Design and Simulation of Loan Approval Prediction Model using AWS Platform

Ramachandra, H. V.; Balaraju, G.; Divyashree, R; Patil, Harish

doi:10.1109/esci50559.2021.9397049

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…Similarly, Ramachandra H. [2] demonstrates the efficacy of ML in loan prediction. The study leverages Demographic information and algorithms like Decision Tree, Logistic Regression, and Random Forest to develop a cloud-based ML model for prediction of the loan repayment outcomes with 86% accuracy.…”

Section: Literature Surveymentioning

confidence: 84%

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

2024

IJSREM

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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.

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Section: Literature Surveymentioning

confidence: 84%

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

2024

IJSREM

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“…The study by Ramachandra et al [33] aims to deploy the model on cloud-based platforms using machine learning algorithms and concepts to identify and understand the working method of loan systems for loan prediction. The main objective of the project is to predict which of the customers will or will not pay their loans, using leading algorithms such as DT, LR and RF.…”

Section: Related Research (İlgi̇li̇ Araştirmalar)mentioning

confidence: 99%

A Comparative Study of Loan Approval Prediction Using Machine Learning Methods

Sinap

2024

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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Loan prediction plays an important role in the process of evaluating loan applications by financial institutions. Machine learning models can automate this process and make the lending process faster and more efficient. In this context, the main objective of this research is to develop models for loan approval prediction using machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest and to compare their performances. In addition, determining the effect of K-Best and Recursive Feature Elimination feature selection methods on model performances is another important objective of the research. Furthermore, the evaluation of the effectiveness of techniques such as cross-validation (K-Fold) and Train, Test and Validation in measuring the performance of models is also among the objectives of the research. The findings revealed that married individuals are more likely to be approved for loans than single individuals, high income individuals more likely than low-income individuals, males more likely than females, and university graduates more likely than non-university graduates. According to the performance measures, Random Forest was the most successful algorithm with an accuracy rate of 97.71% in loan approval prediction. To achieve this accuracy rate, feature selection was performed with the Recursive Feature Elimination method and the measurement was made with the cross-validation method. It was found that the feature selection methods have a significant impact on the model performances and the Recursive Feature Elimination method was the most successful method. Moreover, the highest accuracy rate achieved by the Random Forest algorithm, which showed the highest performance in all cases, was measured by cross-validation.

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“…Credit Card Fraud is referred as unauthorized use of a credit card account. A methodology with a malfeasance property and a clustering time regular by a classifier without an embezzlement attribute were both recommended by the credit card theft model [25][26][27]. Automobile injury claims were divided into different categories based on the degree of deception suspicion using a soul feature map [28][29][30][31].…”

Section: Credit Card Fraudmentioning

confidence: 99%

“…Predicting bank collapse [15][16], identification of potential bank customer churns [17], fraudulent transaction detection [18], customer segmentation [19][20], bank telemarketing predictions [21][22][23][24], sentiment analysis for bank customers [25], and bank loan prediction [26][27][28]. Some categorization studies in the banking sector are compared in Table 2.…”

Section: Data Mining Techniques Have Been Applied In the Banking Indu...mentioning

confidence: 99%

Review of Loan Fraud Detection Process in the Banking Sector Using Data Mining Techniques

Esmail

Alsheref

Aboutabl

2023

Int. j. electr. comput. eng. syst. (Online)

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At the era of digital transformation, fraud has dramatically increased, notably in the banking industry. Annually, it now costs the world's economies billions of dollars. Daily, news of financial fraud has a negative influence on the world economy. According to the harsh loss caused by fraud, effective strategies and methods for avoiding income statement fraud have to be implemented. Also, the procedure of identification should be applied. This is regarded as a result of the development of modern technology, modern invention, and the rapidity of global communications. Actually, deterrent technologies are most effective to reduce fraud and overcome cons. So, it is necessary to find ways to overcome such deterrence by depending on developed methods to identify fraud. Data mining techniques are currently the most widely used methods for the prevention and detection of financial fraud. The use of datasets for fraud detection complies with the norms of data mining, which include feature selection, representation, data gathering and management, pre-processing, comment, and summative evaluation. Methodologies for identifying fraud are essential if we want to catch criminals after fraud prevention has failed. The greatest fraud detection strategies for locating loan banking and financial fraud are compared in this article.

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Design and Simulation of Loan Approval Prediction Model using AWS Platform

Cited by 9 publications

References 11 publications

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

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

A Comparative Study of Loan Approval Prediction Using Machine Learning Methods

Review of Loan Fraud Detection Process in the Banking Sector Using Data Mining Techniques

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