Machine Learning Based Model for Prediction of Loan Approval

Lohani, Bhanu Prakash; Trivedi, Mayank; Singh, Ridhik Jeet; Bibhu, Vimal; Ranjan, Shiv; Kushwaha, Pradeep Kumar

doi:10.1109/iciem54221.2022.9853160

Cited by 22 publications

(4 citation statements)

References 9 publications

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“…Sameerunnisa.SK et al [6] further contribute to the field of financial technology by proposing a machine learning-based loan prediction system employing a Gradient Boosting Classifier. Their work highlights the importance of fairness and efficiency in loan approval processes.…”

Section: Literature Surveymentioning

confidence: 99%

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: 99%

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

2024

IJSREM

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“…These days, emerging technologies like machine learning and natural language processing greatly reduce the amount of work needed to do such tasks [9][10][11]. Machine learning tasks involving classification are typically found in several fields, such as engineering [12,13], medicine [14], finance and economics [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…Through (10), assign the MAE that was calculated between Ĵva and J va to E. 9: end procedure Ensure: E, the error.…”

mentioning

confidence: 99%

Credit and Loan Approval Classification Using a Bio-Inspired Neural Network

Mourtas,

Katsikis,

Stanimirović

et al. 2024

Biomimetics

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Numerous people are applying for bank loans as a result of the banking industry’s expansion, but because banks only have a certain amount of assets to lend to, they can only do so to a certain number of applicants. Therefore, the banking industry is very interested in finding ways to reduce the risk factor involved in choosing the safe applicant in order to save lots of bank resources. These days, machine learning greatly reduces the amount of work needed to choose the safe applicant. Taking this into account, a novel weights and structure determination (WASD) neural network has been built to meet the aforementioned two challenges of credit approval and loan approval, as well as to handle the unique characteristics of each. Motivated by the observation that WASD neural networks outperform conventional back-propagation neural networks in terms of sluggish training speed and being stuck in local minima, we created a bio-inspired WASD algorithm for binary classification problems (BWASD) for best adapting to the credit or loan approval model by utilizing the metaheuristic beetle antennae search (BAS) algorithm to improve the learning procedure of the WASD algorithm. Theoretical and experimental study demonstrate superior performance and problem adaptability. Furthermore, we provide a complete MATLAB package to support our experiments together with full implementation and extensive installation instructions.

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“…These days, emerging technologies like machine learning and natural language processing greatly reduce the amount of work needed to do such tasks [1]. Primarily utilized https://doi.org/10.1051/itmconf/20245901004 HMMOCS-II 2023 for classification and regression issues, neural networks (NNs) have been successfully applied in a number of disciplines, including medicine, engineering, economics, social science research and finance.…”

Section: Introductionmentioning

confidence: 99%

Customer churn classification through a weights and structure determination neural network

Mourtas

2024

ITM Web Conf.

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In today's corporate world, acquiring and keeping clients are the most important priorities. Every business’s market is expanding quickly, which is increasing the number of subscribers. Because neglect could result in a drop in profitability from a major standpoint, it has become imperative for service providers to limit churn rates. These days, identifying which customers are most likely to leave a business requires a lot less work thanks to machine learning. Taking this into account, a novel weights and structure determination (WASD) neural network has been built to meet the aforementioned challenge of customer churn classification, as well as to handle its unique characteristics. Motivated by the observation that WASD neural networks outperform conventional back-propagation neural networks in terms of slow training speed and trapping in a local minimum, we enhance the WASD algorithm's learning process with a new activation function for best adapting to the customer churn model. Superior performance and flexibility to problems are demonstrated in an experimental investigation using a dataset from a telecommunications provider.

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Machine Learning Based Model for Prediction of Loan Approval

Cited by 22 publications

References 9 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

Credit and Loan Approval Classification Using a Bio-Inspired Neural Network

Customer churn classification through a weights and structure determination neural network

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