




































American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

24 | P a g e  

INTERNAL CONTROL AND OPERATIONAL RISK OF QUOTED 

BANKS IN THE NIGERIAN STOCK EXCHANGE 
 

Ozuomba Chidinma N. 
Department of Accounting, University of Agriculture and Environmental Sciences Umuagwo, Imo 

State, Nigeria. 

Email: chidinmaozuomba@gmail.com; chidinma.ozuomba@uaes.edu.ng 

                                                       DOI: https://doi.org/10.5281/zenodo.14863271 

 

 

Abstract: Banks are more likely to fail from operational risk than from credit risk, and internal 

control at banks create operational risk losses. This study investigates the effect of internal control on 

operational risk of quoted banks in Nigeria. 16 quoted banks were studied based on the 2012 banking 

reform on corporate governance by the then CBN governor Sanusi Lamido Sanusi’s “Project Alpha 

Initiative” (PAI). The analysis carried out included pooled OLS regression, fixed and random effect 

and Hausman tests utilizing E-View 9 software. The findings shows that internal control activities 

have a negative correlation and internal control risk assessment has positive significant effect on 

operational risk. We recommend that internal check staff at banks should be sustained as there was 

an inverse relationship between internal check and operational risk at banks. Penalties should be spelt 

out for banking staff who are non-compliant with bank policies and guidelines especially in the area 

of breech in software codes. Banks should ensure that internal control unit personnel are qualified 

and adequately trained especially IT staff.  

Keyword: Internal Control, Control Activities, Risk Assessment, Operational Risk 

 

INTRODUCTION 

One of the main reasons for banking failures which results in major financial loss and even bankruptcy 

is high risks taken by bank management on an excessive scale and inability of controlling them. The 

lack of an internal control system which duty is to keep the risks or major breakdowns within an existing 

internal control system under control pose a threat against the success of the banking sector. This 

operational risk has risen drastically in recent times. According to Moosa, (2007) Banks are more likely 

to fail from operational risk than from credit risk, it is believed that internal control at banks create 

operational risk losses, and many institutions with such losses are repeat offenders (Chernobai, Deumes 

and Knechel 2011). 

Due to recent financial scandals and economic crisis, banking sector all across the globe has become 

vulnerable to fraudulent actions, rising uncertainties and development of more instruments have 

mailto:contact@americaserial.com
mailto:contact@americaserial.com
mailto:chidinma.ozuomba@uaes.edu.ng


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

25 | P a g e  

pressurized the banking organizations to look for the appropriate internal measures to transform their 

business organization as risk and uncertainty proof.  

The banking sector consolidation exercise of 2004/2005 had some salutary impact on the Nigerian 

economy and led to the emergence of bigger banks which, before the global financial crisis, created a 

general belief that the banking sector was sound and growth would be encouraged. However, this 

sentiment proved misplaced following the outbreak of the global financial and economic crises of 

2008/2009 and some interdependent factors that led to the manifestation of an extremely fragile 

financial system. This was because the main downside effect of the consolidation programme on the 

system was the near total neglect of adherence to good corporate governance practice. Corporate 

governance in many banks failed because their boards ignored best practices for various reasons, 

ranging from being misled by executive management and participating in obtaining unsecured loans at 

the expense of depositors, to lack of capacity to enforce good governance on bank management. There 

were also the problems of the overbearing influence on the boards by the Chairmen/CEOs, lack of 

independence of some boards, failure to make meaningful contributions to safeguard the growth and 

development of the banks, weak ethical standards, inadequate training for employees, failure to adhere 

to well established policies and procedures and ineffective board committees. These Internal control 

weaknesses are revealed in operational losses in banks. Consequently, a lot of scholars, accounting 

institutes, investors, standard setters and other stakeholders clamor for disclosure of corporate risk in 

financial reports across the globe as inherited risk from contemporary business environment is on the 

increase. This risk has claimed the lives and property of stakeholders especially shareholders and 

creditors just like the case of savannah bank in Nigeria during the 25-billion-naira capital base for banks 

automaton by the Central Bank of Nigeria in 2005. This obstacle has also tempered with investors’ 

confidence in the business world. Cabedo and Tirado (2004) are of the view that current practice of 

companies’ external reporting is considered insufficient because it is lacking an adequate disclosure on 

corporate risk and uncertainties. Corporate organizations owe a duty to fully disclose matters 

concerning their operations so as to aid investors in making investment decisions.  

METHODOLOGY 

The ex-post factor design type was used in this research work because it deals with historical facts and 

is designed to test an event that has already taken place. (Asika 2006; Agbadudu, 2002 cited in Ordu, 

Enekwe and Anyanwaokoro, 2014; Onwumere 2009). Secondary data was used in this work. The data 

machinery adopted for secondary data was Panel data set from banks published annual reports, NDIC 

report, CBN statistical bulletin, CBN fact books and banks’ Pillar III disclosure report was utilized for 

this study. The panel covers a time frame of 5 years from 2013-2017 and a cross section of 16 banks 

from the population of 23 commercial Banks quoted in the Nigerian Stock Exchange as at 28 September 

2018. However, Heritage bank, Savannah bank, Sky bank, keystone bank, Enterprise bank, Rand bank 

and Jaiz bank were eliminated based on availability of data, commencement of operation and Islamic 

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

26 | P a g e  

bank with different characteristics from commercial banks. The sample size is justified based on the 

theory of Mugenda and Mugenda, (2003), that a good sample covers at least 10%-30% of the 

representative population. Thus, at 67% coverage the sample is a fair representation of the population 

and sufficient for this study. Multiple regression analysis technique was used in this study. Panel data 

regression model was adopted in order to control for individual unobserved heterogeneity, obtain more 

accurate results because it provides more observations and information to work with, it allows a follow 

up on individual dynamics and before and after effect can be easily estimated (Temple, 1999; 

Woodridge, 2002; and Hsiao, 2003 as cited in Alajekwu, 2018). Cross-sectional and time series data 

are pooled in the regression to overcome the problem of insufficient degree of freedom. The Fixed 

Effects model (FEM) can be used to control the unobserved characteristics. Random effects model 

(REM) assumes that firm specific characteristics are not constant and the time effects are absent. The 

Hausman’s specification test in Panel data models was conducted for fixed and random effects test of 

individual characteristics or time effect.  

