Bibliographic record
Abstract
The Indian economy has traditionally been dominated by cash. However, the increased adoption of smart phones together with a favorable regulatory environment are pushing the economy to a less cash-dependent state and promoting the usage of digital payments.Demonetization of Rs.500 and Rs.1000 currency notes, which accounted for over 80% of the bills in circulation, and the subsequent policy measures taken by the Government of India (GOI) and the Reserve Bank of India (RBI) have provided further impetus to digital payments. Some key actions including expansion of the digital payments infrastructure at merchant establishments, expansion into rural areas, relaxation in the Prepaid Payment instruments (PPIs) norms, incentivization of digital payments at fuel pumps, toll plazas, insurance portals etc.Further launch of Bharat QR codes, among others, have helped the adoption of the technology. According to the Reserve Bank of India's data; the digital payments in the market is dominated by card transactions (debit and credit) both in terms of value and volume and thus the number of debit cards in circulation increased from 533 million to 867 million in April 2017 and thenumber of credit cards also increased from 21 million to 31 million in that same time period. The debit card base as of January 2019 is about 930 million, which has grown from 845 million in January 2018 and 780 million in January 2017. Hence, the mobile wallet industry has been on a rapid growth as India moves to cash less economy state. The value and volume of mobile wallet transactions more than doubled last year alone and as such the industry is leading the charge to making India a cashless economy.Increased adoption of smartphones and mobile data packages has been one of the largest contributions to this growth as penetration of the technology increases and mobile data costscome down; the industry is primed for further growth. This research aims to understanding of the digital wallet world and its dynamics to highlight the competitive nature of the market and shed some light on its predicted future trajectory and the challenges that the industry must overcome in order to continue its growth momentum.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".