Comparative Analysis of Machine Learning Models on Loan Risk Analysis

Rao, M. Srinivasa; Sekhar, Ch.; Bhattacharyya, Debnath

doi:10.1007/978-981-15-9516-5_7

Cited by 3 publications

(3 citation statements)

References 8 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

“…For a long time, the banks have carried the country's economy by providing investments and lending for citizens. Although lending brings revenues to the banks, the banks suffer from repaying from customers or companies [1]. Therefore, the banks utilize the new advances in technologies (particularly Artificial Intelligence (AI) to accept or reject lending (also called loan approval).…”

Section: Introductionmentioning

confidence: 99%