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<p>Accurate prediction of loan approval and risk evaluation are critical for financial stability and growth. This study proposes a Deep Learning (DL) framework utilizing CatNet, a model optimized for categorical data, to predict loan approval outcomes and assess financial risk. The research employs the publicly available Financial Risk for Loan Approval dataset from Kaggle, containing demographic and financial attributes such as age, income, credit score, and loan amount. The dataset is preprocessed by encoding categorical variables, handling outliers, and scaling numerical features. These processed data are securely stored and computed on a cloud infrastructure, ensuring privacy and efficient processing. Factor analysis is applied to identify key financial attributes, improving prediction accuracy. Quantum Key Distribution (QKD) is integrated to safeguard sensitive data during the prediction process, addressing data privacy concerns. The CatNet model achieves high performance with a 98.45% accuracy, 98.33% recall, 95.69% precision, and 96.99&#xa0;F1-score, demonstrating its effectiveness in making secure, real-time loan approval predictions while addressing challenges related to model overfitting and financial risk assessment. This work provides a scalable, secure solution for digital loan risk assessment, with potential real-world impact in enhancing financial decision-making processes.</p>

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Leveraging Catnet for Loan Approval Prediction and Financial Risk Assessment on Cloud Infrastructure

  • 期刊论文

  • 发表日期：

    2026年1月27日

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  • Yashwant Kumar Kolli,
  • Priyadarshini Radhakrishnan,
  • Vijai Anand Ramar,
  • Karthik Kushala,
  • Venkataramesh Induru,
  • S. K. Pravin Kumar

摘要

Accurate prediction of loan approval and risk evaluation are critical for financial stability and growth. This study proposes a Deep Learning (DL) framework utilizing CatNet, a model optimized for categorical data, to predict loan approval outcomes and assess financial risk. The research employs the publicly available Financial Risk for Loan Approval dataset from Kaggle, containing demographic and financial attributes such as age, income, credit score, and loan amount. The dataset is preprocessed by encoding categorical variables, handling outliers, and scaling numerical features. These processed data are securely stored and computed on a cloud infrastructure, ensuring privacy and efficient processing. Factor analysis is applied to identify key financial attributes, improving prediction accuracy. Quantum Key Distribution (QKD) is integrated to safeguard sensitive data during the prediction process, addressing data privacy concerns. The CatNet model achieves high performance with a 98.45% accuracy, 98.33% recall, 95.69% precision, and 96.99 F1-score, demonstrating its effectiveness in making secure, real-time loan approval predictions while addressing challenges related to model overfitting and financial risk assessment. This work provides a scalable, secure solution for digital loan risk assessment, with potential real-world impact in enhancing financial decision-making processes.