Fraud remains a significant threat to businesses and is leading to complexity with advancements in technology. Companies today need to address traditional fraud risks and adapt to AI-driven schemes. Data analytics has emerged as a powerful tool amid all these as it enables organizations to detect and prevent fraud.

Organizations should start with accurate and complete data to maximize the potential of data analytics. BDO forensic technology expert Jared Crafton said that accurate, complete and timely data are the backbone of effective fraud detection.

Fraud analytics models rely on clean and unbiased data to produce reliable results. Forensic data science specialist Roxanne Wang stated that companies need to align risk factors with business contexts as well as past fraud patterns. It is also important to review on a regular basis and update the models.

Advanced data analytics techniques such as predictive modeling, real-time monitoring and machine learning are transforming the process of combating fraud. The tools can identify anomalies, flag suspicious activities and forecast potential fraud scenarios. Predictive models can help in anticipating risks in specific industries such as purchase order fraud in manufacturing.

The collaboration between internal teams like finance, compliance, legal and IT is simultaneously important to align analytics models with regulatory requirements as well as actionable outcomes.

The fraud segment has become sophisticated with the rise of generative AI (GenAI) and more such technologies. Fraudsters are now using AI to automate phishing attacks, create deepfakes and conduct large-scale social engineering campaigns. Seasoned data analytics consultant Mason Pan said that the technological shift has transformed fraud into a more global as well as intricate challenge.