Table 1: Operational definition of variables 

  Variables Proxy 
variables 

Dependent  Operational Risk: 3 years Gross income @ 15% divided by 
3 

OPR 

Independent Control Environment:  Internal and External  

 Internal Environment:  Bank strength Income diversification 
Liquidity Employee size  

BS 
ID 
LD 
ES 

 External Environment:  Technology Socio-environmental 
factors Economic factor Legal factors  

TEC 
SEF 
ECF 
LGF 

 Risk Assessment Employee turnover 
Personnel Quality 

ET 
PQ 

 Control Activities Internal check 
Compliance and Prudence 
Internal auditors 

ICK 
CLP 
IAD 

 Monitoring 
 

Board size 
Board Independence 
Board internal audit size 
Board with expertise in finance 

BDS 
BDI 
IAS 
BEF 

 Information and 
Communication 

Feedback 
Feedforward 
Time lag 

FDB 
FDF 
TLG 

Control Variables Bank size  BS 
 Leverage  LEV 

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

27 | P a g e  

Source: Author’s conception, 2019 

 

 

We indicate that there are bank-specific and other variables which could affect the dependent variable 

in one way or the other and must be controlled. These variables are bank size and Leverage. 

Table2: showing definition of Proxy variable 

 Proxy 
Variables 

Derivation Source Aproprari 
expectations 

 OPR 3 years Gross income @ 15% divided by 3   

Model 
1 

BS 
ID 
LD 
ES 

Capital Adequacy Ratio = total equity/total 
assets 
non - interest income/total operating income 
(EBIT) 
total loans/total customers’ deposit 
yearly no of bank staff 

 
Adapted from 
Afia 2015 

 
Positive 

Model 
2 

TEC 
SEG 
ECF 
LGF 

Total amount in IT losses in the year 
Total amount of loss in fraud and forgeries 
reported by NDIC 
Non-performing loans in the year 
Litigation losses in the year 

Adapted from 
Afia 2015 

Positive 

Model 
3 

ICK 
CLP 
IAD 

No of internal check staff in the year 
Non adherence to accounting principles in the 
year 
No of internal audit department members 

 Positive 

Model 
4 

ET 
PQ 

%No of employee who have left 
%Employees at the beginning of the year + 
employee at year end/2 
No of errors and bugs in software codes 

 Positive 

Model 
5 

FDB 
FDF 
TLG 

Dummy 1 after 48 hrs and 0 at 48 hrs. 
Dummy 1 after 72 hrs and 0 at 72 hrs. 
Duration of deviation in compliance 

 Positive 

Model 
6 

BDS 
IAS 
BDI 
BEF 

No of board members 
Internal audit size 
No of independent board members 
No of board members with expertise in finance 

Adapted from 
Sadiq 2013, 
Ellis and Jordi 
2006, Almazari 
2014 

Positive 

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

28 | P a g e  

Model 
7 

BKS 
LEV 

 Total no of bank in the year 
Debt to total assets 

Ellis and Jordi 
2006; King’oo 
2015 

Negative 

Source: Author’s conception, 2019 

 

3.9 Model Specification and Justification 

Model 1: opr = (bs, id, ld, es, bks, lev) -------------------------------------------------------------------1 

 

opr!t = ao!t +b1+bs!t, b2+id!t,+ b3+ld!t,+ b4+es!t, + b5+bks!t, + b6+lev!t, + εr!t ---------------------2  

 

opr!t = ao!t +bs!t *id!t *ld!t *es!t *bks!t *lev!t + εr!t ------------------------------------------------------3 

 

β0, β1, β2, β3, β4, β5, β6= coefficients 

εi = error terms. 

 

Model 2: opr = (tec, sef, ecf, lgf, bks, lev) --------------------------------------------------------------1 

 

opr!t = ao!t +b1+tec!t, b2+sef!t,+ b3+ecf!t,+ b4+lgf!t, + b5+bks!t, + b6+lev!t, + εr!t ----------------2  

 

opr!t = ao!t +tec!t *sef!t *ecf!t *lgf!t *bks!t *lev!t + εr!t ------------------------------------------------3 

 

β0, β1, β2, β3, β4, β5, β6= coefficients 

εi = error terms. 

 

 Model 3: opr = (ick, clp, iad, bks, lev) -----------------------------------------------------------------1 

 

opr!t = ao!t +b1+ick!t,+ b2+clp!t, + b3+iad!t + b4+bks!t,+ b5+lev!t,+ εr!t ----------------------------2  

 

opr!t = ao!t +ick!t *clp!t *iad!t *bks!t *lev!t + εr!t --------------------------------------------------------3 

 

β0, β1, β2, β3, β4, β5= coefficients 

εi = error terms. 

 

Model 4: opr = (et, pq, bks, lev) -------------------------------------------------------------------------1 

 

opr!t = ao!t +b1+et!t, b2+pq!t, + b3+bks!t, + b4+lev!t,+ εr!t ------------------------------------------2  

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

29 | P a g e  

 

opr!t = ao!t +et!t *pq!t *bks!t *lev!t + εr!t -------------------------------------------------------3 

 

β0, β1, β2, β3, β4= coefficients 

εi = error terms. 

