Harnessing AI in Fraud Prevention and Detection

According to the Association of Certified Fraud Examiners (ACFE) 2024 Report to the Nations, the average organization loses 5% of its annual revenue due to fraud. The total loss caused by the cases included in the report exceeded $3.1 billion, with an average loss per case of $1.7 million. Bad actors are leveraging advanced technologies, including artificial intelligence (AI) and machine learning, to commit fraud. While advancements in AI make fraud detection more challenging, they also open new avenues for using technology in fraud prevention and detection.

The Latest Trends in Fraud: How AI Is Shaping the Landscape

The IIA Internal Audit Foundation Risk in Focus 2025 Report found that the fastest-growing risks regionally and worldwide in the next three years include climate change and digital disruption, including AI—and this concern is only expected to grow. AI connects to many risk areas worldwide, including cybersecurity, human capital, and fraud. While organizations are driven to adopt AI to remain competitive, governance and transparency are key to the effective use of AI and other digitally disruptive technologies.

The Role of Generative AI in Fraud

Generative AI refers to a group of artificial intelligence techniques used to create new content, such as images, text, music, and videos, that mimic or are inspired by existing data. Generative AI models learn patterns and structures from a dataset and generate new data that follow similar patterns.

AI is adding to the complexity of fraud. While organizations have more sophisticated AI-powered tools for detection, bad actors also have more advanced tools and can create threats at an increasing speed and volume. For example, deepfakes, synthetic identity creation, and fake reviews or social proof are becoming more prevalent. Using generative AI, a fraudster can create a fake identity with a legitimate-looking credit history and apply for loans that will never be repaid, disappearing before detection. AI has become so sophisticated that we can no longer reliably detect deepfakes.

Implementing AI Solutions: Best Practices for Effective Fraud Detection

There are varying approaches to balancing risk and opportunity concerning AI and fraud. Controls associated with digital transformation, such as adequate data security, data compliance, patch management, and software security, are generally understood. However, strategies and policies for using and managing AI are often inconsistent, uncoordinated, or, in some cases, nonexistent.

Key Fraud Trends and Strategies to Address

Below are five major fraud trends within the 2024 ACFE Report to the Nations and strategies to consider to address them.

1. The most prominent organizational weakness is the lack of internal controls.

• Action: Implement strong internal controls. Regular and surprise audits, segregation of duties, and robust authorization and approval processes can help mitigate risks.

2. The median duration of fraud schemes was 12 months.

• Action: Enhance real-time fraud monitoring and leverage data analysis techniques to identify anomalies and patterns that may indicate fraudulent activity.

3. Tips were the most common initial detection method.

• Action: Promote a whistleblower culture that encourages employees to report suspicious activities without fear of retaliation.

4. Anti-fraud controls correlated with lower losses.

• Action: Invest in proactive fraud controls.

5. Anti-fraud controls correlated with quicker detection.

• Action: Deploy analytics and AI for faster fraud detection and response.

Challenges and Opportunities in AI-Driven Fraud Detection

The introduction of AI doesn’t completely change how we prevent and detect fraud. Key prevention and detection principles remain, but advancements in AI are introducing powerful tools. AI can process and analyze much faster than humans, enabling faster fraud detection and response. AI systems identify subtle patterns and anomalies that could be missed by traditional methods, making them more precise.

Organizations are using a multitude of tools to detect and prevent fraud, such as:

• Anomaly detection
• Behavioral analysis
• Identity verification
• Automated risk scoring
• Real-time monitoring
• Machine learning models
• Natural language processing (NLP)

Still, organizations are facing challenges in implementing AI for fraud detection and prevention within their businesses, including:

• Resource constraints
• Integration with existing systems
• Bias in models
• Accuracy of output
• Regulatory compliance
• Ethical considerations

Leveraging AI for Enhanced Fraud Prevention and Detection Strategies

Case studies and lessons learned emphasize the importance of a proactive approach to fraud prevention and the need for continuous monitoring. To learn more about how your organization can enhance its anti-fraud program or utilize AI in fraud detection and prevention, please contact us using the form below.