Dataset Analysis of Factors Affecting Lending Patterns in Scheduled Commercial Banks under the CGTSME Scheme: An Empirical Study Integrating the Theory of Planned Behaviour.

Published: 13 October 2023| Version 1 | DOI: 10.17632/chjzdvjg6s.1
Somya Trivedi, Amit Kumar Sinha, Rohit Kushwaha, Manish Mishra


The dataset under scrutiny pertains to an empirical study focused on understanding the factors influencing lending patterns within scheduled commercial banks participating in the Credit Guarantee Fund Trust for Small and Medium Enterprises (CGTSME) Scheme. This dataset comprises a comprehensive array of critical variables to shed light on the multifaceted dynamics at play in this lending environment. It encompasses borrower-specific information, including unique identifiers, demographic details such as age, gender, and location, and the nature of the borrower's business. Moreover, it delves into loan particulars, including the loan amount, term, interest rate, purpose, and crucially, the loan's approval status. The financial context is enriched with indicators like annual revenue, credit scores, profit margins, and debt-to-equity ratios. Behavioral data introduces elements such as loan history, credit behavior, and past defaults. In parallel, psychological factors are examined, including attitudes toward borrowing, subjective norms, and perceived behavioral control, all integral to the Theory of Planned Behaviour. Furthermore, the dataset encapsulates scheme-specific features such as CGTSME coverage and details of guarantors. Economic and market data present information on macroeconomic conditions and market competition, offering a holistic view of the external context. Time-series data, featuring loan disbursement and repayment dates, ensures a dynamic analysis. Ultimately, the dataset provides insight into outcome variables, specifically repayment performance and lending patterns, while also accounting for control variables that might influence lending decisions. The integration of the Theory of Planned Behaviour into the analysis promises a more nuanced understanding of the psychological drivers behind lending patterns within this financial landscape.



Banking, Behavioral Finance