 

Model 5: opr = (fdb, fdf, tlg, bks, lev) ------------------------------------------------------------------1 

 

opr!t = ao!t +b1+fdb!t, b2+fdf!t,+ b3+tlg!t, + b4+bks!t, + b5+lev!t, + εr!t -----------------------------2  

 

opr!t = ao!t +fdb!t *fdf!t *tlg!t*bks!t *lev!t + εr!t --------------------------------------------------------3 

 

β0, β1, β2, β3, β4, β5= coefficients 

εi = error terms. 

 

Model 6: opr = (bds, ias, bdi bef) --------------------------------------------------------------1 

 

opr!t = ao!t +b1+bds!t, b2+ias!t,+ b3+bdi!t,+ b4+bef!t + εr!t ----------------------------------2  

 

opr!t = ao!t +bds!t *ias!t *bdi!t *bef!t *bks!t *lev!t + εr!t ---------------------------------------3 

 

β0, β1, β2, β3, β4= coefficients 

εi = error terms. 

The model is expected to be β0 > 0, β1 >0, β2> 0 β3 >0, β4 >0, β5 >0, β6 >0. 

These variables which cover all the five broad domains of internal controls found in the conceptual 

framework constitute the independent variables for the study. We indicate that there are bank-specific 

and other variables which could affect the dependent variable in one way or the other and must be 

controlled. These variables are bank size and Leverage. 

4.1 Descriptive Statistics 

The summary statistics provided information about the means, Standard deviation, minimum and 

maximum of all the employed variables. Mean is the average value of the series; the maximum and 

minimum values of the series are the highest and the lowest values of the series, while the standard 

deviation measures dispersion in the series. The descriptive statistics for the core variables are 

explained to give insight into the nature and activities of the selected quoted banks. 

4.1.1 Internal Control and Operational Risk 

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

30 | P a g e  

The Dependent Variable: Operational risk (OPR) is measured as three (3) years Gross income at 15% 

divided by 3. Of the 80 observations, it can be seen that OPR was 5.38; with the highest value, lowest 

value and standard deviation of 6.87, 4.30 and 30.09 respectively.  

 

The Independent Variable: internal Controls with it five (5) domain- (1) control environment, (2) 

control activities, (3) risk assessment, (4) information and communications and (5) monitoring are 

described as follows: 

1. Control environment was sub divided into  

(A) Internal environment with proxy variables- (i) bank strength measured with capital adequacy 

ratio which demonstrates the internal strength of the bank to support losses during crisis periods. 

High of this ratio shows high profitability and lower ratio indicates the decrease of the profitability. 

Capital adequacy is computed as a ratio of total equity to total asset. It showed a maximum value of 

223.00, minimum of 12.50 and standard deviation of 4252.00. (ii) Income diversification derived 

from non-interest income as a ratio of operating income (measured as earnings before interest and 

tax- EBIT) with an average value of 2.21, maximum value of 92.00, minimum of 0.84 and standard 

deviation of 8175.40. (iii) Liquidity measured as cash to asset ratio showed an average value of 19.48, 

maximum value of 86.29, minimum value of 1.65 and standard deviation of 2842.7 and (iv) Employee 

size measured as the total number of banking staff showing a mean value of 3.48, maximum value of 

4.97, minimum of 2.74 and a standard deviation of 13.74. 

(B) External environment with proxy variables- (i) Technology measured with total amount in IT 

losses which showed a mean value of 4.07, maximum value of 5.41, and minimum of 3.09 and 

standard deviation of 24.54. (ii) Socio-economic factor derived from total amount in fraud and 

forgeries reported by NDIC with an average value of 3.59, maximum value of 143.00, minimum of 

6.00 and standard deviation of 3956.90. (iii) Economic Factor measured as non-performing loan in 

the year showed an average value of 3.59, maximum value of 8.45, minimum value of 3.10 and 

standard deviation of 7.49 and (iv) Legal factor measured as litigation losses in the year showing a 

mean value of 7.40, maximum value of 112.00, minimum of 6.08 and a standard deviation of 27.45. 

(2) Control Activities is represented by three (3) proxy variables ICK, CLP and IAD explained as 

follows: 

(i) Internal Check measured as no of internal check staff showed a mean of 56.54, maximum value of 
6.00, minimum of 23.00 and standard deviation of 40147.89. (ii) Compliance with Accounting 
Principles measured by number of times there was a deviation from accounting principles. It showed 
an average value of 0.99, maximum value of 776.00, minimum of 0.00 and standard deviation of 
124.99. (iii) Internal Audit members measured by total number of audit staff showing an average value 
of 382.64, maximum value of 137.00, minimum value of 147.00 and standard deviation of 2428.09.  
(3) Risk Assessment is represented by two (2) proxy variables ET and PQ explained as follows: (i) 
Employee turnover measured percentage number of employee who have left dived by the percentage 

