Customer Loan Eligibility Prediction using Machine Learning Algorithms in Banking Sector

Kumar, Ch. Naveen; Keerthana, D.; Kavitha, M.; Kalyani, M

doi:10.1109/icces54183.2022.9835725

Cited by 17 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data characteristics of this study are different from those of other similar studies. Although similar to the results obtained by other scholars, the algorithm accuracy of this study is higher [4,5,11,12]. It can be seen that the data characteristics in this research are beneficial to prediction.…”

Section: Discusionsupporting

confidence: 87%

“…Many scholars use machine learning to predict loan defaults. Kumar et al examined the precision of customers' loan eligibility with decision tree, Random Forest, support vector machine, k-nearest neighbour, and decision tree using adaboost technology [4]. On the basis of the data set, Singh et al presented three machine learning (ML) algorithms: XGBoost, Random Forest, and Decision Tree.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on loan default prediction based on logistic regression, randomforest, xgboost and adaboost

Lin

2024

SHS Web Conf.

View full text Add to dashboard Cite

Lenders often experience loan defaults, resulting in huge losses to lenders. Lenders are required to conduct a credit assessment of borrowers before making loans. Machine learning plays an essential role in loan credit analysis. This study analyzes the application of machine learning in loan credit analysis through a dataset of borrowers from Kaggle and looks for an excellent algorithm.This study use Logistic Regression, randomforest, XGBoost and AdaBoost to fit the dateset and compare their accuracy in prediction.In terms of results, XGBoost performed well while logistic regression performed poorly. For banks or lending institutions, using Gradient Boosting Decision Tree like XGBoost to predict loan default can increase profit.

show abstract

Section: Discusionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Research on loan default prediction based on logistic regression, randomforest, xgboost and adaboost

Lin

2024

SHS Web Conf.

View full text Add to dashboard Cite

show abstract

“…Sandeep Kumar Hegde and Rajalaxmi Hegde [10] explore loan prediction within the banking sector using ML algorithms. Their research focuses on predicting applicant risk and streamlining the loan approval process.…”

Section: Literature Surveymentioning

confidence: 99%

Enhancing Loan Prediction Accuracy: A Comparative Analysis of Machine Learning Algorithms with XAI Integration

2024

IJSREM

View full text Add to dashboard Cite

The contemporary financial landscape necessitates loan recommendation systems that offer both accuracy and transparency. Conventional assessment methodologies often suffer from limitations in efficiency and transparency, leading to potential risks for both lenders and borrowers. This research proposes the development of a novel loan recommendation system that leverages the power of machine learning (ML) and Explainable Artificial Intelligence (XAI). The paper delves into the processes of data collection, preprocessing, model training, evaluation, and subsequent integration into a web application using the Flask framework. The employed datasets encompass a variety of loan types, with the study aiming to identify the most effective ML algorithms from a selection that includes XGBoost, CatBoost, Random Forest, Gradient Boosting, and Logistic Regression. To enhance the system's transparency, Explainable AI methods, such as LIME, are incorporated. The culmination of this research is a web application that facilitates personalized predictions regarding loan eligibility, accompanied by clear explanations. Index terms - Loan Recommendation System, Machine Learning, Explainable AI, XGBoost, CatBoost, Random Forest, Gradient Boost, Logistic Regression,LIME.

show abstract

“…An understandable artificial intelligence (AI) decision-support system was researched to automate the loan underwriting process with a belief-rule-base (BRB) and was capable of learning from and incorporating human knowledge through supervised learning, and historical data [18]. In recent times, authors of [19] made a comparative study in predicting eligible customer loan receivers using five ML algorithms recommending a Decision tree with AdaBoost ML to have the highest accuracy rate where the data cleansing mechanism played an important role. In [20], a logistic regression model was utilized for predicting the problem of forecasting loan defaulters fetching the Kaggle dataset, depending on sensitivity and specificity as the two parameters to compare the performance of the ML model.…”

Section: A Ml-based Loan Predictionmentioning

confidence: 99%

Eligible Personal Loan Applicant Selection using Federated Machine Learning Algorithm

Anannya,

Khatun,

Hosen

et al. 2023

IJACSA

View full text Add to dashboard Cite

Loan sanctioning develops a paramount financial dependency amongst banks and customers. Banks assess bundles of documents from individuals or business entities seeking loans depending on different loan types since only reliable candidates are chosen for the loan. This reliability materializes after assessing the previous transaction history, financial stability, and other diverse kinds of criteria to justify the reliance of the bank on an applicant. To reduce the workload of this laborious assessment, in this research, a machine learning (ML) based web application has been initiated to predict eligible candidates considering multiple criteria that banks generally use in their calculation, in short which can be briefed as loan eligibility prediction. Data from prior customers, who are authorized for loans based on a set of criteria, are used in this research. As ML techniques, Random Forest, K-Nearest Neighbour, Adaboost, Extreme Gradient Boost Classifier, and Artificial Neural Network algorithms are utilized for training and testing the dataset. A federated learning approach is employed to ensure the privacy of loan applicants. Performance analysis reveals that Random Forest classifier has provided the best output with an accuracy of 91%. Based on the mentioned prediction, the web application can decide whether the customers' requested loan should be accepted or rejected. The application was developed using NodeJs, ReactJS, Rest API, HTML, and CSS. Furthermore, parameter tuning can improve the performance of the web application in the future along with a usable user interface ensuring global accessibility for various types of users.

show abstract

Customer Loan Eligibility Prediction using Machine Learning Algorithms in Banking Sector

Cited by 17 publications

References 12 publications

Research on loan default prediction based on logistic regression, randomforest, xgboost and adaboost

Research on loan default prediction based on logistic regression, randomforest, xgboost and adaboost

Enhancing Loan Prediction Accuracy: A Comparative Analysis of Machine Learning Algorithms with XAI Integration

Eligible Personal Loan Applicant Selection using Federated Machine Learning Algorithm

Contact Info

Product

Resources

About