Fraud is a persistent and evolving risk that can undermine financial integrity, regulatory compliance, and organisational trust. As businesses grapple with sophisticated fraudulent tactics, integrating fraud detection into audits is no longer optional – it is a necessity. The challenge for auditors is twofold: ensuring compliance while proactively identifying and mitigating fraud risks before they escalate.
Effective fraud detection strategies leverage AI-driven analytics, robust internal controls, and a risk-based auditing approach. According to the Association of Certified Fraud Examiners (ACFE), organisations worldwide lose an estimated 5% of their revenue to fraud each year, highlighting the urgency of robust fraud detection mechanisms[1].
Strengthening fraud detection starts with mastering your control environment. Download our Mastering controls for risk management eBook to explore best practices in controls:
Assessing fraud risk: the foundation of prevention
A robust fraud risk assessment is the first step in integrating fraud detection into audits. Organisations must identify vulnerabilities in their financial and operational processes to establish proactive controls that deter fraud before it occurs.
Some of the key components of the process include:
- Understanding fraud drivers: The Fraud Triangle Theory (opportunity, incentive, rationalisation) helps auditors assess behavioural and systemic fraud risks
- Leveraging AI fraud detection software: AI-powered tools analyse vast datasets to detect anomalies and suspicious transactions, increasing audit efficiency
- Data analytics for fraud auditing: Pattern recognition, machine learning, and predictive modelling enhance fraud identification in audits
According to EY, a critical component of effective fraud detection is embedding forensic data analytics into risk assessments. EY says that "advanced data analytics techniques can help organisations proactively detect and prevent fraud by identifying patterns that traditional audit approaches may overlook"[2].
An example of effective fraud risk assessment is Visa’s investment of $12 billion in scam detection over five years. Visa’s dedicated fraud prevention team dismantled over $350 million in fraud in 2024, including removing 12,000 fraudulent merchant sites linked to background-check scams on dating apps[3].
AI and data analytics: revolutionising fraud detection
The integration of AI for fraud detection is transforming the audit landscape. Unlike traditional fraud detection methods, AI offers continuous monitoring, anomaly detection, and automated fraud identification – critical for organisations facing complex fraud schemes.
While AI offers advanced fraud detection capabilities, organisations must assess its cost-effectiveness compared to traditional methods. Key considerations include:
- Implementation costs: AI-based solutions often require significant upfront investment in software, integration, and training, while traditional manual audits rely more on existing human resources
- Detection accuracy: AI-driven fraud detection leverages machine learning to identify anomalies with greater precision than traditional methods, which rely on rule-based approaches and sampling
- Scalability and efficiency: AI continuously analyses large datasets in real time, reducing human workload and increasing fraud detection speed
- Regulatory compliance: AI solutions must be explainable and meet regulatory standards, whereas traditional methods rely on well-established audit procedures
The potential biases in AI models and the necessity for human oversight underscore the need for a balanced approach – combining AI analytics with auditor expertise. The Institute of Internal Auditors (IIA) underscores this need, stating that "internal auditors must ensure that AI-driven fraud detection solutions are transparent, explainable, and regularly validated to maintain integrity in financial oversight" (IIA).
Strengthening internal controls to combat fraud
Internal controls are the backbone of fraud prevention, ensuring that risks are mitigated before they escalate. A well-structured governance system enhances fraud identification while maintaining compliance.
Best practices for internal controls include:
- Segregation of duties: Prevents unauthorised financial actions by distributing responsibilities
- Continuous monitoring: Regular audits detect control weaknesses and evolving fraud tactics
- Fraud awareness training: Empowers employees to recognise and report fraudulent activities
A comprehensive internal control framework ensures that fraud auditors can detect, assess, and mitigate risks effectively. The ACFE emphasises that "effective internal controls significantly reduce the likelihood of fraud, but they must be continually updated to remain effective against evolving threats"[4].
Future trends: fraud detection maturity and enhanced regulation
As fraud tactics evolve, organisations must continuously enhance their fraud detection capabilities.
An important emerging trend in fraud auditing is the fraud detection maturity model, a framework for assessing fraud detection effectiveness and progressing towards advanced fraud analytics. Organisations can evaluate their fraud detection readiness using a structured maturity model, progressing through different levels:
- Reactive: Fraud detection occurs after incidents are discovered
- Basic monitoring: Limited rule-based fraud detection with manual reporting
- Data-driven: AI-assisted fraud analytics improve detection speed and accuracy
- Predictive and adaptive: AI continuously learns from fraud patterns, proactively preventing risks
- Fully integrated: Fraud detection is embedded into enterprise risk management, linking AI, auditors, and governance structures
Regulators worldwide are intensifying their scrutiny of fraud detection and risk management. Legislative updates, industry-specific compliance standards, and evolving international regulations are driving organisations to enhance fraud monitoring and reporting. For example, frameworks such as the EU’s Digital Operational Resilience Act (DORA) and the US Anti-Money Laundering Act are enforcing stricter fraud detection mandates, requiring businesses to adopt automated monitoring systems and real-time reporting capabilities.
At the same time, compliance is moving beyond mere rule-following to proactive risk management, where companies are expected to demonstrate continuous fraud monitoring, AI-driven analytics, and governance transparency. Failure to comply can lead to hefty fines, reputational damage, and even criminal liabilities for executives, making integrated fraud detection within audit programs a regulatory imperative rather than an optional control measure.
Conclusions and next steps for your organisation
Fraud detection is just one part of a broader audit management strategy that ensures governance, compliance, and risk mitigation. Organisations that implement structured audit processes, risk-based assessments, and integrated control frameworks can proactively identify vulnerabilities and strengthen financial oversight.
With Protecht ERM’s audit management capabilities, you can streamline audit planning, automate workflows, and centralise findings – all while maintaining a single source of truth for governance and compliance. From scheduling audits to tracking findings and driving action, Protecht ERM provides the tools to enhance audit efficiency and provide independent assurance of your enterprise risk framework.
See how Protecht ERM can elevate your audit management. Request a demo today to experience a smarter, more effective approach to audit oversight:
