FINANCIAL TECHNOLOGY AND LIQUIDITY IN THE NIGERIAN BANKING SECTOR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent times, financial technology advancement has been growing in volume of transactions. The increasingly used payment system has prompted concern on the long run impact of electronic payment on liquidity of the Nigerian banking sector. The study investigated impact of financial technology on the liquidity of the Nigerian banking sector. A case study research design was used to determine relationship existing between electronic payment services and banking sector liquidity in Nigeria. The study covered nine years period, using quarterly data spanning from the first quarter of 2009 to the fourth quarter of 2017. Secondary data was also collected in order to estimate the model. The dependent variable was proxied by loan to deposit ratio while the independent variables was proxied by automated teller machine, point of sales, mobile payment and automated clearing system-cheque. A unit root test was employed as a pre-estimation technique for this study, hence the variables where stationary at first difference. The study employed the Auto Regressive Distributed Lag or Bounds test approach in order to establish the short run dynamics and long run relationship of the model. Findings from the study suggested that there was a notable impact of electronic payment (fin-tech) on liquidity among all Deposit Money banks in Nigeria. Due to this finding the study concluded that an e-system in the banking sector will bring about financial development. Deposit Money banks should be encouraged to adopt electronic payment systems so as to have a better banking experience, easy access to banking products,reduced cost and flexibility of online international transactions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it