How auditors can help detect fraud and reduce fraud risks

As we know, the auditor is responsible for obtaining reasonable assurance in an audit that the financial statements are free of material misstatements, whether due to fraud or error. AU-C Section 240, Consideration of Fraud in a Financial Statement Audit, addresses the auditor’s responsibilities relating to fraud in an audit of financial statements. Management’s responsibilities include the prevention and detection of fraud, as well as the design, implementation, and maintenance of a system of internal control relevant to the preparation and presentation of financial statements that are free from material misstatement, whether due to fraud or error unless the accountant decides to accept responsibility for such internal control.

We dedicate our literature review to the second category, namely fraud detection, and focus specifically on experiments on fraud detection by an external auditor to close a gap, because prior literature reviews do not address experiments. Dong et al. (2018) discuss financial fraud and fraud detection in the context of accounting, finance, and information systems. One of the main findings is that auditors who use long lists of fraud cues and fraud checklists are inaccurate in their fraud-risk assessments. Effective corporate governance, including the board of directors, the audit committee, internal controls, and the external auditor, plays a key role in reducing the potential to commit fraud.

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Our findings underscore the effectiveness of sanctions and the crucial role of robust internal control. The literature also demonstrates that the LVA software, utilizing vocal dissonance markers, effectively distinguishes between truth and fraud. Taking the perspective of the client or client’s staff elevates attention to fraud motivation. Team brainstorming results in a superior quality of fraud suspicions, whereas individual brainstorming excels in terms of the number how to detect fraud during audit of fraud ideas generated. Regarding RQ1, our review identifies several highly effective methods for detecting fraud. Hogan et al. (2008) and Trompeter et al. (2013) fall into the first category of fraud models and prevention.

This is generally used to detect asset misappropriation and management fraud (Coderre and Warner 1999; Christensen and Byington 2003; Kenyon 2009). Furthermore, we exclude experiments about the auditor-auditee problem (5), as a discussion of their findings requires a detailed description of the underlying analytical model of the auditor-auditee interaction, in order to fully understand what they check experimentally. Section 3 presents an overview of fraud theory and widely used fraud-detection methods.

After significant regulatory changes, financial crises, and the COVID-19 pandemic, publications increase in the following years. Nevertheless, we followed the advice of Hardies et al. (2024) and Siddaway et al. (2019) and search the SSRN database for recent (last three years) working papers that align with our search string. First, we define our research questions, and afterwards, decide upon the data-collection method. http://en.cosmoeng21.com/which-of-the-following-accounts-typically-has-a/ The following section describes the methodology of our systematic literature review.

The scope of forensic accounting extends beyond traditional financial analysis. Their primary objective is to uncover financial misconduct, gather evidence and support legal proceedings. Services that fall under this specialization include compilations, reviews, audits and agreed-upon-procedures engagements. While both involve the analysis and reporting of financial information, they differ in many ways. Auditors also promote ethical behavior within organizations.

Inquiries Required by Audit Standards

These procedures can assist operators to improve the detection of intrusions against Cyber Physical Systems (CPSs) and cloud resources at large. Basically, empirical studies on smart IDSs in the context of Cyber Physical Systems (CPSs) involve pragmatic examinations of specific experiments conducted with smart IDSs to concurrently correct security concerns and audit issues. Thus, smart IDSs are IDSs that are configured such that operators can receive and respond to their alerts through Short Message Services (SMS) to the GSM or email addresses of the operators of IDSs in Cyber Physical Systems (CPSs). In other words, Figure 1 demonstrates one of the two approaches organizations can adopt to position Network Intrusion Detection System (NIDS) in relation to firewall within the peripherals and gateways that connect them to the entire Cyber Physical Systems (CPSs) 7, 8. The impacts of some successful cyber attacks in this domain may corrupt or damage Cyber Physical data 2, 7. This chapter uses alerts from Snort and C++ programming language to practically explore the above issues and further proposes a feasible model for operators and researchers to lessen the above problems.

• As AGA notes, “To fight fraud one must not only realize that it occurs, but also how and why it occurs.”
• Specifically, when auditors are presented with information indicating that the client manager benefits from the misstatement, which implies a high fraud risk, those who consider the manager’s perspective assess the manager’s incentive for fraud significantly higher than those who do not.
• Auditors increase their skepticism when data presentation is combined with an inquiry about it (Lee and Welker 2007).
• The future of AI fraud detection is privacy-preserving, scalable, and audit-driven.
• However, auditors need new IT skills and technical knowledge to design efficient and effective audit processes to review the system for potential risks and frauds (Garanina et al. 2022; Mugwira 2022; Hongdan Han et al. 2023; Parmoodeh et al. 2023).
• Auditors should consider the significance of the identified fraud risks and the organization’s risk appetite in assessing fraud risks.
• The document must categorically state allowable length of time to train supervised learning algorithms as well as the acceptable level that log analyzers must reduce workload due to IDS alerts in other to undermine the generality of intrusions IDSs have warned.

