Shieldbase
Sep 14, 2024
Global Survey Highlights Enthusiasm for GenAI but Warns of Challenges
A global survey by the Association of Certified Fraud Examiners (ACFE) and SAS has revealed that 83% of anti-fraud professionals expect to deploy generative AI (GenAI) technology within the next two years. While the excitement surrounding GenAI's potential is undeniable, historical data from previous studies suggests that real-world adoption may face significant hurdles.
The 2024 Anti-Fraud Technology Benchmarking Report, the third in a series that began in 2019, surveyed nearly 1,200 ACFE members. It provides an in-depth look at the evolving trends in fraud prevention technologies.
Key Findings: AI and Machine Learning Adoption
The report reveals a surge in interest in artificial intelligence (AI) and machine learning (ML) within fraud detection. Currently, 18% of anti-fraud professionals use AI/ML tools, and another 32% plan to adopt these technologies by 2025. This projected growth suggests AI/ML use in fraud detection could triple within the next two years.
However, despite the rising interest, the actual adoption rates of AI and ML technologies have lagged behind expectations. Since 2019, AI/ML use has grown by just 5%, far below the anticipated rates from earlier surveys. In 2019 and 2022, adoption rates were forecasted at 25% and 26%, respectively.
Emerging Technologies: Biometrics and Robotics
While the adoption of traditional data analysis methods appears to have leveled off, the use of biometrics and robotics in anti-fraud programs continues to grow. Physical biometric tools have seen a 14% increase since 2019, with 40% of respondents now using these systems. Robotic process automation (RPA) has also gained traction, with 20% of respondents implementing robotics in their operations, up from 9% in 2019.
These technologies are particularly prevalent in the banking and financial services sectors. Half of respondents in these industries use physical biometrics, and one-third have incorporated robotics into their fraud prevention strategies.
The Challenge of Generative AI in Fraud Prevention
While the possibilities of generative AI are exciting, experts warn of the risks it poses if misused. ACFE President John Gill emphasized the ethical challenges organizations face in deploying these technologies. "Unlike fraudsters, organizations must use AI tools responsibly," Gill said, highlighting the importance of balancing technological advancements with ethical considerations.
The growing interest in advanced analytics contrasts with slower-than-expected adoption rates, pointing to the complexities of scaling AI solutions. SAS Senior Vice President Stu Bradley stressed the importance of selecting the right technology partner. “AI and machine learning are not plug-and-play solutions,” Bradley noted. Modular, AI-powered platforms, like SAS Viya, can help organizations deploy these tools more effectively.
Industry-Specific Insights and Global Trends
The survey gathered responses from professionals across 23 industries, including banking, financial services, and government sectors, each accounting for 22% of respondents. Other sectors represented include professional services, insurance, healthcare, technology, and education. Companies ranged from small organizations with fewer than 100 employees to global enterprises with more than 10,000 workers.
SAS has created an online data dashboard where users can explore the survey results by industry, geography, and company size. Key areas of focus include:
Techniques organizations use to detect and prevent fraud.
How organizations apply data analytics to fraud risk management.
Sources of data used in anti-fraud initiatives and perspectives on data-sharing consortiums.
The use of case management and digital forensics tools.
The challenges of implementing new anti-fraud technologies.
The role of generative AI in enhancing fraud detection efforts.
The Future of Generative AI: Promise or Peril?
Generative AI holds the potential to revolutionize fraud prevention by rapidly identifying anomalies and patterns in massive datasets. Yet, the question remains: Will the real-world challenges of data quality, budget constraints, and workforce readiness hinder the technology’s adoption?
As organizations move to integrate GenAI into their anti-fraud programs, they must weigh the benefits against the risks. One survey respondent summed it up well, stating that while GenAI offers great promise, “proper guidelines are needed to minimize errors and bias.”
Mason Wilder, Research Director at ACFE, echoed these sentiments. “Generative AI has made significant advances, and we’re seeing more organizations adopt it,” Wilder said. “It will be fascinating to see how fast adoption progresses and how the technology evolves both inside and outside the workplace.”
Responsible AI Adoption is Key
As the fraud prevention landscape changes, generative AI may play a critical role in improving efficiency and accuracy. However, organizations must approach its deployment with caution, ensuring that they use the technology ethically and effectively.