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

31 | P a g e  

number of employee at the beginning of the year plus percentage number of employee at the end of the 
year divided by two showed a mean of 68.85, maximum value of 14.10, minimum of 29.00 and standard 
deviation of 47670.20. (ii) Personnel Quality measured by number of errors and bugs in software codes 
showing an average value of 7.55, maximum value of 14.10, minimum of 4.40 and standard deviation 
of 314.10. 
(4) Information and communications are represented by three (3) proxy variables FDB, FDF and TLG 
explained as follows: (i) Feedback measured by 0 and 1. 0 is used when it takes more than 48hrs for 
board decision to be communicated to management and more than two weeks to be implemented and 
1 when information is timely. This showed a mean of 0.69, maximum value of 1.00, minimum of 0.00 
and standard deviation of 17.19. (ii) Feedforward measured by 0 and 1. 0 is used when it takes more 
than 48hrs for management decision to be communicated to the Board and more than two weeks for 
board decision. This showed a mean of 0.61, maximum value of 1.00, minimum of 0.00 and standard 
deviation of 18.84. (iii) Time Lag measured by 0 and 1. 0 is used when there is no delay in feedback and 
feedforward and 1 when there is delay. This showed a mean of 0.67, maximum value of 1.00, minimum 
of 0.00 and standard deviation of 17.44. 
(5) Monitoring with proxy variables- (i) Board size measured with Number of board members showed 
a mean value of 11.50, maximum value of 20.00, and minimum of 8.00 and standard deviation of 
342.00. (ii) Board Independence with an average value of 3.38, maximum value of 10.00, minimum of 
2.00 and standard deviation of 108.75. (iii) Board internal audit staff showed an average value of 6.53, 
maximum value of 9.00, minimum value of 4.00 and standard deviation of 165.95 and (iv) Board 
expertise in finance showing a mean value of 6.44, maximum value of 9.00, minimum of 6.99 and a 
standard deviation of 181.69. 
(6) Control variables with proxy variables- (i) Bank size showed a mean value of 9.16, maximum value 
of 16.03, and minimum of 5.56 and standard deviation of 156.42. (ii) Leverage with an average value of 
66.12, maximum value of 93.01, minimum of 5.56 and standard deviation of 89567.04.  
Table 3: Descriptive Analysis for Internal Control variables and Operational Risk 
variable from 2013-2017 

VARIABLES COMMERCIAL BANKS 
  Mean Max Min Std. Dev. 
Dependent 
Variable 

Operational Risk 5.38 6.87 4.30 30.09 

Control 
Environment
:  

Internal and 
External 

    

Internal 
Environment
:  

Bank strength  
(BS) (Ratio) 
Income 
diversification 
(ID)(ratio) 
Liquidity  
(LD) (ratio) 
Employee size  

23.58 
 
2.21 
 
19.48 
 
3.48 

223.00 
 
92.00 
 
86.29 
 
4.97 

12.50 
 
0.84 
 
1.65 
 
2.74 
 

4252.00 
 
8175.40 
 
2842.7 
 
13.74 

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

32 | P a g e  

(ES)  

External 
Environment
:  

Technology 
(TEC) Socio-
environmental 
factors (SEF) 
Economic factor 
(ECF) Legal 
factors  
(LGF)  

4.07 
 
32.10 
 
 
3.59 
 
7.40 
 
 

5.41 
 
143.00 
 
 
8.45 
 
112.00 
 
 

3.09 
 
6.00 
 
 
3.10 
 
6.08 

24.54 
 
3956.90 
 
 
7.49 
 
27.45 
 
 
 
 

Control 
Activities 

Internal check 
(ICK) 
Compliance and 
Prudence (CLP) 
Internal auditors 
(IAD) 

56.54 
 
0.99 
 
382.64 

6.00 
 
776.00 
 
137.00 

23.00 
 
0.00 
 
147.00 
 

40147.89 
 
124.99 
 
2428.09 
 

Risk 
Assessment 

Employee 
turnover  
(ET) (%) 
Personnel 
Quality (PQ) 

68.85 
 
 
7.55 

14.10 
 
 
14.10 

29.00 
 
 
4.40 

47670.20 
 
 
314.20 
 

Information 
and 
Communicati
on 

Feedback (FDB) 
 
Feedforward 
(FDF) 
 
Time lag (TLG) 

0.69 
 
0.61 
 
0.67 

1.00 
 
1.00 
 
1.00 

0.00 
 
0.00 
 
0.00 

17.19 
 
18.84 
 
17.44 

Monitoring 
 

Board size (BDS) 
 
Board 
Independence 
(BDI) 
 
Board internal 
audit (BIAS) 
 
Board with 
expertise in 
finance (BEF) 

11.50 
 
3.38 
 
 
 
6.53 
 
 
6.44 
 

20.00 
 
10.00 
 
 
 
9.00 
 
 
9.00 

8.00 
 
2.00 
 
 
 
4.00 
 
 
6.99 

342.00 
 
108.75 
 
 
 
165.95 
 
 
181.69 
 
 

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

33 | P a g e  

Source: Output generated using Eviews 7 

4.2 Normality Test 
Jargue-Bera test of normality was used to identify the normality of error term. It is tested at 0.05 level 
of significance. The decision rule is to reject the null hypothesis, when P. value is less than 0.05 level of 
significance, otherwise, do not reject. The null hypothesis that error terms are normally distributed is 
rejected at 5% level of significance for all the variables. Thus, error term is not normally distributed. 
The variable used in the study lacks normality for selected commercial banks quoted in the Nigerian 
Stock Exchange. 
Table 4: Result of Jargue- Bera Satistics for the test of normality 

Control 
Variables 

Bank size (BKS) 
 
Leverage (LEV) 
(Ratio) 

9.16 
 
66.12 
 
     
 

16.03 
 
93.01 

5.56 
 
5.56 

156.42 
 
89567.04 

VARIABLES COMMERCIAL BANKS 
  Jarque-Bera Prob.  

