empirical study on evaluation of fintech industry in Bengaluru
Bibliographic record
Abstract
Digitalization has brought challenges in all the industries and business sectors (Ryan Randy Suryono et al. 2020). The research in this area reveals clearly that the development of digital transformation has given the way for emergence of fintech initiatives. India is one of the fastest growing fintech markets in the world. India has Asia’s highest fintech investment institutions with a deal value around $647.50 million across 33 deals as compared to China's $284.9 million during the quarter end of June 30, 2020 (RSBA Advisors 2021). As per the MEDICI India FinTech Report 2020 India has witnessed exclusive growth in the number of new ventures launched in the FinTech space. Cumulative investments in India crossed $10 billion by the end of June 2020. The number of fintech startups in India is now almost 2280 with Bengaluru and Mumbai contributing to 42% of the companies (India Ego Medic.com). The first wave of disruptions in Financial services was led by digital payments followed by digital lending, wealth management and insurrect startups. This trend led to the emergence of fintech 2.0 where there are new use cases emerging with completely new models.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".