Research on the Impact of Fintech on Customer Satisfaction in Gurgaon’s Banking Sector
Bibliographic record
Abstract
The global economy has evolved into a dynamic, digital landscape, characterized by globalization, digitalization, government reforms, and heightened competition. In this context, businesses are compelled to reassess their strategies, policies, products, and services. Technology has played a pivotal role in revolutionizing economies worldwide, bridging existing gaps among developed, developing, and under-developed nations. Among sectors, banking stands out as a key provider of financial assistance and services to various industries. Given the centrality of customer satisfaction in market dynamics, banks must continually innovate their business models to enhance and sustain customer satisfaction levels. FinTech emerges as a crucial enabler in this pursuit. This study aims to analyze the role of FinTech and its impact on customer satisfaction within the banking sector of Pune city. Through a structured questionnaire administered to 100 respondents in Pune, employing non-probabilistic convenient sampling, this research explores the socio-demographic profile of participants and assesses their satisfaction levels with banking services. Utilizing frequency distribution analysis, the study sheds light on the significance of FinTech in fostering sustainable business growth within the Indian banking landscape.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".