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DC Field | Value | Language |
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dc.contributor.author | Deepika, Azmeera | - |
dc.date.accessioned | 2025-06-04T04:28:35Z | - |
dc.date.available | 2025-06-04T04:28:35Z | - |
dc.date.issued | 2023-01-01 | - |
dc.identifier.other | SP003638 | - |
dc.identifier.uri | http://hdl.handle.net/11718/27878 | - |
dc.description | In 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.abstract | In 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.iso | en | en_US |
dc.publisher | Indian Institute of Management Ahmedabad | en_US |
dc.subject | Financial technology - Economic aspect | en_US |
dc.subject | Digital finance - Statistical methods | en_US |
dc.subject | Credit - Gross domestic product - Econometric models | en_US |
dc.title | Credit to GDP ratio: to examine whether technological innovation had impact on credit-to-GDP ratio through cross-country evidence | en_US |
dc.type | Student Project | en_US |
Appears in Collections: | Student Projects |
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SP003638.pdf Restricted Access | 890.64 kB | Adobe PDF | View/Open Request a copy |
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