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Reducing Accounting Frauds Risks within Fintech Organisations
Introduction
Financial technology (Fintech) entails new technology which aims to automate and enhance the delivery and utilisation of financial services and is used by companies, business owners, or individuals to improve processes (Ng and Kwok, 2017). Besides its perceived benefits, the technology has also faced possible risks of accounting frauds (Ryu, 2018). Therefore, the research will focus on evaluating the response of different Fintech organisations in cushioning their firms from the risks of accounting frauds.
Research Aims
To quantify the risks of accounting frauds in financial technology organisations
To examine the measures undertaken by various financial technology organisations to prevent the risks of accounting frauds
Research Question
What strategies have financial technology organisations adopted to reduce risks of accounting frauds?
Literature and Rationale for Research
With the rise of financial technology organisations, the imposed risks have also sharply increased, presenting numerous risk factors for accounting frauds (Van, 2018). Therefore, the research seeks to examine deeply the topic in the context of how Fintech organisations mitigate risks of accounting frauds. Organisations recognise that the consequences of such frauds can be detrimental to various shareholders. Therefore, various measures have been undertaken to shield the organisations from these risks (Kagan, 2018). Bates (2017) echoes that most financial technology organisations reduce risks of accounting frauds by implementing internal controls that safeguard the company’s assets and maintain the integrity of all accounting records by ensuring privacy and preventing any unauthorised access (Magnuson, 2018).
Similarly, fraud-detection methods are used by several financial technology organisations as a prevention strategy, as stated by Bates (2017). Das (2019) asserts that when tools that can detect accounting frauds are used in organisations and made visible to employees, they act as suitable deterrents to any fraudulent behaviour. However, Giudici (2018) argues that critical consideration is to ensure that these systems are updated and monitored frequently so that the plan can improve the prevention controls.
Mention (2019) adds that other organisations uphold reliable hiring practices to ensure that they hire only trustworthy experts. According to Ryu (2018), having a competent team of certified public accountants and certified fraud examiners is essential as they can establish anti-fraud policies for maintaining accountability. Thus, Van (2018) states that when hiring people who handle all accounting matters, the organisations ensure that individuals have excellent reputations founded on quality.
Ng and Kwok, (2017) highlight another solution that is the increased development of machine-learning approaches to prevent accounting frauds through the use of artificial intelligence. Through machine learning, the computer systems can identify schemes that are likely to be fraudulent by evaluating previous data and then finalising decisions relative to the on-going transactions and accounting procedures, as illustrated by Kagan (2018). The goal is to create a system that can assist computers in recognising other potential risk-imposing transactions that can result in accounting frauds (Lemma, 2020).
Methodology
The primary approach to data collection for the research will be through interviews conducted among fifty individuals representing different financial technology organisations. The interviews will be structured as sets of open-ended questions for people to portray their perceptions on how Fintech organisations have cushioned risks of accounting frauds within the firms. The advantage of interviews is the ease of administration, cost-effectiveness, and ability to collect diverse and comprehensive information (McGrath et al., 2019). In this case, they will be administered via online platforms. However, with the vast amount of data likely to be collected, transcription and analysis may be tedious (Bryman, 2016). The data will be analysed using a coding or thematic analysis approach to highlight consistent themes and identify the common strategies that Fintech organisations use to mitigate the risks of accounting frauds (McGrath et al., 2019).
The relevant ethical issues that are likely to arise during the study include the need to add the three most crucial ethical practices of research namely anonymity, confidentiality, and consent of all participants (Yip, Han, and Sng, 2016) and the provided information, and they will be upheld throughout the study.
References
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Bates, R., 2017. Banking on The Future: An Exploration of Fintech and the Consumer Interest.
Board, F.S., 2017. Financial stability implications from fintech: Supervisory and regulatory issues that merit authorities’ attention. June, Basel.
Bryman, A., 2016. Social research methods. Oxford University Press.
Cyfar, 2019. Collecting Data. Retrieved from https://cyfar.org/collecting-data
Das, S.R., 2019. The future of fintech. Financial Management, 48(4), pp.981-1007.
Fox, N., 2013. Using interviews in a research project. The NIHR RDS for the East Midlands/Yorkshire & the Humber.
Giudici, P., 2018. Fintech risk management: A research challenge for artificial intelligence in finance. Frontiers in Artificial Intelligence, 1, p.1.
Huang, M. K., 2017. Fraud prevention and detection – focus on the technological trend. Retrieved from https://www.financierworldwide.com/fraud-prevention-and-detection-focus-on-the-technological-trend#.Xr5_0GVR3IU
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Mention, A.L., 2019. The future of fintech.
Ng, A.W. and Kwok, B.K., 2017. Emergence of Fintech and cybersecurity in a global financial centre. Journal of Financial Regulation and Compliance.
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Ryu, H.S., 2018, January. Understanding benefit and risk framework of fintech adoption: Comparison of early adopters and late adopters. In Proceedings of the 51st Hawaii International Conference on System Sciences.
Vanclay, F., Baines, J.T. and Taylor, C.N., 2013. Principles for ethical research involving humans: ethical professional practice in impact assessment Part I. Impact Assessment and Project Appraisal, 31(4), pp.243-253.
Van Loo, R., 2018. Making innovation more competitive: the case of fintech. UCLA L. Rev., 65, p.232.
Yip, C., Han, N.L.R. and Sng, B.L., 2016. Legal and ethical issues in research. Indian journal of anaesthesia, 60(9), p.684.
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