Security Challenge, Bank Fraud and Commercial Bank Performance in Nigeria: An Evaluation
Bibliographic record
Abstract
The issues of insecurity and fraud in the banking sector of the Nigerian have become the concern of everyone. Achumba, Ighomereho, and Akpor-Robaro (2013) maintain that the concrete evidences of these incidences in different parts of Nigeria indicate that the security challenge in the country is enormous and complex and would continue to be, if the situation remains unabated. This paper evaluates the insecure situation, bank fraud and their impact on bank performance. This evaluation requires the formulation of some testable hypotheses to confirm the impact of insecurity and fraud on bank performance. Multiple regression analysis was applied to determine if there is any significant relationship between the indicators of bank insecurity, fraud and the earnings before tax (the indicator of bank performance) of the Commercial banks in Nigeria. Data were obtained through secondary sources on the indicators of bank insecurity and fraud and the earnings before tax of Commercial banks in Nigeria for the period 1991 -2013 from Nigeria Deposit and Insurance Corporation’s Annual Report. The results of the study demonstrate an inverse relationship between Expected Losses on insecurity and Fraud (ELF), Number of Fraud Cases (NFC) and Number of Staff involved in Fraud Cases and earnings before tax of commercial banks in Nigeria. The results of the Granger causality test show a uni-directional causality from bank insecurity and fraud to commercial bank performance. However, the Volume (Amount) of bank insecurity, Fraud cases (VFC) and earnings of commercial banks in the parsimonious ECM show positive but significant relationship. We therefore recommend that both government and banks should team up and involve foreign intervention in fighting insecurity and fraud in the banking sector. New staff of the bank should have a guarantor who will pledge a reasonable sum with the bank in case the staff is involved in fraud. Directors should be knowledgeable in accounting and banking and must have stakes in the bank.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".