Integrity by Design: A New Frontier in Fraud Detection

By Dr. Abram Walton & Taylor Mooney

Imagine lining up every employee in your organization. At one end are the bad actors – the estimated 5-10% who actively seek opportunities to exploit systems for personal gain. At the other end are the 5-10% who remain consistently incorruptible, regardless of circumstance. Between them sits the remaining 80% – the vast majority of employees who are generally well-intentioned, yet who have at some point made conscious decisions that crossed ethical or policy boundaries when pressure, opportunity, and rationalization aligned. Consistent with this reality, research shows that 75% of employees admit to having engaged in substantial workplace theft for personal gain at some point in their careers. Viewed through this lens, fraud is not merely a financial risk – it is a human one.

The central question for HR, then, is whether organizational systems are designed to simply presume integrity, or to proactively safeguard against moments when it falters.

The State of Reaction: What Traditional Fraud Controls Miss About Human Risk

Certified fraud examiners estimate that organizations lose approximately 5% of annual revenue to fraud, with a median loss of $145,000 per case and an average loss exceeding $1.7 million. While non-profits tend to experience lower losses, private companies, public organizations, and government entities face similar exposure, collectively costing organizations billions of dollars globally.

The 2024 Occupational Fraud Report found that the typical fraud scheme persists for approximately 12 months before detection, with formal investigations often beginning only after the misconduct has been confirmed. These extended timelines significantly amplify financial and organizational damage, along with the associated costs, and reflect a heavy reliance on reactive detection methods rather than proactive prevention. Notably, 43% of fraud is uncovered through tips from employees or other insiders, while internal audits account for 14% of detections and management reviews another 13%.

From Insight to Intervention: HR and the Human Side of Fraud Risk

Among organizational functions, we are uniquely positioned to help shift our organizations from a reactive posture to a proactive one. An estimated 84% of fraud perpetrators exhibit one or more behavioral red flags before misconduct occurs – early behavioral signals that often surface first in workforce data rather than financial records. Within the employee information we already collect lie early indicators that can accelerate detection and enable prevention.

By coordinating existing data, guiding the responsible application of AI-driven tools, and equipping leaders with a forensic mindset, HR can play a decisive role in strengthening organizational defenses, potentially reducing losses by a median of $150,000 per case (Bracco et al., 2024).

Where HR’s Human Data Makes the Difference:

Predictive risk analytics can help organizations identify fraud risk earlier by detecting patterns, anomalies, and behavioral trends across workforce data. When applied thoughtfully, machine learning models surface early behavioral signals that warrant attention, enabling intervention before misconduct escalates (Rippleshot, 2024). For HR, the value of these tools lies not in automation alone, but in augmentation – strengthening human judgment with timely, data-informed insight.

AI, however, is only as effective as the data and governance behind it. High-quality, unbiased data and ongoing oversight are essential to ensure accuracy and fairness. This is where HR plays a critical role: helping define appropriate use cases, steward employee data responsibly, and partnering with Finance, IT, and Compliance to ensure AI-enabled monitoring is transparent and ethical. When properly governed, these tools support continuous monitoring and earlier risk detection.

Importantly, these capabilities also lay the foundation for broader, cross-functional insight – once HR data is disciplined and governed, it can be responsibly combined with operational, financial, and technology data to reveal risk patterns that no single function could see on its own.

So where can HR most effectively apply these capabilities to detect and prevent fraud?

Data Theft and Turnover

Employee turnover presents a significant but often underestimated fraud risk, particularly when departures coincide with access to sensitive data or intellectual property. The 2023 Insider Risk Investigations Report found that data theft incidents increased by 35%, driven largely by employees leaving their organizations (Roessler, 2023). Research on employee fraud consistently identifies data misuse, credential abuse, and policy circumvention as common insider-driven risk vectors during periods of transition and disengagement (Catalán, 2025). While turnover is a normal feature of organizational life, unmanaged transitions can expose organizations to material data loss and downstream risk.

Common indicators of elevated turnover risk include declining productivity, disengagement, negative attitude shifts, reduced focus, and changes in work patterns – behaviors that may reflect a range of underlying conditions, from quality-of-work-life challenges to heightened risk exposure. These signals are typically captured through performance reviews, manager observations, and engagement feedback, placing HR in a strong position to recognize emerging risk without prematurely assigning intent.

When these indicators are evaluated collectively and in context, organizations gain the ability to apply more appropriate countermeasures – differentiating between situations that call for supportive interventions and those that warrant additional safeguards, and responding in ways that reduce risk without inadvertently creating it.

Payroll Fraud and Compensation Controls

Payroll manipulation remains a significant and persistent source of fraud risk, particularly where controls rely heavily on trust, routine compliance, or infrequent review. While safeguards such as segregation of duties, audits, and automated payroll systems are widely used, they are not foolproof. Gaps can emerge through overreliance on manual processes, limited analytics, insufficient training, or practices such as ghost employees – allowing errors or misconduct to persist undetected. Identifying where existing controls are strained or inconsistently applied is a necessary step toward strengthening payroll and compensation integrity.

