September 26, 2023
Explore the nature, payoffs, and applications of this technology in common fraud scenarios, along with some guidelines to streamline its adoption. Finally, find out how ML services and software can help you protect your company and customers from ever-evolving fraud threats.
of organizations experienced some form of fraud over the previous 24 months
PwC
of organizations already leverage AI and ML to detect and deter fraud
ACFE
of organizations plan to adopt AI and ML for fraud detection in the next 2 years
ACFE
ML-enabled anomaly detection applications differ from traditional software in terms of detection technique:
ML-based fraud detection systems are trained with large amounts of labeled data, previously annotated with certain labels describing its key features. This can be data from legitimate and fraudulent transactions described with "fraud" or "non-fraud" labels, respectively. These labeled datasets, which require rather time-consuming manual tagging, provide the system with both the input (transaction data) and the desired output (groups of classified examples), allowing algorithms to identify which patterns and relationships connect them and apply such findings to classify future cases.
These algorithms are fueled with unlabeled transaction data and have to autonomously group these transactions into different clusters based on their similarities (shared behavioral patterns) and differences (typical vs unusual patterns which can correspond to fraudulent activity). This approach, typically associated with deep learning, is computationally demanding but can be the only choice when facing fraud attempts that have never been met before and therefore unlabelled.
This trial-and-error approach involves multiple training iterations in which the algorithm performs a fraud detection task in different ways several times until it can accurately identify fraudulent and non-fraudulent attempts. Since it does not require labeled inputs, reinforcement learning can be applied without prior knowledge of the current fraud scenario. However, it requires considerable computing power.