgtudzeo43rjy

Risk Assessment of Internet Credit Based on Big Data Analysis

WANG HAORU, Yi Zhixuan, WEI YUJIA, Tianpeng Yao, Zhao Shuoheng, Xuzhi qiang, Y. Ahn, F. Wu
2020 E3S Web of Conferences  
loans has continued to increase.  ...  Based on the theories and technologies related to big data analysis, this paper comprehensively evaluates the online credit risk in the form of example data analysis, thereby effectively reducing the online  ...  group is 83.64%, of which the prediction accuracy rate of normal customers is 86.55%, and the prediction accuracy rate of default customers is 80.73%.  ... 
doi:10.1051/e3sconf/202021401012 fatcat:coowtd6vindrnd7u5w2fnpvqca

Enhancing Loan Default Prediction with Text Mining

Barry Egan, Kyle Goslin
2022 International Conference on Intelligent Environments  
This research examines if the text data contained in the loan applications of a peer-to-peer (P2P) lending platform can be utilized to enhance loan default prediction.  ...  Results showed that text data holds significant value for assessing credit risk, and when text data and numeric data are combined there is an enhancement in the prediction of loan default.  ...  Goslin / Enhancing Loan Default Prediction with Text Mining  ... 
doi:10.3233/aise220039 dblp:conf/intenv/EganG22 fatcat:dmd7tkertzctliywa65exje674

The Model and Empirical Research of Application Scoring based on Data Mining Methods

Lai Hui, Shuai Li, Zhou Zongfang
2013 Procedia Computer Science  
, the author establishes the static application scoring model based on the data mining methods of Logistic regression and MCLP.  ...  Specially, application scoring provides an important basis for the approval of customers the first time.  ...  In the prediction of the probability of default of personal customers, the customers are divided into default and non-default categories.  ... 
doi:10.1016/j.procs.2013.05.116 fatcat:mjeuqotur5bprh2xq3ejbd6hyy

Using Memory-Based Reasoning For Predicting Default Rates On Consumer Loans

Jozef Zurada, Robert M. Barker
2011 Review of Business Information Systems (RBIS)  
The paper then compares the performance of this technique to other data mining techniques proposed in earlier works and analyzes the risk of default inherent in each loan for each technique.  ...  In recent years, financial institutions have struggled with high default rates for consumer lending.  ...  mining techniques in predicting the credit worthiness of customers.  ... 
doi:10.19030/rbis.v11i1.4426 fatcat:jwkue2kiobcm3f4akr37jyujra

Customer Loan Approval Classification by Supervised Learning Model

2019 International journal of recent technology and engineering  
Machine Learning models are known to have a high accuracy on prediction problems, so in this paper we use some of the machine learning models in default loan prediction.  ...  In order to solve this problem, banks need to use thehelp of some techniques in predicting the loan repayment status.  ...  The reason for this research is to use different data mining techniques to compare the predictive accuracy of the default payment of the customer.  ... 
doi:10.35940/ijrte.d9275.118419 fatcat:ryx3n6ty6ve2nmiqsmprrvndma

Modeling consumer loan default prediction using ensemble neural networks

Amira Kamil Ibrahim Hassan, Ajith Abraham
In this paper, a loan default prediction model is constricted using three different training algorithms, to train a supervised two-layer feed-forward network to produce the prediction model.  ...  But first, two attribute filtering functions were used, resulting in two data sets with reduced attributes and the original data-set.  ...  Their main focus was to research customer classification and prediction in Customer Relation Management concerned with data mining based on Back propagation technique.  ... 
doi:10.1109/icceee.2013.6634029 fatcat:sy7u4jllznhrvhoxr2izdz5zem

Developing a model for validation and prediction of bank customer credit using information technology (case study of Dey Bank)

M.H. Yazdani
2017 Journal of Fundamental and Applied Sciences  
According to the analysis, interest rate parameter is more important in determining a customer validation.  ...  In this paper, in order to establish a communication between the final status and the parameters of facilities granted, data mining technique with the help of machine learning and neural networks have  ...  Data mining operations and techniques Operation Data Mining Techniques Predictive modeling Classification, prediction of value Database segmentation Statistical clustering Link analysis Discovery  ... 
doi:10.4314/jfas.v9i1s.693 fatcat:46t5rzx6zrcoboicdyw4dmwznq

Research on bank credit default prediction based on data mining algorithm

Li Ying
2018 The International Journal of Social Sciences and Humanities Invention  
Based on data mining technology, it is an effective method to classify loan customers by classification algorithm.  ...  This paper use the data mining classification algorithm to identify the risk customers from a large number of customers to provide an effective basis for the bank's loan approval.  ...  INTRODUCTION In today's information and digit age, bank credit default is still frequent, how to establish an effective model for the prediction whether bank customers will default on the loan for recognition  ... 
doi:10.18535/ijsshi/v5i6.09 fatcat:mq5wzzmheffnfkajivrviqp74y
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