Dependent Variable Operational Risk 1.59 0.35  

Control 
Environment:  

Internal and External    

Internal 
Environment:  

Bank strength (BS) (Ratio) Income 
diversification (ID)(Tobin’s Q) Liquidity (LD) 
(ratio) Employee size (ES)  

15828.36 
19681.30 
215.47 
29.47 

0.00 
0.00 
0.00 
0.00 

 
 
 

External 
Environment:  

Technology (IT) Socio-environmental factors 
(SEF) Economic factor (ECF) Legal factors 
(LGF)  

1.90 
264.01 
4.58 
3.66 

0.39 
0.00 
0.10 
0.16 

 

Control Activities Internal check (ICK) 
Compliance and Prudence (CLP) 
Internal auditors (IAD) 

8.09 
57.48 
6.38 

0.01 
0.00 
0.04 

 

Risk Assessment Employee turnover (ET) (%) 
Personnel Quality (PQ) 

3.94 
61.33 

0.13 
0.00 

 

Information and 
Communication 

Feedback (FDB) 
Feedforward (FDF) 
Time lag (TLG) 

14.76 
13.29 
14.08 

0.00 
0.01 
0.00 

 

Monitoring 
 

Board size (BDS) 
Board Independence (BDI) 
Board internal audit (BIAS) 
Board with expertise in finance (BEF) 

122.36 
2.80 
454.70 
3.50 

0.00 
0.00 
0.24 
0.17 

 

Control Variables Bank size (BKS) 469.96 0.00  

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

34 | P a g e  

Source: Output generated using Eviews 7 

 

4.3 Test for Multicollinearity 

The test is conducted to check for suitability of the of the control variables in each of the model. Model 

1 to 6 are the theoretical model of the relationship between operational risk and internal controls. Bank 

size and Leverage being control variables were tested for the existence of multicollinearity between 

variable using correlation matrix as shown on table 8.the existence of collinearity shows that the 

regression cannot precisely intercept the influence of independent variable towards dependent variable 

(Gujarati and Porter, 2009). High pair wise correlation between two variables means there is a serious 

multicollinearity problem in the regression model. The level of high multicollinearity exists when the 

correlation between two variables exceed 0.8 (Gujarati and Porter, 2009). The result on table 8 showed 

correlation matrix for quoted banks. The highest pair wise correlation is 0.79 and the lowest is -0.21. 

Since it is not more than 0.8, the researcher conclude that the two variables do not suffer from serious 

multicollinearity and that the six model in which the five objectives are anchored are suitable for 

regression analyses. 

Table 5: Correlation Matrix for test for multicollinearity in Operational Risk (OPR) and Control 

Variables (BKS and LEV) of the study. 

    

 OPR BKS LEV 

OPR 0.729393   

BKS 0.065918 0.798290  

LEV -0.218296 -0.345518 0.747946 

    

    

4.4 Goodness of Fit Test 

This is a measure of how well the observed moments fit which is the covariance between all pairs of 

relationship. When all variables in the model are observed, there may not be a need for Goodness –of-

Leverage (LEV) (Ratio) 18.08 0.00 
 Number of Banks 

Number of Observation 
 
 
 

16 
80 

 

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

35 | P a g e  

fit statistcs but in order to account for the overly influence of sample size and correlation to the model 

and multivariant non-normality (Kline, 2011). Since the Incremental fit indices are less than 0.05% 

level of significance, we reject the null hypothesis and accept the alternate hypothesis that the baseline 

and hypothesis in the model have a good fit. 

 

Table 6: Showing Goodness of Fit 

Goodness-of-fit Summary  

Factor: Untitled   

Date: 07/14/19 Time: 23:09  

    
     Model Independence Saturated 

Parameters  3  3  6 

Degrees-of-freedom  3  3 --- 

Parsimony ratio  1.000000  1.000000 --- 

    
    Absolute Fit Indices  

 Model Independence Saturated 

Discrepancy  0.373011  0.373011  0.000000 

Chi-square statistic  29.46791  29.46791 --- 

Chi-square probability  0.0000  0.0000 --- 

Bartlett chi-square statistic  28.78405  28.78405 --- 

Bartlett probability  0.0000  0.0000 --- 

Root mean sq. resid. (RMSR)  0.296842  0.296842  0.000000 

Akaike criterion  0.293349  0.293349  0.000000 

Schwarz criterion  0.204023  0.204023  0.000000 

Hannan-Quinn criterion  0.257535  0.257535  0.000000 

Expected cross-validation (ECVI)  0.448961  0.448961  0.151899 

Generalized fit index (GFI)  0.850174  0.850174  1.000000 

Adjusted GFI  0.700347  0.700347 --- 

Non-centrality parameter  26.46791  26.46791 --- 

Gamma Hat  0.000000  0.000000 --- 

McDonald Noncentralilty  0.845761  0.845761 --- 

Root MSE approximation  0.334184  0.334184 --- 

    
    Incremental Fit Indices  

 Model   

Bollen Relative (RFI)  0.000000   

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

36 | P a g e  

Bentler-Bonnet Normed (NFI)  0.000000   

Tucker-Lewis Non-Normed (NNFI)  0.000000   

Bollen Incremental (IFI)  0.000000   

Bentler Comparative (CFI)  0.000000   

        4.5 Test of Hypotheses 

The five objectives of the study were estimated for the effect of internal control on operational risk. The 

analyses were conducted for pooled OLS, fixed effect and random effect model. The results are shown 

on table 8-13 for internal and external control environment, control activities, risk assessment, 

information and communications and monitoring. 

The analyses involved commercial 16 banks quoted in the Nigerian stock exchange for a period of five 

years (2013-2017) and consisting of 80 observations. They are presented as follows; 

4.5.1 H01: Internal control environment system does not have significant effect on 

operational risk. 

