Automated Loan Lending System with Artificial Neural Networks

The proposed project involves designing an Artificial Neural Network (ANN) model to predict loan repayment behavior. Using data sourced from platforms such as the 'LendingClub' dataset available on Kaggle, the system aims to classify potential borrowers as likely to repay or default on loans. The ANN model is expected to enhance decision-making in financial institutions by providing a robust, data-driven mechanism for risk assessment. This paper outlines the project's objectives, background, methodology, and conclusions, demonstrating the potential of machine learning in the domain of financial risk analysis.

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Work Title Automated Loan Lending System with Artificial Neural Networks
Access
Open Access
Creators
  1. Rohit Chorghe
Keyword
  1. Neural Networks
  2. Loan Lending System
  3. Exploratory Data Analysis
  4. Logistic Regression
  5. Financial Dataset
License CC BY 4.0 (Attribution)
Work Type Masters Culminating Experience
Sub Work Type Scholarly Paper/Essay (MA/MS)
Program Information Systems
Degree Master of Science
Acknowledgments
  1. Girish Subramanian
Publisher
  1. ScholarSphere
Publication Date April 2025
DOI doi:10.26207/nsfg-2r56
Deposited April 23, 2025

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Work History

Version 1
published

  • Created

    April 23, 2025 16:23 by elm43

  • Updated

    April 23, 2025 16:23 by [unknown user]

  • Updated Keyword, Degree, Program, and 3 more Show Changes

    April 23, 2025 16:24 by elm43

    Keyword
    • Neural Networks, Loan Lending System, Exploratory Data Analysis, Logistic Regression, Financial Dataset
    Degree
    • Master of Science
    Program
    • Information Systems
    Description
    • The proposed project involves designing an Artificial Neural Network (ANN) model to predict loan repayment behavior. Using data sourced from platforms such as the 'LendingClub' dataset available on Kaggle, the system aims to classify potential borrowers as likely to repay or default on loans. The ANN model is expected to enhance decision-making in financial institutions by providing a robust, data-driven mechanism for risk assessment. This paper outlines the project's objectives, background, methodology, and conclusions, demonstrating the potential of machine learning in the domain of financial risk analysis.
    Sub Work Type
    • Scholarly Paper/Essay (MA/MS)
    Publication Date
    • 2025-04
  • Updated Acknowledgments Show Changes

    April 23, 2025 16:24 by elm43

    Acknowledgments
    • Girish Subramanian
  • Added Creator Rohit Chorghe

    April 23, 2025 16:24 by elm43

  • Added Creator Emily Mross

    April 23, 2025 16:24 by elm43

  • Added CHORGHE_ROHIT.pdf

    April 23, 2025 16:24 by elm43

  • Updated License Show Changes

    April 23, 2025 16:25 by elm43

    License
    • https://creativecommons.org/licenses/by/4.0/
  • Published Publisher Show Changes

    April 23, 2025 16:25 by elm43

    Publisher
    • ScholarSphere
  • Updated

    April 23, 2025 22:05 by [unknown user]

  • Updated

    May 01, 2025 20:31 by avs5190

  • Deleted Creator Emily Mross

    May 01, 2025 20:31 by avs5190