Modern Approach for Loan Sanctioning in Banks Using Machine Learning

Rath, Golak Bihari; Das, Debasish; Acharya, Biswaranjan

doi:10.1007/978-981-15-5243-4_15

Cited by 10 publications

(3 citation statements)

References 15 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.

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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

View full text Add to dashboard Cite

show abstract

“…Authors of [21] used the Logistic regression model to estimate various performance metrics providing a wide range of outcomes disregarding two important variables, such as gender and marital status. A technique was utilized in [22] for developing a model using the information and outcomes of loan applicants who had already submitted applications which discovered that the logistic regression model performs better than other models. Under the assumption that loan quality has a direct impact on a bank's profitability, in [23], a combined logistic regression method and artificial neural network (ANN) was utilized to improve the predictive performance based on real data from a rural commercial bank.…”

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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