Application of forensic accounting techniques in the South African banking industry for the purpose of fraud risk mitigation

Figures & data

Figure 1. Wide fraud risk assessment and response rate.

Source: KPMG (2019:16)

Figure 2. Research design.

Source: Authors’

Table 1. Chi-square and fischer’s exact tests for the possible causes of cyberfraud

Identified possible causes of cyberfraud	Chi-square statistics	df	Asymp. Sig.	Fischer’s Exact Sig.	Point probability
Overrides of internal controls by the management (OV)	8.714	4	0.069	0.071	0.007
Poor organisational culture (POC)	23.476	4	0.000	0.000	0.000
Lack of ethical culture (LEC)	18.714	4	0.001	0.001	0.000
Absence of accountability (AOA)	24.429	4	0.000	0.000	0.000
Inadequate documentation/ record keeping (ID)	8.000	4	0.092	0.091	0.005
Installation & use of new technology (INT)	10.429	2	0.005	0.006	0.001

Source: Field Survey

Table 2. Possible causes of cyberfraud

Possible causes of cyberfraud	No of response (n) Agree	No of response (n) Strongly Agree	No of response (n) Disagree	No of response (n) Strongly Disagree	No of response (n) Undecided	Total no of respondents (N)
Overrides of internal controls by the management	13	7	11	9	2	42
Poor organisational culture	11	2	16	11	2	42
Lack of ethical culture	7	2	17	13	3	42
Absence of accountability	6	7	14	14	1	42
Inadequate documentation/ record keeping	9	3	11	15	4	42
Installation & use of new technology	18	20	0	4	0	42

Source: Field Survey

Figure 3. The identified possible causes of cyberfraud.

Source: Survey

Table 3. The statistical analysis of the possible causes of cyberfraud

Paired Factors	Fischer’s Exact Test Statistics	df	Exact. Sig. (2 tailed)	Remarks	Spearman’s Correlation Coefficient	Relationship
POC & LEC	64.770	16	0.000	23 cells (92.0%) have expected outcomes of less than 5. The minimum expected count is 0.05	0.542	Positive and moderate
POC & AA	30.158	16	0.001	23 cells (92.0%) have expected outcomes of less than 5. The minimum expected count is 0.02	0.545	Positive and moderate
LEC & AA	38.180	16	0.000	23 cells (92.0%) have expected outcomes of less than 5. The minimum expected count is 0.05	0.731	Positive and strong
OIC & POC	27.255	16	0.004	24 cells (96.0%) have expected outcomes of less than 5. The minimum expected count is 0.05	0.429	Positive and moderate
OIC & LEC	26.780	16	0.003	24 cells (92.0%) have expected outcomes of less than 5. The minimum expected count is 0.10	0.415	Positive and moderate
OIC & AA	28.358	16	0.002	24 cells (92.0%) have expected outcomes of less than 5. The minimum expected count is 0.05	0.524	Positive and moderate
OIC & ID	24.553	16	0.011	25 cells (100.0%) have expected outcomes of less than 5. The minimum expected count is 0.19	0.069	Positive but weak
ID & INT	13.993	8	0.034	12 cells (80.0%) have expected outcomes of less than 5. The minimum expected count is 0.57	0.174	Positive but weak

Source: Field Survey

Figure 4. Cause - effect diagram of the possible causes of cyberfraud.

Source: Authors’

Figure 5. Cross tabulation chart for fraud risk assessment and management with respect to effective forensic accounting application.

Source: Survey results obtained from SPSS

Table 4. Forensic accounting technique implementation for risk assessment and management

Variables	No of response (n) Agree	No of response (n) Strongly Agree	No of response (n) Disagree	No of response (n) Strongly Disagree	No of response (n) Undecided	Total no of respondents (N)
Effective forensic accounting techniques can promote fraud risk assessment	34	6	0	0	2	42
Effective forensic accounting techniques can promote fraud risk management	30	8	0	0	4	42

Source: Field survey

KPMG. 2019. The multifaceted threat of fraud: are banks up to the challenge? Global banking fraud survey. www.kpmg.com. [Accessed: 2nd February 2020]

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