![](bg7.png)
guarantor shall be liable in case of non-
compliance by the customer.
2. I recommend banks to close the account of any
person who does not commit to pay up to three
cheques and to conduct a complete and detailed
study of customers who want to open a bank
account, which will help in the inability of
scammers to have a cheques book and thus the
inability to buy through cheques. And thus helps
companies not to fall into the trap of these
fraudsters and prevent them from falling into this
problem.
3. The government must impose strict laws against
those who fail to commit themselves to paying
the amounts due, forcing any person to fear the
consequences before thinking about fraud from
the bank and forcing him to commit to pay the
value of cheques and promissory notes on time.
Such as long prison terms or pay fines.
4. I proposed the establishment of a huge
information system between the banks operating
in the country and the Association of
Accountants and a committee of the government,
where the idea to collect all data in this system
includes all persons with bank accounts and
therefore the company and before the approval of
the sale back to this system through the
Association of Accountants And the approval of
the government committee to obtain this
information, which makes the names of
fraudsters in this system on the black list. Not
only cheques, but all kinds of installment as the
companies have many names of referrals who
dealt with them through promissory notes and
then can add their names to the black list in the
system, which benefits all other companies that
follow the system of sale installments. This
significantly reduces the sale of fraudsters
because the company has advance information
about this person through this common system.
Not only that, but companies can add information
about all the customers who have bought from
them whether they were fraudulent or committed
to pay their dues.
REFERENCES
Albashrawi, M. (2016). Detecting financial fraud using data
mining techniques: A Decade Review from 2004 to
2015. Journal of Data Science, 14(3), 553-569.
Brennan, P. (2012). A comprehensive survey of methods for
overcoming the class imbalance problem in fraud
detection. Institute of technology Blanchardstown
Dublin, Ireland.
Chakrabarti et al. (2006). Data mining curriculum: A
proposal (Version 1.0). Intensive Working Group of
ACM SIGKDD Curriculum Committee, 140.
Desai, B. and Deshmukh, R. (2013). Data mining
techniques for fraud detection. International Journal of
Computer Science and Information Technologies
(IJCSIT), 4(1), 1-4.
Dzomira, S. (2015). Fraud prevention and detection
research. Journal of finance and Accounting, 6.
Eid, A. (2014). The impact of accounting information
systems (ais) on performance measures with value
relevance of auditors’communications. Internal
auditing and risk management, 2(34), 143-158.
Farrell, R. and Franco, R. (1999). The role of the auditor in
the prevention and detection of business fraud: SAS No.
82. Western Criminology Review, 2(1), 1-12.
Fayyad et al. (1996). From data mining to knowledge
discovery in databases. AI magazine, 17(3), 37.
Friendly, M. and Denis, J. (2001). Milestones in the history
of thematic cartography, statistical graphics, and data
visualization. URL http://www. datavis. ca/milestones,
32.
Gerety, M. and Lehn, K. (1997). The causes and
consequences of accounting fraud. Managerial and
Decision Economics, 587-599.
GOODE, S. and LACEY, D. (2011). Detecting complex
account fraud in the enterprise: The role of technical
and non-technical controls. Decision Support Systems,
50(4), 702-714.
Ittonen, K. (2010). A theoretical examination of the role of
auditing and the relevance of audit reports. University
of Vaasa, Finland.
Koornhof, C., & Du Plessis, D. (2000). Red flagging as an
indicator of financial statement fraud: the perspective
of investors and lenders. Research Journal of the
School of Accounting Sciences, 8(1), 69-93.
Kurt, T. (2004). An Introduction to Data Mining.
doi:http://www.thearling.com/text/dmwhite/dmwhite.h
tm
Miller, J. and Han, J. (2009). Geographic data mining and
knowledge discovery. CRC Press.
Nemati, R. and Barko, D. (2002). Enhancing enterprise
decisions through organizational data mining. Journal
of computer information systems, 42(4), 21-28.
doi:10.1080/08874417.2002.11647049
Palshikar, K. (2002). A critical application for business
intelligence.
RamaKalyani, K. and UmaDevi, D. (2012). Fraud detection
of credit card payment system by genetic algorithm.
International Journal of Scientific & Engineering
Research, 3(7), 1-6.
Salehi, M. (2011). Audit expectation gap: Concept, nature
and trace. African Journal of Business Management,
5(21), 8376-8392.
Sharma, A. and Panigrahi, K. (2013). A review of financial
accounting fraud detection based on data mining
techniques.
Fraud Detection: Using Of Data Mining In Accounting Information System
29