A recent study conducted by the Association of Certified Fraud Examiners (ACFE) and SAS has highlighted the growing interest in generative artificial intelligence (AI) among anti-fraud professionals. According to the 2024 Anti-Fraud Technology Benchmarking Report, approximately 83% of respondents anticipate the integration of generative AI into their toolkits within the next two years.
The report reveals that while 18% of anti-fraud professionals currently utilize AI and machine learning (ML) technologies for fraud detection, an additional 32% plan to implement them by the end of 2024. This indicates that the use of AI/ML in anti-fraud programs is expected to nearly triple in the coming year.
However, despite this significant level of interest, the growth of AI and ML for fraud prevention has been relatively slow, with a mere 5% increase since 2019. This is a stark contrast to the expected adoption rates reported in previous years, which stood at 25% in 2019 and 26% in 2022.
In contrast, the application of biometrics and robotics in anti-fraud programs has experienced steady growth. The use of physical biometrics has risen by 14% since 2019, with 40% of respondents now incorporating it into their fraud-fighting strategies. Additionally, the utilization of robotics has increased from 9% in 2019 to 20% in the present survey. These technologies have found the most significant application in the banking and financial sector, where 51% employ physical biometrics and 33% utilize robotics.
While the adoption of generative AI shows promise, several challenges may hinder its predicted rise in anti-fraud programs. Budget restrictions, data quality issues, skills gaps, and ethical concerns surrounding AI deployment are among the factors that organizations may need to address.
The study emphasizes the need for responsible and ethical usage of generative AI tools. Organizations must carefully invest their anti-fraud technology budgets to gain an upper hand in the ongoing technology arms race with criminal enterprises. It is crucial to acknowledge the complexities of scaling AI and analytics life cycle and opt for modularized solutions that deploy AI-powered platforms effectively.
As society continues to explore the advantages and disadvantages of generative AI, more organizations are taking the first step towards incorporating these technologies into their anti-fraud initiatives. While there may be challenges to overcome, the growing interest in AI among anti-fraud professionals signifies a clear shift towards the use of advanced technologies in the fight against fraud.
FAQ Section:
Q: What is the main finding of the study conducted by ACFE and SAS?
A: The study found that approximately 83% of respondents anticipate integrating generative AI into their anti-fraud toolkits within the next two years.
Q: How many anti-fraud professionals currently use AI and machine learning technologies for fraud detection?
A: Currently, 18% of anti-fraud professionals utilize AI and machine learning technologies for fraud detection.
Q: What percentage of anti-fraud professionals plan to implement AI and machine learning technologies by the end of 2024?
A: An additional 32% of anti-fraud professionals plan to implement AI and machine learning technologies by the end of 2024.
Q: How much has the use of AI and machine learning for fraud prevention grown since 2019?
A: The use of AI and machine learning for fraud prevention has only increased by 5% since 2019.
Q: Which technologies have experienced steady growth in anti-fraud programs?
A: The application of biometrics and robotics has experienced steady growth in anti-fraud programs.
Q: How much has the use of physical biometrics risen since 2019?
A: The use of physical biometrics has risen by 14% since 2019.
Q: How much has the utilization of robotics increased since 2019?
A: The utilization of robotics has increased from 9% in 2019 to 20% in the present survey.
Q: Which sector has found the most significant application of biometrics and robotics?
A: The banking and financial sector has found the most significant application of biometrics and robotics.
Q: What challenges may hinder the predicted rise of generative AI in anti-fraud programs?
A: Budget restrictions, data quality issues, skills gaps, and ethical concerns surrounding AI deployment may hinder the rise of generative AI.
Q: What is the study’s emphasis regarding the usage of generative AI tools?
A: The study emphasizes the need for responsible and ethical usage of generative AI tools.
Key Terms/Jargon:
1. Generative artificial intelligence (AI) – Refers to AI systems that are capable of creating new content or generating new information.
2. Anti-fraud professionals – Individuals who specialize in preventing, detecting, and investigating fraudulent activities within organizations or industries.
3. Artificial Intelligence (AI) – The simulation of human intelligence in machines that are programmed to think, learn, and problem-solve.
4. Machine Learning (ML) – A subset of AI that enables computers to learn and improve from experience without being explicitly programmed.
5. Biometrics – The measurement and analysis of unique physical or behavioral characteristics of individuals, such as fingerprints or facial features, for identification and authentication purposes.
6. Robotics – The branch of technology that deals with the design, construction, and operation of robots that can perform tasks autonomously or with human guidance.
Suggested Related Links:
– Association of Certified Fraud Examiners
– SAS
The source of the article is from the blog papodemusica.com