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Record W3203706695

Insecurity of Cash-Less Banking Transactions: An Empirical Evidence from Nigerian Banks

2021· article· en· W3203706695 on OpenAlexaboutno aff
Amara Priscilia Ozoji, Okoi Etim Iwara, Charity Nkeiru Ezuwore-Obodoekwe, Sunday Joseph Inyada, Beatrice O. Ezechukwu, Polytechnic Oko, Terkura Fella Ayem-Fella, Chidimma Odilia Ezuma, Lilian N. Ebisi, Kemdi Lugard Okoroiwu

Bibliographic record

VenueAcademy of Accounting and Financial Studies journal · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
Fundersnot available
KeywordsCashDatabase transactionDescriptive statisticsDistributed lagEconomicsQuarter (Canadian coin)Unit root testBusinessActuarial scienceEconometricsStatisticsFinanceComputer scienceDatabaseCointegrationMathematics
DOInot available

Abstract

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This investigation primarily aimed at assessing cash-less banking operations since cash-less policy was introduced/implemented in Nigeria in the year 2012, with a view to determine the significance of its effect on insecurity of banking transactions in the economy. Ex-post facto research design and secondary sources of data collection were employed. The work studies the aggregate quarterly data (quarter 1-quarter 4, 2012 to quarter 1-quarter 4, 2019) of all the Deposit Money Banks operating in Nigeria as at 2012-2019 as contained in CBN statistics database and NDIC annual reports; summing up to 32 observations. Total quarterly volume of: automated teller machine transactions, point of sale terminals transactions, web transactions and mobile phone banking transaction were used as proxies for cash-less banking; while total quarterly: number of fraud and forgery cases in Nigeria’s Deposit Money Banks, amount involved in the attempted/reported fraud and forgery incidences in Nigeria’s DMBs and actual loss to fraud and forgery in Nigeria’s Deposit Money Banks were employed as proxies for insecurity of banking transactions. The study employed descriptive statistics to give the description of individual research variables and the inferential statistics, multivariate regression techniques of model estimation (Error Correction Model estimation and short-run, Autoregressive Distributed Lag model estimation) for data analysis/test of hypotheses which were preceded by Augmented Dickey-Fuller Unit Root Test and co-integration test using Autoregressive Distributed Lag bound testing technique. Findings revealed that the introduction of cash-less banking in Nigeria has not significantly affected the increased number of fraud and forgery cases in Nigeria’s Deposit Money Banks. It further disclosed that cash-less banking in Nigeria has significantly affected the increased amount involved in attempted/reported fraud and forgery incidences in Nigerian Deposit Money Banks. Finally, the results showed that the practice of cash-less banking in Nigeria has not significantly affected the actual loss to fraud and forgery in Nigerian Deposit Money Banks. The study concludes that the perception of most Nigerians that cash-less banking transactions are insecure is wrong; instead, cash-less banking is even more secure than the previously practiced cash-based banking system since the opportunity to actually commit the frauds (frauds reported as attempted amounts involved in fraud incidences) and inflict financial losses to banks and the victims was drastically minimized as revealed in the study’s results; only that there is need for more improvement on the security measures of some cash-less banking channels like automated teller machines (ATM) and internet. The study recommended that, Nigerian government should ensure the allocation of adequate funds for the establishment and equipping of special electronic fraud (cyber-crime) department within the policy force, and also training the Officers to serve under the department on the e-fraud policing. Banks’ Customers should keep their online and ATM transaction credentials (user ID, password, token/PIN) confidential. Also, Financial Institutions should ensure continuous review and security upgrade of its electronic platforms and services.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.101
GPT teacher head0.322
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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