Data mining algorithm for development of a predictive model for mitigating loan risk in Nigerian banks (original) (raw)
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International Journal of Innovative Technology and Exploring Engineering, 2020
Data mining is the key tools for discoveries of knowledge from large data set. Nowadays, most of the organizations using this technology to maintain their data. This paper focuses on the Bank sector in Risk management specifically, detecting Bank loan defaulters through the data mining application to examine the patterns of different attribute which would contribute for detecting and predicting defaulters thus preventing wrong loans. This process can be done without change the current systems and the data. Then it helps to distinguish borrowers who repay loans promptly from those who don’t and avoid wrong loan allotment. In order to show the results of the study Classification model is implemented in order to find interesting patterns among attributes of customer. A total of 20461 sample data were taken by data base admin randomly from 3 consecutive years from the Bank database to build and test the model. In this research we used Classification model of decision tree and Naïve Baye...
Accurately identifying credit risk is essential for the successful operation and growth of any business. By accurately predicting an applicant's loan status, businesses can better understand the drivers of credit risk and develop informed market strategies to promote business expansion. The goal of this research is to develop a machine learning model that can reliably predict the loan status (approved or rejected) of applicants to the Commercial Bank of Ethiopia (CBE). The study utilized a dataset of 32,285 applicants with 10 attributes. To evaluate the performance of different classifiers, the overall accuracy was used as the primary metric. Supervised machine learning techniques including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Network (DNN) were applied to the applicant loan status prediction task, based on their widespread use in prior literature. Feature importance and correlation matrix analysis were used for feature selection. Additionally, the SMOTE technique was employed to balance the dataset. The results show that the RF classifier achieved the best overall performance, with an accuracy of 93.62%, a recall of 94.72%,
Review-Calculation of client credit risk prediction in banking sector using data mining
International Journal of Advance Research, Ideas and Innovations in Technology, 2019
Data Mining is a competent section of data exploration which seeks to eliminate realistic data from the implausible extent of comprehensive data. The massive measurement of these data grounds formulates it impractical for a human predictor to come up with stirring in turn that will help out in the judgment conception process. A numeral of commercial endeavour has been hasty to be proverbial with the attraction of this deliberation. The explanation of this dissertation is to manner a relation erudition on the precision of categorization models and their cost can be smoothly comprehended and they can be realistic on both specific and ceaseless data. Many data mining techniques is intended to bulge admire attaining plight that everybody has some significance and limitations another way. The aim of this interpretation is affordable that an entire evaluation associated with sensible data mining process in credit scoring condition. Such direction can support the superintendent to be cognizant of most usual practice in recognition scoring measurement, determine their boundaries, get superior then and recommend a new system with the enhanced facility.
EKSAKTA: Journal of Sciences and Data Analysis
The bank conducts an analysis or survey in the credit system to determine whether the customer is eligible to receive credit. With a case study of Bank BJB debtor data in December 2021, credit classification analysis was carried out by forming a model using the Naïve Bayes Classifier and Decision Tree J48. Thus it is expected to minimize the occurrence of bad loans. The data are divided into several categories: debtors with good, substandard, doubtful, and bad credit. The analysis was carried out using a 10-fold cross-validation model, where the results obtained from both tests, the highest accuracy value was the Decision Tree J48 of 78.26%. While the Naïve Bayes Classifier has a lower level of accuracy, the prediction results tend to be better than the Decision Tree J48. The prediction results with the Naïve Bayes Classifier can predict all classes and the most influential variable in classifying credit is the loan term.
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International Journal of Economics and Management Engineering, 2016
In this study, the clients who applied to a bank branch for loan were analyzed through data mining. The study was composed of the information such as amounts of loans received by personal and SME clients working with the bank branch, installment numbers, number of delays in loan installments, payments available in other banks and number of banks to which they are in debt between the years of 2010 and 2013. The client risk profile was examined through Classification and Regression Tree (CART) analysis, one of the decision tree classification methods. At the end of the study, the clients’ risk categories were determined and according to these risk categories, it was determined in the event that these clients request another loan that which requests will be responded positively or negatively. Furthermore, it was determined that the loan requests of which clients are responded positively in the event which conditions are provided.