6 Research and audit issues with log analyzers in Cyber Physical Systems

Limited financial budget (46%), competing organizational priorities (43%), and insufficient time (43%) to dedicate to AI-specific risk management efforts also pose significant challenges for audit functions. Other common ways that audit functions are taking action include supporting awareness or training initiatives (40%); testing or strengthening fraud prevention and detection (38%); providing fraud risk assessments to leadership (31%); and investigating and documenting AI’s role in fraud incidents (26%). As adoption accelerates, internal audit has a critical role to play in helping organizations understand these risks, identify emerging threats, and respond effectively. LAKE MARY, Fla. (February 17, 2026) – A new joint report from The Internal Audit Foundation and AuditBoard reveals that, while internal audit leaders widely recognize artificial intelligence–enabled fraud as a growing organizational risk, only four in ten believe their functions are adequately prepared to detect or respond to it. Specifically, management will represent that it has notified the auditor of any known significant facts relating to actual or suspected fraud and any allegations of known or suspected fraud that may have affected the entity’s financial statements. When performing a preparation engagement, where the objective is to prepare financial statements pursuant to a specified financial reporting framework, the auditor’s main responsibility is the requirement to obtain management’s agreement that it acknowledges and understands its responsibility for preventing and detecting fraud.

Transparency in AI models, decision support systems, and linguistic analysis of unstructured data sources could also enhance risk assessment comprehensiveness. Additionally, future research could investigate auditors’ trust dynamics, examine personality traits influencing trust, and explore behavioral analysis to interpret non-verbal cues for detecting deceptive behavior. Team dynamics, virtual collaboration practices, and interdisciplinary training programs could optimize creativity during brainstorming sessions, thereby increasing fraud detection. Integrating behavioral factors, decision heuristics, and social influences can enhance fraud detection. They also validate the utility of existing regulations, such as the mandated brainstorming sessions and decomposition of fraud risk assessment in line with the fraud triangle. Accountability increases perceived responsibility for fraud detection, and accountability to superiors results in more conservative judgments.

Audit teams get better results when they adjust the fraud triangle for the landscape in front of them. Traditional controls (like manual approvals) lose power in the face of well-equipped attackers. Someone may justify their actions by blaming weak security or telling themselves that large organizations “won’t miss it.” Capability becomes the gatekeeper — fraudsters might need specific software, credentials, or insider knowledge to exploit a control. In cyber fraud, motivation isn’t always about money. Instead of only looking for pressure and opportunity, audit teams must consider technical skill and intent.

By doing so, organizations can provide assurance to stakeholders that their financial reports are reliable and accurate. Fraud detection is a crucial step in ensuring the integrity of financial reporting. Fraud detection also requires specialized skills and knowledge, which may not be readily available within an organization. They can use technology to conceal their activities and make it difficult to detect fraud.

• The review should cover all the IDSs in the organization together with infrastructure in the organization that relates to them, logical access and physical security of each smart IDS.
• This element differs from strengthening internal controls in that the point of contact for concerns need not absolutely be someone tasked with receiving them by policy.
• In the intricate world of auditing, the importance of fraud detection cannot be overstated.
• “While the awareness of AI-enabled fraud is high, the ‘readiness gap’ remains a significant vulnerability for most organizations,” said Richard Chambers, Senior Advisor, Risk and Audit at AuditBoard.
• The manager knows if concerns are voiced, the controls are so weak that little will come of complaint(s)-the manager also already has a script in place based on plausible deniability.

The Internal Audit Foundation is the preeminent global resource, in strategic partnership with The IIA, dedicated to elevating and empowering the internal audit profession by developing cutting-edge research and programs. Established in 1941, The IIA is recognized throughout the world as the internal audit profession’s leader in standards, certifications, education, research, and technical guidance. The report also highlights the growing implementation of AI within internal audit functions. Importantly, the report highlights the key actions that internal audit leaders see as most critical to enhancing readiness. “While the awareness of AI-enabled fraud is high, the ‘readiness gap’ remains a significant vulnerability for most organizations,” said Richard Chambers, Senior Advisor, Risk and Audit at AuditBoard. Across the board, AI-powered phishing attacks are the most-cited concern for audit leaders, with 88% of respondents identifying them      as a top risk.

