Effect of the Capability Component of Fraud Theory on Fraud Risk Management in Nigerian Banks
Bibliographic record
Abstract
The incidence of bank fraud is a fundamental problem with diverse consequences to banks and their stakeholders. Therefore, this study examined the effect of the capability component of fraud theory on fraud risk management in Nigerian banks. The specific objectives of the study are to: examine the effect of malicious insider abuses on fraud risk management efficiency of Nigerian banking sector; evaluate the effect of internal control bypasses on fraud risk management efficiency of Nigerian banking sector; investigate the effect of information security breaches on fraud risk management efficiency of Nigerian banking sector, and ascertain the effect of fraud risk governance on fraud risk management efficiency of Nigerian banking sector. The study adopted ex-post factoresearch design. Secondary data were gathered from the quarterly report on fraud and forgeries of the Financial Institutions Training Centre (FITC) from the first quarter of 2011 to the second quarter of 2020 given a total of thirty-eight (38) observations. The dependent variable of the study was fraud risk management efficiency (FRMη) while the independent variables were malicious insider abuses (MIA), Internal Control Bypasses (ICB), Information Security Breaches (ISB), and fraud risk governance (FRG). Four hypotheses were formulated and tested using robust linear regression analysis. The study employed Stata 14.2 and SPSS 22 in data analyses. We also conducted Skewness/Kurtosis and Shapiro-Francia W’ normality tests, Variance Inflation Factor (VIF) of multicollinearity, Breusch-Pagan/Cook Weisberg test of heteroskedasticity, and Durbin-Watson test for autocorrelation. The results revealed statistically significant negative effects of internal control bypasses and information security breaches on fraud risk management efficiency. The study also found an insignificant positive effect of malicious insider threats and fraud risk governance on fraud risk management efficiency. The implication of these findings is that the Nigerian banking sector is confronted with both internal and external fraud capability challenges which require management attention and stakeholders’ education and awareness. Based on these findings, the study offers comprehensive fraud vulnerability suggestions integrating all banking stakeholders (internal and external) to improve fraud risk management efficiency in Nigerian banking sector.
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 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 | 0.012 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".