Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/27878
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dc.contributor.authorDeepika, Azmeera-
dc.date.accessioned2025-06-04T04:28:35Z-
dc.date.available2025-06-04T04:28:35Z-
dc.date.issued2023-01-01-
dc.identifier.otherSP003638-
dc.identifier.urihttp://hdl.handle.net/11718/27878-
dc.descriptionIn an increasingly interconnected and technology-driven world, digitalization and financial technology (fintech) have significantly shaped economies. This project aimed to examine the impact of digitalization and fintech on the credit-to-GDP ratio of the country, a crucial indicator of financial stability and economic progress. The project employed regression analysis to link digitalization, fintech indicators, and credit-to-GDP ratios in a sample of economies. The results revealed essential insights illuminating the intricate interplay between technology and the credit-to-GDP ratio. The model fit measures provided valuable information about the overall effectiveness of the regression model. The coefficient of determination (R-squared) indicated that approximately 36.9% of the variability in the credit ratio could be explained through the predictor variables, namely: Individuals using the internet (% of population) as a proxy for technology adoption, Digital transaction value (in billion USD) as a proxy for fintech adoption by the country, and ATM availability (per 100,000 adults) as a proxy for the digitalization of the banking system. The adjusted R-squared of 0.352 considered the trade-off between model complexity and explanatory power, suggesting that about 35.2% of the variability could be explained while accounting for the number of predictors.en_US
dc.description.abstractIn an increasingly interconnected and technology-driven world, digitalization and financial technology (fintech) have significantly shaped economies. This project aimed to examine the impact of digitalization and fintech on the credit-to-GDP ratio of the country, a crucial indicator of financial stability and economic progress. The project employed regression analysis to link digitalization, fintech indicators, and credit-to-GDP ratios in a sample of economies. The results revealed essential insights illuminating the intricate interplay between technology and the credit-to-GDP ratio. The model fit measures provided valuable information about the overall effectiveness of the regression model. The coefficient of determination (R-squared) indicated that approximately 36.9% of the variability in the credit ratio could be explained through the predictor variables, namely: Individuals using the internet (% of population) as a proxy for technology adoption, Digital transaction value (in billion USD) as a proxy for fintech adoption by the country, and ATM availability (per 100,000 adults) as a proxy for the digitalization of the banking system. The adjusted R-squared of 0.352 considered the trade-off between model complexity and explanatory power, suggesting that about 35.2% of the variability could be explained while accounting for the number of predictors.en_US
dc.language.isoenen_US
dc.publisherIndian Institute of Management Ahmedabaden_US
dc.subjectFinancial technology - Economic aspecten_US
dc.subjectDigital finance - Statistical methodsen_US
dc.subjectCredit - Gross domestic product - Econometric modelsen_US
dc.titleCredit to GDP ratio: to examine whether technological innovation had impact on credit-to-GDP ratio through cross-country evidenceen_US
dc.typeStudent Projecten_US
Appears in Collections:Student Projects

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