AI and fraud risk management in financial processes: Rethinking internal controls

Accurate financial reporting relies on effective oversight, accountability, and controls that can adapt as risks change. Fraud remains one of the most significant threats to financial integrity, exposing organizations to financial loss, regulatory scrutiny, and reputational damage.

At its core, fraud is an intentional act designed to obtain an unfair or illegal advantage through deception. It often occurs when pressure, opportunity, and rationalization converge. While Internal Controls over Financial Reporting (ICFR) are designed to reduce the risk of material misstatement, the increasing speed and sophistication of fraud require organizations to continuously evaluate whether their control environments remain effective.

Although public companies face specific reporting obligations, strong financial controls are critical for organizations of all sizes. Smaller and privately held organizations often operate with fewer resources, making them equally vulnerable to operational disruption and financial loss.

As a result, leadership teams must balance growth, innovation, and risk management while maintaining an internal control environment capable of preventing, detecting, and responding to emerging threats.

How AI is Changing Fraud

Perhaps one of the most significant implications of AI adoption is its impact on accounting and financial reporting processes. AI is rapidly increasing the scale, speed, and sophistication of fraud across financial processes. Rather than replacing traditional fraud schemes, it enhances them by making attacks more convincing, expanding their reach, and accelerating execution.

According to Feedzai's 2025 AI Trends in Fraud and Financial Crime Prevention report, more than half of fraud activity now involves AI, including deepfakes, synthetic identities, and AI-enabled phishing attacks.

These schemes are also reaching further into day-to-day operations. As organizations adopt AI within their own processes, they should assess the opportunities and risks it presents alongside overall organizational readiness. Historical controls were designed for human-driven activities and may not be sufficient to address the speed, scale, and complexity of AI-enabled environments.

Rethinking Controls for an AI Environment

As AI becomes more embedded in business processes, organizations need to move toward more proactive, data-driven, and scalable risk management. A strong AI control environment is built on traceability and transparency. When organizations can clearly understand how AI-supported decisions were made, what data was used, who was responsible, and what controls governed the process, they strengthen oversight, accountability, and trust.

In response, organizations are enhancing fraud prevention and detection through:

• Continuous transaction monitoring
• Stronger identity verification processes
• AI model governance and validation

Modern fraud risk management requires integrated, intelligence-driven monitoring capabilities.

Leading organizations are leveraging AI to strengthen fraud detection and internal controls by identifying unusual transactions and behaviors, monitoring high-risk activities across financial processes, detecting emerging fraud patterns, analyzing structured and unstructured data for risk indicators, and prioritizing higher-risk activities for investigation and review.

It is important to understand that technology alone does not manage risk. These capabilities hold only when someone owns them, challenges them, and independently validates that they are working as intended.

Governance and Fraud Risk Ownership

Effective fraud risk management begins with clear ownership, independent oversight, and accountability. Organizations are best positioned to manage fraud when risks are addressed at the point of activity, challenged through independent review, and monitored through an objective assurance process.

Strong governance frameworks share several core characteristics:

• Business-owned controls: Operational leaders are responsible for executing controls, identifying risks, and escalating concerns before they impact financial reporting or customer trust.
• Independent oversight and guidance: Risk and compliance functions establish expectations, monitor threats, and evaluate whether controls remain effective as fraud risks evolve.
• Objective assurance and accountability: Independent reviews validate control performance, identify gaps, and provide leadership with visibility into areas requiring attention or remediation.

Preventive, Detective, Directive, and Corrective Controls

Ownership becomes visible in the controls an organization operates. Well-designed controls reduce fraud risk by strengthening oversight, accountability, and transparency across business processes. They help organizations prevent, detect, and respond to issues while improving the accuracy, reliability, and security of operations.

A strong internal control framework helps organizations reduce losses, maintain compliance, and build trust. Effective programs rely on multiple control types working together to prevent, detect, and respond to fraud risks. Examples of these control types include:

Preventive controls (stop issues before they occur):

• System access controls (role-based permissions, MFA)
• Dual authorization for wire transfers, ACH releases, and account changes
• Segregation of duties in payment processing
• Pre-approval workflows for high-risk activities

Detective controls (identify issues after they occur):

• Continuous transaction monitoring and real-time fraud alerts
• Monitoring for unusual volume, velocity, or customer behavior
• Exception reports and reconciliations to surface discrepancies
• Audit trails and activity logs to support rapid investigation

Directive controls (guide behavior and decision-making):

• Fraud risk policies and procedures that define accountability
• Code of conduct and ethics guidelines to support ethical decisions
• Required training on fraud awareness and controls
• Speak-up culture supported by whistleblower and escalation channels

Corrective controls (address and fix issues once identified):

• Incident response and investigation processes
• Account suspension, transaction reversal, or access termination protocols
• Root cause analysis to identify control breakdowns
• Remediation plans with tracking and follow-up

Fraud risk will continue to evolve alongside the technology that enables it. Organizations that build governance in from the beginning, rather than retrofitting it after adoption, are more likely to manage risk effectively, maintain accountability, and support responsible AI adoption amid expanding use cases and growing third-party dependencies.

Approached this way, governance is not a constraint on innovation. It is what gives leadership confidence in its numbers, withstands the scrutiny of regulators and auditors, and earns the trust of the customers and stakeholders who depend on it.

We Can Help

Elliott Davis helps organizations assess, design, and enhance internal controls, AI governance programs, and fraud risk management capabilities to support compliance, strengthen oversight, and build stakeholder trust.

Interested in learning more about AI Governance? Watch the replay of our AI Governance Webinar to explore practical strategies for governing AI, managing emerging risks, and implementing controls that enable responsible and sustainable adoption.

Contact us today to schedule a fraud and internal controls readiness consultation.