IOP Conference Series: Materials Science and Engineering, 2018
The purpose of this study is to create an application which functions automatically with high accuracy when analyzing bank customer data. This needed due to non-performing loans occurring frequently caused by the inaccuracy of credit analysts in the assessment of creditworthiness. This can be seen in the incident occurred in a public bank located in Bandung. This bank does not have the database that serves to accommodate data history and the method used in assessing creditworthiness is merely based on the simple statistical analysis. This leads to reduced accuracy and speed in the decision-making process. This research applies Naïve Bayes Classifier (NBC) method, a Data Mining technique. This helps credit analysts to select customers who are truly eligible to be given credit so that nonperforming loan can be avoided. NBC calculates the probability of one class from each group of attributes and determines which class is most optimal. The accuracy of the NBC sampling test from 501 data is 94% compared to the decision made by a credit analyst. It can be concluded that this application is very helpful for credit analysts in recommending customers who are eligible for a loan to the bank's decision maker.
Comparison of Data Mining Classification Algorithms Determining the Default Risk
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Big data and its analysis have become a widespread practice in recent times, applicable to multiple industries. Data mining is a technique that is based on statistical applications. This method extracts previously undetermined data items from large quantities of data. The banking and insurance industries use data mining analysis to detect fraud, offer the appropriate credit or insurance solutions to customers, and better understand customer demands. This study aims to identify data mining classification algorithms and use them to predict default risks, avoid possible payment difficulties, and reduce potential problems in extending credit. The data for this study, which contains demographic and socioeconomic characteristics of individuals, were obtained from the Turkish Statistical Institute 2015 survey. Six classification algorithms—Naive Bayes, Bayesian networks, J48, random forest, multilayer perceptron, and logistic regression—were applied to the dataset using WEKA 3.9 data minin...
Intelligent System for Credit Risk Management in Financial Institutions
International Journal of Artificial Intelligence and Machine Learning, 2019
Credit crunch is an alarming challenge facing financial institutions in Ghana due to their inability to manage credit risk. Failure to manage credit risk may lead to customers defaulting and institutions becoming bankrupt, making it a major concern for financial institutions and the government. The assessment and evaluation of loan applications based on a loan officer's subjective assessment and human judgment is inefficient, inconsistent, non-uniform, and time consuming. Therefore, a knowledge discovery tool is required to help in decision making regarding the approval of loan application. The aim of this project is to develop an intelligent system based on a decision tree model to manage credit risk. Data was obtained from the bank loan histories. The data is comprised of four hundred observations with seven variables: client age, amount requested, dependents, collateral value, employment sector, employment type, and results. The results of study suggest that the proposed syst...
Artificial Intelligence-Enhanced Credit Risk Assessment on Commercial Bank of Ethiopia
Thesis, 2024
Accurately identifying credit risk is essential for the successful operation and growth of any business. By accurately predicting an applicant's loan status, businesses can better understand the drivers of credit risk and develop informed market strategies to promote business expansion. The goal of this research is to develop a machine learning model that can reliably predict the loan status (approved or rejected) of applicants to the Commercial Bank of Ethiopia (CBE). The study utilized a dataset of 32,285 applicants with 10 attributes. To evaluate the performance of different classifiers, the overall accuracy was used as the primary metric. Supervised machine learning techniques including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Network (DNN) were applied to the applicant loan status prediction task, based on their widespread use in prior literature. Feature importance and correlation matrix analysis were used for feature selection. Additionally, the SMOTE technique was employed to balance the dataset. The results show that the RF classifier achieved the best overall performance, with an accuracy of 93.62%, a recall of 94.72%,