RESEARCH QUESTIONS 2
Problem Statement
The general problem to be addressed is the complexity and sophistication of fraudulent
activities resulting in challenges for organizations in detecting, preventing, and deterring fraud.
The Association of Certified Fraud Examiners, ACFE (2022) determined that the estimated costs
of complex and sophisticated fraudulent activities such as pharming costs organizations 5% of
revenues each year, projecting nearly $4.7 trillion in fraud loses globally. According to Hilal et
al. (2022), this scale of fraud can lead to significant global financial crises and bankruptcy of
companies if governments and business communities does not deploy a sense of urgency to
address the complexity and sophistication of fraudulent activities which continue to evade most
of the traditional methods used in fraud detection, prevention and detection. The emergence of
blockchain technology and use of cryptocurrency in the regular operations of organizations
presents a challenge for businesses navigating the course of complex fraudulent activities
(Kutera, 2023). In 2020, United States Small Business Administration’s Office of Inspector
General identified $78.1 billion in potentially fraudulent Economic Injury Disaster Loans (EIDL)
and grants paid to ineligible entities and another $6.9 billion in loans and grants linked to alleged
identity theft (Brewer, 2022). The specific potential problem to be addressed is the complexity
and sophistication of fraudulent activities within the financial industry resulting in challenges for
organizations in detecting, preventing, and deterring fraud in financial and technological-related
transactions.
Research Questions
Given the advancements of technology and the complexity, volume and frequency of
data and financial transactions, the traditional methods of fraud detection, prevention, and
deterrence are no longer sufficient which present an overwhelming feeling for auditors and fraud
RESEARCH QUESTIONS 3
investigators who do not have the right tools to execute their tasks (Tan et al., 2023; Mill et al.,
2023). Without the aid of artificial intelligence and machine learning which offer consistency in
predicting patterns and identifying fraudulent schemes, businesses and accounting professionals
are faced with financial losses which are detrimental to the economy (Guikema, 2020).
Researchers have explored the use of predictive models for timely fraud detection, prevention
and detection but further research is needed to assess the role artificial intelligence plays in
combating the challenges and complexities associated with fraudulent patterns that continues to
plague the business environment and various economies around the world. This researcher seeks
to gather information to better understand the current artificial intelligence and machine learning
models used in fraud detection, prevention and deterrence. This researcher attempts to answer
the following research questions:
RQ1. How do fraudsters leverage artificial intelligence and machine learning
technologies to perpetrate sophisticated fraudulent activities, and what challenges do
organizations face in keeping up with these evolving techniques?
RQ2. What are the emerging techniques and tools used by fraudsters to evade detection
and deceive organizations in the financial industry, and how do these pose challenges for
organizations in developing effective fraud detection strategies?
RQ3. How do regulatory frameworks and compliance requirements impact the ability of
organizations to detect and prevent fraudulent activities within the financial industry, and what
challenges do organizations face in navigating these complex regulatory landscapes while
effectively combating fraud?
RESEARCH QUESTIONS 4
References
Association of Certified Fraud Examiners (ACFE). (2022). Report to the nations: 2022
global on study on occupational fraud and abuse. https://acfepublic.s3.us-west-
2.amazonaws.com/2022+Report+to+the+Nations.pdf
Brewer, K. (2022). Bills extend statute of limitation for prosecuting PPP, EIDL fraud.
https://www.journalofaccountancy.com/news/2022/aug/bills-extend-statute-
limitation-prosecuting-ppp-eidl-fraud.html
Guikema, S. (2020). Artificial intelligence for natural hazards risk analysis: Potential,
challenges, and research needs. Risk Analysis, 40(6), 1117-1123.
https://doi.org/10.1111/risa.13476
Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection
techniques and recent advances.%Expert Systems with Applications,%193, 116429.
https://doi:10.1016/j.eswa.2021.116429
Kutera, M. (2023). Cryptocurrencies as a subject of financial fraud. Geographical, 95(1), 45+.
https://doi.org/10.7341/20221842
Mill, E., Garn, W., Ryman-Tubb, N., & Turner, C. (2023). Opportunities in real time fraud
detection: An explainable artificial intelligence (XAI) research agenda. International
Journal of Advanced Computer Science and Applications,
14(5)https://doi.org/10.14569/IJACSA.2023.01405121
Tan, E., Petit Jean, M., Simonofski, A., Tombal, T., Kleizen, B., Sabbe, M., Bechoux, L., &
Willem, P. (2023). Artificial intelligence and algorithmic decisions in fraud detection: An
interpretive structural model. Data & Policy, 5https://doi.org/10.1017/dap.2023.22
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