HOW DOES THE FINTECH INNOVATION WAVE AFFECT FINANCIAL MARKETS, THE BANKING INDUSTRY, AND CUSTOMER BEHAVIOR?
Bibliographic record
Abstract
This study investigate FinTech’s on financial markets, the banking sector, and consumers. It aims to examine the role and effects FinTech in the presentation and use of financial services. The study focuses on FinTech investment areas in countries such as America, Canada, Brazil, Germany, France, Israel, China, and India, which are prominent in FinTech investments, between 2012 and 2020, using the panel data method and fixed effects model. FinTech investments are grouped according to payment management, insurance, information technologies, software, financial services, and other categories, and the relationships between them have been empirically tested. The FinTech investment amount was used as the dependent variable Inflation, number of branches, unemployment, and GDP were considered as independent variables. The results show that the increase in FinTech investment is affected by inflation and the number of branches in a negative and statistically significant manner. However, the results concluded that the individual Internet usage variable positively affected the FinTech investment amount. These findings provide strong empirical evidence that FinTech investments can increase profitability levels in the finance and banking sectors. This study highlights the impact of FinTech on the transformation process in the financial sector, and it can offer valuable insights for financial service providers and policymakers. It may also be essential for understanding consumers' demands and expectations for financial technologies. Such studies can offer a valuable roadmap for understanding
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".