Four variables representing model 1 on the effect of internal control environment on operational risk 

were employed to test the hypotheses of this study. From the regression analysis result as shown on 

table 8, it is observed that r2 for pooled OLS, fixed effect and random effect are 0.20 and 0.21 

respectively and that of random effect is 0.90 that is, for each model used 20%, 21% and 90% of the 

dependent variable (OPR) is explained by the independent variables: BS, ID, LD and ES and control 

variable BKS and LEV. The coefficient value of the independent proxy variables: BS, ID, LD, and ES are 

positively correlated with the dependent variable OPR. This implies that any decrease in the 

independent variables will result in a decrease in the dependent variable. From the further test 

conducted, the fixed effect model showed a value of 166.954077 with a probability of 0.0000 and the 

random effect model showed a value of 4.125385 and a probability of 0.6597. The fixed effect is 

preferred because the probability of the Chi. Square is less than 0.05% level of significance. From the 

result obtained, we accept the alternate Hypotheses which states that internal control environment has 

a significantly positive effect on operational risk of quoted banks in Nigeria and reject the null 

hypothesis. The variables employed showed positive value that is, any increase/decrease in any of the 

independent variables will lead to an increase in the dependent value except for bank size that does not 

have a positively significant effect on operational risk.  

Durbin Watson is close to 2.0 as such the variables are highly significant. Probability values of the 

coefficient at 0.1 – 0.7 implies that the regression parameters are significantly different from zero and 

the probability for the variables reveal a normal curve. The F-statistics is 1.766192 to show that the 

coefficient of explanatory variables has a significant effect on operational risk in the annual financial 

reports of quoted companies in Nigeria. From the result obtained, we accept the alternate Hypothesis 

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

37 | P a g e  

which states that internal control environment has a significantly positive effect on operational risk of 

quoted banks in Nigeria and reject the null hypothesis. 

 

 

 

Table 7: Result of the effect of internal control environment on operational risk of quoted 

banks in Nigeria 

4.5.2 H01: External control environment does not have significant effect on operational 
risk.  
Four variables representing model 2 on the effect of external control environment on operational risk 
were employed to test the hypotheses of this study. From the regression analysis result as shown on 
table 9, it is observed that r2 for pooled OLS, fixed effect and random effect are 0.25 and 0.26 
respectively and that of random effect is 0.92 that is, for each model used 25%, 26% and 92% of the 
dependent variable (OPR) is explained by the independent variables: TEC, SEF, ECL and LGF and 
control variable BKS and LEV. The coefficient value of the independent proxy variables: SEF, BKS and 

  

Independent Variables Pooled OLS Fixed Effect 

(Preferred Model) 

Random Effect 

Constant (C) 

 

Bank Strength (BS) 

 

Income Diversification (ID) 

 

Liquidity (LD) 

 

Employee Size (ES) 

 

Bank Size (BKS) 

 

Leverage (LEV) 

3.599612* 

(4.695850) 

0.002450* 

(0.848119) 

0.130211* 

(0.695367) 

0.009810* 

(2.670924) 

0.403149* 

(2.421923) 

-0.060530* 

(-1.115413) 

0.008291* 

(3.455690) 

3.545422* 

(4.380025) 

0.002862* 

(0.936542) 

0.150099* 

(0.748953) 

0.009888* 

(2.619871) 

0.403026* 

(2.307828) 

-0.058434* 

(-1.032348) 

0.008335* 

(3.371851) 

4.763952* 

(5.480358) 

0.001704* 

(1.232416) 

0.045398* 

(0.448589) 

-0.005624* 

(-1.282022) 

0.072616* 

(0.515052) 

-0.055025* 

(-1.280855) 

0.013468* 

(1.508671) 

R-Squared 

F-Statistics (Prob.) 

Durbin Watson (DW) 

Hausman Test (Prob.) 

0.201216* 

0.010817 

0.285333 

0.206182* 

1.766192(0.083838) 

0.278861 

166.954077(0.000000) 

** 

0.901387* 

24.81041(0.000000) 

1.636648 

4.125385 (0.6597) ** 

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

38 | P a g e  

LEV are positively correlated with the dependent variable OPR. While TEC, ECF and LGF are negatively 
correlated with the dependent variable OPR. This implies that any increase/decrease in the 
independent variables will result in an increase/decrease in the dependent variable. From the further 
test conducted, the redundant (Hausman) fixed effect model showed a value of 188.907070 with a 
probability of 0.0000 and the Hausman random effect model showed a value of 23.374523 and a 
probability of 0.0007. The fixed effect is preferred because the probability of the Chi. Square is less than 
0.05% level of significance. From the result obtained, we accept the alternate Hypotheses which states 
that external control environment has a significantly positive effect on operational risk of quoted banks 
in Nigeria and reject the null hypothesis. The variables employed showed positive value that is, any 
increase/decrease in any of the independent variables will lead to an increase in the dependent value 
except for TEC, ECF and LGF that does not have a positively significant effect on operational risk.  
Durbin Watson is close to 2.0 as such the variables are highly significant. Probability values of the 
coefficient at 0.1 – 0.7 implies that the regression parameters are significantly different from zero and 
the probability for the variables reveal a normal curve. The F-statistics is 2.474900 to show that the 
coefficient of explanatory variables has a significant effect on operational risk in the annual financial 
reports of quoted companies in Nigeria. From the result obtained, we accept the alternate Hypothesis 
which states that external control environment has a significantly positive effect on operational risk of 
quoted banks in Nigeria and reject the null hypothesis. 
Table 8: showing the effect of external control environment on operational risk 

  
Independent Variables Pooled OLS Fixed Effect 

(Preferred Model) 
Random Effect 

Constant (C) 
 
Technology (TEC) 
 
Socio-economic 
Factor (SEF) 
Economic factor (ECF) 
 
Legal factor(LGF) 
 
Bank Size (BKS) 
 
Leverage (LEV) 

10.24404* 
(7.457786) 
-0.013726* 
(-0.085779) 
0.003575* 
(0.875275) 
-0919117* 
(-3.291574) 
-0.291641* 
(-2.492607) 
0.001713* 
(0.030498) 
0.007905* 
(2.493555) 

10.48535* 
(7.364314) 
-0.001410* 
(-0.008421) 
0.004563* 
(1.034498) 
-0.974016* 
(-3.313844) 
-0.314992* 
(-2.586460) 
0.003191* 
(0.055190) 
0.008405* 
(2.503374) 