Understanding the Different Forms of Fraudulent Activities

To ensure the integrity of financial reporting, organizations should implement best practices in fraud detection. Risk assessment involves identifying and evaluating potential fraud risks and implementing controls to mitigate those risks. There are various fraud detection techniques that can be used to identify potential fraudulent activities. Automated controls, on the other hand, rely on technology to detect and prevent fraudulent activities. Fraud detection software can analyze large data sets and identify fraudulent activities based on predefined rules and patterns.

Interview-Level Integrity Analysis

The fraud triangle’s basics still hold up, but the model doesn’t catch everything in today’s complex environments. By using the fraud triangle as your lens, you don’t chase ghosts. Perceived opportunity appears when controls get loose or duties aren’t separated. You see the elements of the fraud triangle every day. For fraud to occur, these three forces typically work together, creating the conditions where someone might cross ethical boundaries.

Behavioral analysis, informed by psychological research (Luyben 2009), could enable auditors to interpret non-verbal cues and detect subtle signs of deceptive behavior. Including psychological research on trust (Simpson 2007) could shed light on the behavior of auditors in interviews. Instructed auditors achieve 72% accuracy for non-fraud companies and improve the accuracy for fraud companies to 70%. Lee and Welker (2022) examine whether auditors are able to detect fraud in email messages and what language-based factors increase auditor skepticism.

His background includes HCC auditing for CMS, coding and auditing for a large global healthcare network, and serving as a compliance educator and speaker for AIHC. They are as outraged by fraud or someone on their team manipulating information. The vast majority of healthcare managers are ethical, hard-working people who care about their organization both downward and upward. This only happens when a top-down culture of compliance has been instilled within the organization and demonstrated by the items listed below.

The next step is reading the titles and abstracts of these articles to identify those experimental studies that addressed fraud detection in the context of an audit. Regulators may benefit from our literature review, which can assist them in evaluating and updating existing, and developing complementary auditing standards related to fraud detection. Overall, prior research shows that digitalization is becoming increasingly important to fraud detection, as data analysis and new methods, such as blockchain and AI, become more relevant (RQ1 see, Sect. 4.1). The fraud literature in auditing and accounting can be classified into two broad categories, namely fraud models and prevention on the one hand, and fraud detection on the other hand. AU-C Section 240 (AICPA 2021a) addresses the auditor’s responsibilities relating to fraud in an audit of financial statements. According to these clarified statements on auditing standards (AU-C) and the Auditing Standards (AS) from the PCAOB, an auditor must obtain reasonable assurance as to whether the financial statements as a whole are free from material misstatement, whether due to fraud or error.

Studies advise that skilled intruders are common threats that are extremely disturbing corporate and private users of computer systems in Cyber Physical systems (CPSs) 2, 7, 10. Nonetheless, there are numerous challenges with research on audit of smart IDSs in corporate setting in the past years . The evaluation and the reports of this kind of audit can go a long way to determine the level of compliance and operations of all intrusion detectors in the company with best global practices. These developments have led to the need to audit smart Intrusion Detection Systems (IDSs) to improve their efficacies. The rationale is that operators should be able to remotely analyze intrusion logs and counter attacks on Cyber Physical Systems without the need to physically report to their offices. A few numbers of researches has suggested that, the above devices should be upgraded so that they can intimate operators with alerts on real-time basis 11, 20.

It is expected to reflect the approved connectivity between log analyzers and logs of smart IDSs. This document reflects and states how all IDSs in the organization are implemented and managed. New waves of stealthy attacks can shutdown IDSs; enable triggers and disable or re-start the back-end databases of the detectors. The above components also enable analysts to visualize and analyze alerts on web interface 8, 10. Using Snort as an example 7, 10, this premises that components such as Apache, Pretty Home Page (PHP), WinPcap and Analysis Console for Intrusion Databases (ACID) must be audited to ascertain their levels of compliance to best industrial practice .

There are several reasons why such frauds are not detected. The SEC’s enforcement was issued to safeguard investors in the US markets, and the SEC ensured that the public could trust a company’s financial numbers. The Chinese http://mongolx.com/3-things-bookkeepers-do-for-small-businesses/ affiliate of Deloitte was fined $20 million because it let some of its clients conduct their auditing. As a result, the company portrays a worse financial state than it is in.