Bank Loan Prediction System using Machine Learning

Gupta, Abhijit; Pant, Vinay Kumar; Kumar, Sudhanshu; Bansal, Pravesh Kumar

doi:10.1109/smart50582.2020.9336801

Cited by 32 publications

(7 citation statements)

References 3 publications

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“…The predictive model is beneficial in terms of decreasing the time and efforts necessary to approve loans as well as filtering out the best applicants for granting loans. Further study can be found in the following works 2,14‐22 …”

Section: Literature Surveymentioning

confidence: 99%

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Analyzing the impact of loan features on bank loan prediction using Random Forest algorithm

Dansana

Patro

Mishra

et al. 2023

Engineering Reports

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Loans are a crucial source of income for the financial sector, but they also come with significant financial risks. The interest on loans constitutes a significant portion of a bank's assets. The demand for loans is growing worldwide, and organizations are devising efficient business strategies to attract more clients. Every day, a large number of people apply for loans for various reasons, but not all of them can be approved due to the risk of loan default. It is not uncommon for people to default on their loans, causing significant losses to banks. The purpose of this article is to determine whether to grant loans to specific individuals or organizations. The Random Forest Regressor model has been utilized to measure performance and identify suitable customers for loan approval. The model suggests that banks should not only target affluent clients but also consider other customer characteristics that are critical in credit granting and predicting loan default. The research examines various loan approval parameters such as gender, educational qualification, employment type, business type, loan term, and marital status. Additionally, the study analyzes the number of approved, drawn, and rejected loans, which provides valuable insights into loan approval and prediction.

show abstract

Section: Literature Surveymentioning

confidence: 99%

“…Further study can be found in the following works. 2,[14][15][16][17][18][19][20][21][22] This research article is organized as follows: literature survey is mentioned in Section 2. Materials and methods are presented in Section 3.…”

Section: Literature Surveymentioning

confidence: 99%

Analyzing the impact of loan features on bank loan prediction using Random Forest algorithm

Dansana

Patro

Mishra

et al. 2023

Engineering Reports

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show abstract

“…Authors in [9] used the ML approach to predict eligible candidates to receive loan amounts by collecting previous banks' data who are accredited before. For predicting loans, a simple comparative study was made in [10] based on six machine-learning classification models in R to find out whether allocating a loan to a certain person is risky or not without recommending any specific algorithm.…”

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

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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.

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“…Major banks have huge digital data warehouses within their computational storage systems. The quantity and quality of data have been developed [3].…”

Section: Introductionmentioning

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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Bank Loan Prediction System using Machine Learning

Cited by 32 publications

References 3 publications

Analyzing the impact of loan features on bank loan prediction using Random Forest algorithm

Analyzing the impact of loan features on bank loan prediction using Random Forest algorithm

Eligible Personal Loan Applicant Selection using Federated Machine Learning Algorithm

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

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