-4.088791* 
(-1.850001) 
-0.117646* 
(-1.330116) 
0.000784* 
(0.399997) 
2.429391* 
(4.187763) 
0.192742* 
(2.445534) 
-0.058757* 
(-1.646174) 
0.004791* 
(0.688127) 

R-Squared 
F-Statistics (Prob.) 
Durbin Watson (DW) 
Hausman Test 

0.252342* 
0.001321 
0.309938 

0.263992* 
2.474900(0.013521) 
0.313080 
188.907070 (0.0000) 
** 

0.928226* 
35.71892(0.000000) 
1.962402 
23.374523(0.0007) 
** 

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

39 | P a g e  

4.5.3 H02: Control activities does not have significant effect on operational risk. 

Three proxy variables representing model 3 on the effect of control activities on operational risk were 
employed to test the hypotheses of this study. From the regression analysis result as shown on table 10, 
it is observed that r2 for pooled OLS, fixed effect and random effect are 0.29 and 0.29 respectively and 
that of random effect is 0.89 that is, for each model used 29%, 29% and 89% of the dependent variable 
(OPR) is explained by the independent variables: ICK, CLP and IAD and control variable BKS and LEV. 
The coefficient value of the independent proxy variables: CLP, IAD and LEV are positively correlated 
with the dependent variable OPR. While ICK and BKS are negatively correlated with the dependent 
variable OPR. This implies that any increase/decrease in the independent variables will result in an 
increase/decrease in the dependent variable. From the further test conducted, the redundant 
(Hausman) fixed effect model showed a value of 157.193595 with a probability of 0.0000 and the 
Hausman random effect model showed a value of 2.O42970 and a probability of 0.8432. The fixed effect 
is preferred because the probability of the Chi. Square is less than 0.05% level of significance. From the 
result obtained, we accept the alternate Hypotheses which states that control activities has a 
significantly positive effect on operational risk of quoted banks in Nigeria and reject the null hypothesis. 
The variables employed showed positive value that is, any increase/decrease in any of the independent 
variables will lead to an increase in the dependent value except for internal check and bank size that 
does not have a positively significant effect on operational risk.  
Durbin Watson is close to 2.0 as such the variables are highly significant. Probability values of the 
coefficient at 0.1 – 0.7 implies that the regression parameters are significantly different from zero and 
the probability for the variables reveal a normal curve. The F-statistics is 2.474900 to show that the 
coefficient of explanatory variables has a significant effect on operational risk in the annual financial 
reports of quoted companies in Nigeria. From the result obtained, we accept the alternate Hypothesis 
which states that control activities have a significantly positive effect on operational risk of quoted 
banks in Nigeria and reject the null hypotheses. 
Table 9: showing the effect of control activities on operational risk  

  
Independent Variables Pooled OLS Fixed Effect 

(Preferred Model) 
Random Effect 

Constant (C) 
 
Internal Check (ICK) 
 
Compliance Principle (CLP) 
 
Internal Audit Dept. (IAD) 
 
Bank Size (BKS) 
 
Leverage (LEV) 

5.612601* 
(12.99178) 
-0.009437* 
(-2.806589) 
-0.052043* 
(-1.033823) 
0.002038* 
(0.0000) 
-0.097963* 
(0.0570) 
0.007169* 
(3.499688) 

5.615702* 
(12.50482) 
-0.009480* 
(-2.739738) 
-0.053243* 
(-1.017637) 
0.002037* 
(4.490175) 
-0.097874* 
(-1.854999) 
0.007167* 
(3.391316) 

4.870071* 
(6.234542) 
-0.002051* 
(-0.354322) 
-0.005497* 
(-0.234809) 
0.000942* 
(0.737127) 
-0.052716* 
(-1.269378) 
0.011460* 
(1.449637) 

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

40 | P a g e  

 

 

References 

Abdullah, A., Khan, A. Q., & Nazir, N. (2012). A comparative study of credit risk management: A case 

study of domestic and foreign banks in Pakistan. Academic Research International, 3(1), 371-

377. 

Adams, M. B. (1994). Agency theory and the internal audit. Managerial Auditing Journal, 5(1), 9-15. 

Adeyemi, S. B. (2006). Impact of accounting standards on financial reporting in Nigeria (Unpublished 

doctoral dissertation). University of Lagos. 

Ahmed, N. K., Alfarra, H. X., Ahmed, H., & Mahmoud, A. E. (2017). Potential influence of information 

systems on bank risk. IAENG International Journal of Computer Science, 44(2), 1-9. 

Ahmed, N., Akhtar, M. F., & Usman, M. (2011). Risk management practices and Islamic banks: An 

empirical investigation from Pakistan. Interdisciplinary Journal of Research in Business, 1(6), 

50-57. 

Akhigbe, A., & Martin, A. D. (2008). Influence of disclosure and governance on risk of US financial 

services firms following Sarbanes-Oxley. Journal of Banking and Finance, 32, 2124-2135. 

Akhigbe, A., & Whyte, A. M. (2003). Changes in market assessments of bank risk following the Riegle-

Neal Act of 1994. Journal of Banking and Finance, 27, 87-102. 

Akhtar, M. F., Ali, K., & Sadaqat, S. (2011). Liquidity risk management: A comparative study between 

conventional and Islamic banks of Pakistan. Interdisciplinary Journal of Research in Business, 

1(1), 35-44. 

Archambault, J. J., & Archambault, M. E. (2003). A multinational test of determinants of corporate 

disclosure. The International Journal of Accounting, 38, 173-194. 

Asika, N. (2006). Research methodology in behavioural sciences (2nd ed.). Longman Nigeria PLC. 