When applied thoughtfully, AI-enabled analytics can support payroll accuracy and compliance by flagging anomalies, inconsistencies, or patterns that warrant human review. For HR, the value of these tools lies in reinforcing oversight rather than replacing judgment – supporting transaction validation, monitoring compliance with evolving wage and tax requirements, and prioritizing issues for appropriate follow-up (Payroll Plus CSRA, 2024). Used in this way, AI functions as a decision-support capability that reduces payroll-related risk while reinforcing accuracy, compliance, and employee confidence.

The Smaller Opportunities

Smaller, incremental discrepancies can accumulate into meaningful fraud risk when documentation systems lack consistency or visibility. Organizations manage growing volumes of digital contracts, evaluations, disciplinary records, and compliance artifacts, and even minor mismatches across these documents can weaken internal controls (Muller, 2024). While audits identify approximately 14% of fraud cases, documentation gaps and fragmented records can limit their effectiveness, increasing the likelihood that irregularities go undetected.

When applied thoughtfully, AI-enabled document analytics can support greater consistency and integrity across HR records by identifying anomalies, mismatches, or patterns that warrant review (Wolters Kluwer, 2024). For HR, the value lies not in automating judgment, but in strengthening it – improving visibility across complex documentation, reducing blind spots, and enabling earlier, context-aware review. Used in this way, these tools help surface low-signal risks before they compound, supporting more reliable and defensible HR forensic analysis.

Next Steps: Building a Fraud-Resistant HR Strategy

Building a fraud-resistant organization begins with adopting a forensic mindset – one that prioritizes curiosity, context, and disciplined judgment over assumption or reaction. For HR, this mindset can be operationalized through four core practices:

1. Observe – Slow down and look for changes, gaps, or unusual patterns in behavior, processes, or data.
2. Question – Ask what may be driving those patterns, resisting the urge to assign intent prematurely.
3. Compare – Examine related data sets and trends to distinguish coincidence from meaningful signal.
4. Validate – Determine whether observed patterns reflect benign explanations, systemic issues, or elevated risk requiring action.

Applied consistently, this mindset enables HR to strengthen fraud prevention efforts by improving data quality, revealing process gaps, and guiding the responsible use of AI-enabled tools. Rather than treating analytics as standalone solutions, HR can deploy them as decision-support capabilities across predictive risk analysis, turnover and red-flag monitoring, forensic auditing, payroll oversight, and investigative prioritization – reducing risk while preserving fairness, accuracy, and trust.

In Conclusion

Fraud will never be eliminated entirely, because no organization is immune to human complexity, nor should one attempt to be. The goal is not perfect control, but better design. By shifting from reactive detection to proactive prevention, and by equipping HR with the data, tools, and forensic mindset needed to interpret human risk responsibly, organizations can meaningfully reduce the conditions under which fraud takes hold.

Critically, this work requires judgment as much as technology. Signals must be interpreted in context, countermeasures must be proportionate, and quality-of-work-life factors must be distinguished from true risk indicators to avoid creating the very outcomes organizations seek to prevent. When HR leads with disciplined data stewardship and governed use of AI, it lays the foundation not only for stronger internal controls, but for future cross-functional insight that no single function could achieve alone. In this model, integrity is no longer assumed or enforced after the fact – it is deliberately designed into the systems that shape how organizations work.

Resources

Amazing Workplace. (2023, April 17). The turnover warning signs. https://www.amazingworkplace.com/resources/article/the-turnover-warning-signs/

Association of Certified Fraud Examiners. (2024). Occupational Fraud 2024: A Report to the Nations. https://www.acfe.com/-/media/files/acfe/pdfs/rttn/2024/2024-report-to-the-nations.pdf

Bracco, A. M., Beckman, D., Kolb, M., & Sanvidge, B. (2024). Occupational Fraud 2024: A report to the nations. https://www.anchin.com/wp-content/uploads/2024/08/2024-ACFE-Occupational-Fraud-Report.pdf

Catalán, C. (2025, February 22). 18 types of employee fraud & how to prevent them. https://www.teramind.co/blog/types-of-employee-fraud/

Muller, D. (2024, October 25). Why workplace documentation matters: Best practices HR teams need in 2025. https://www.hracuity.com/blog/workplace-documentation-best-practices/

Payroll Plus CSRA. (2024, July 2). How AI is Enhancing Payroll Accuracy and Reducing Errors. https://payrollpluscsra.com/how-ai-is-enhancing-payroll-accuracy-and-reducing-errors/

Rippleshot. (2024, November 8). Predictive fraud analytics: The key to proactive risk management. https://www.rippleshot.com/post/predictive-fraud-analytics-the-key-to-proactive-risk-management

Roessler, K. (2023). 2023 insider risk investigations report: The rise of employee attrition and data exfiltration. https://www.dtexsystems.com/blog/2023-insider-risk-investigations-report-the-rise-of-employee-attrition-and-data-exfiltration/

Wolters Kluwer. (2024, April 17). Artificial intelligence in auditing: Enhancing the audit lifecycle. http://www.wolterskluwer.com/en/expert-insights/artificial-intelligence-auditing-enhancing-audit-lifecycle