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This research analyzes the effect of the Marginal Cost of Funds-based Lending Rate (MCLR) on the share price performance of prominent Indian banks—HDFC, ICICI, Axis Bank, and SBI—using sophisticated machine learning models with cybersecurity focus. In 2016, the Reserve Bank of India introduced MCLR as a dynamic benchmark for computing loan interest rates, which affect corporate borrowing as well as share price performance. With historical stock data and trends of MCLR, Linear Regression, Support Vector Machines (SVM), and Random Forest models were used to measure the connection between interest rate movement and stock returns. The most efficient model was Random Forest, which indicated a negative correlation between stock returns and MCLR, with SBI being most sensitive to movements. In the modern digital financial world, cybersecurity is essential because cyber-attacks can jeopardize data integrity and model results. This study emphasizes how predictive precision must be paired with sound cybersecurity practices to ensure investor confidence and financial stability, urging combined systems to preserve investor confidence and financial soundness. These implications are noteworthy for policymakers, investors, and financial analytics cybersecurity professionals.

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Impact of Marginal Cost Lending Rate on the Indian Banking Sector: An Analysis Using Machine Learning for Cybersecurity

  • 会议论文

  • 发表日期：

    2026年

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  • C. A. Sapna Jain,
  • Bhupendra Jain,
  • Sumit Bhardwaj,
  • Monika Aggarwal

摘要

This research analyzes the effect of the Marginal Cost of Funds-based Lending Rate (MCLR) on the share price performance of prominent Indian banks—HDFC, ICICI, Axis Bank, and SBI—using sophisticated machine learning models with cybersecurity focus. In 2016, the Reserve Bank of India introduced MCLR as a dynamic benchmark for computing loan interest rates, which affect corporate borrowing as well as share price performance. With historical stock data and trends of MCLR, Linear Regression, Support Vector Machines (SVM), and Random Forest models were used to measure the connection between interest rate movement and stock returns. The most efficient model was Random Forest, which indicated a negative correlation between stock returns and MCLR, with SBI being most sensitive to movements. In the modern digital financial world, cybersecurity is essential because cyber-attacks can jeopardize data integrity and model results. This study emphasizes how predictive precision must be paired with sound cybersecurity practices to ensure investor confidence and financial stability, urging combined systems to preserve investor confidence and financial soundness. These implications are noteworthy for policymakers, investors, and financial analytics cybersecurity professionals.