R-Squared 
F-Statistics (Prob.) 
Durbin Watson (DW) 
Hausman Test 

0.290638* 
(0.000093) 
0.252821 

0.292507* 
3.215653(0.002575) 
0.248353 
157.193595(0.0000) ** 

0.899063* 
26.27619(0.000000) 
1.577831 
2.042970(0.8432) ** 

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

41 | P a g e  

Basel Committee on Banking Supervision. (2010). Principles for enhancing corporate governance. 

Retrieved from www.bis.org 

Barron, O. E., Kile, C. O., & Keefe, T. B. (1999). MD&A quality as measured by the SEC and analysts’ 

earnings forecasts. Contemporary Accounting Research, 1(1), 75–109. 

Beasley, M. B. (2007). An empirical analysis of the relation between board of director compensation 

and financial statement fraud. The Accounting Review, 71(4), 443-466. 

Cabedo, J. D., & Tirado, J. M. (2004). The disclosure of risk in financial statements. Accounting Forum, 

28(8), 181-200. 

Cebenoyan, A. S., & Zarb, F. G. (2004). Risk management, capital structure and lending at banks. 

Journal of Banking & Finance, 28(1), 19–43. 

Chernobai, R., Deumes, R., & Knechel, W. R. (2011). Economic incentives for voluntary reporting on 

internal risk management and control systems. Auditing: A Journal of Practice and Theory, 

27(1), 35-66. 

Committee of Sponsoring Organizations of the Treadway Commission (COSO). (1992). Internal 

control-integrated framework. New York: AICPA. 

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–

334. 

Delis, M. D., & Karavias, Y. (2014). Optimal versus realized bank credit risk and monetary policy. 

Journal of Financial Stability, 16, 13-30. 

Ewa, E. U., & Udoayang, J. O. (2012). The impact of internal control design on banks' ability to 

investigate staff fraud, and lifestyle and fraud detection in Nigeria. International Journal of 

Research in Economics & Social Sciences, 2(2), 32-43. 

Hossain, M. (2008). The extent of disclosure in annual reports of banking companies: The case of India. 

European Journal of Scientific Research, 23(4), 659-680. 

Iatridis, G. (2008). Accounting disclosure and firms' financial attributes: Evidence from the UK stock 

market. International Review of Financial Analysis, 17(2), 219-241. 

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

42 | P a g e  

Idowu, A. (2009). An assessment of fraud and its management in Nigeria commercial banks. European 

Journal of Social Sciences, 10(4), 628-640. 

Jones, M. J. (2008). Internal control, accountability and corporate governance: Medieval and modern 

Britain compared. Accounting, Auditing & Accountability Journal, 7, 1052–1075. 

Kantarelis, D. (2007). Theories of the firm. Kenya Financial Sector Stability Report, 2010. 

Khan, T., & Ahmed, H. (2001). Risk management: An analysis of issues in Islamic financial industry. 

IRTI/IDB Occasional Paper, No. 5. 

Leng, L., & Ding, Y. (2011). Internal control disclosure and corporate governance: Empirical research 

from Chinese listed companies. Technology and Investment, 2(4), 286-294. 

https://doi.org/10.4236/ti.2011.24029 

Manthos, D. D., & Yiannis, K. (2015). Optimal versus realized bank credit risk and monetary policy. 

Journal of Financial Stability, 16, 13-30. 

Mohammed, H. A. (2013). Internal auditing practices and internal control system in Somali remittance 

firms. International Journal of Business and Science, 4(4). 

Moosa, I. A. (2007). Operational risk: A survey. Financial Markets, Institutions, and Instruments, 16, 

167-194. 

Munene. (2013). Effect of internal control on financial performance of technical training institute in 

Kenya (Unpublished master's thesis). 

Nejeri, K. (2014). Effect of internal controls on the financial performance of manufacturing firms in 

Kenya (Unpublished master's thesis). 

Nyakundi, D. O., Nyamita, M. O., & Tinega, T. M. (2014). Effect of internal control systems on financial 

performance of small and medium-scale business enterprises in Kisumu City, Kenya. 

International Journal of Social Sciences and Entrepreneurship, 1(11), 719-739. 

Ofoegbu, G., & Okoye, E. (2006). The relevance of accounting and auditing standards in corporate 

financial reporting in Nigeria: Emphasis on compliance. The Nigerian Accountant, 39(4), 45-53. 

Ogwuma, P. A. (1998). The efforts of the Central Bank of Nigeria in the fight against advance fee fraud. 

The Bullion, 22(2), 21–24. 

mailto:contact@americaserial.com
mailto:contact@americaserial.com


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ARJEFM, Email: contact@americaserial.com 

43 | P a g e  

Olaoye, C. (2009). Impact of internal control system in banking sector. Pakistan Journal of Social 

Sciences, 6(4), 181-187. 

Oyerogba, E. O. (2014). Risk disclosure in the published financial statements and firm performance: 

Evidence from the Nigeria listed companies. Journal of Economics and Sustainable 

Development, 5(8), 86-96. 

Pathan, S. (2009). Strong boards, CEO power and bank risk-taking. Journal of Banking and Finance, 

33(7), 1340-1350. https://doi.org/10.1016/j.jbankfin.2009.02.001 

Verrecchia, R. E. (1999). Disclosure and the cost of capital: A discussion. Journal of Accounting and 

Economics, 26, 271–283. 

Vithessonthi, C. (2014). The effect of financial market development on bank risk: Evidence from 

Southeast Asian countries. International Review of Financial Analysis, 35, 249-260. 

Wallace, R. S. O. (1988). Corporate financial reporting in Nigeria. Accounting and Business Research, 

18(72), 352-362. 

mailto:contact@americaserial.com
mailto:contact@americaserial.com

