February 9, 2023
Anomalies can be classified into three main archetypes according to their relationship to the majority of the data under consideration.
An individual data point assumes an abnormal value compared to the common value range in the dataset.
Example: A suspiciously high-value card payment or bank deposit considering the account holder’s previous transactions.
This type of anomaly is context-specific as it entails a data point that is anomalous compared to most data points in the same scenario (typically from a temporal perspective).
Example: A spike in network traffic overnight or a skyrocketing sales growth outside the holiday season.
These are subsets of data points that might not seem anomalous data per se but raise suspicion when occurring together.
Example: Multiple login attempts from the same account or a sequence of unusually expensive purchases.
An ML algorithm can learn to identify patterns and anomalies via three different training techniques:
The anomaly detection algorithm is trained with already labeled data, namely the data already labeled as normal or anomalous.
These are the main steps required to build and deploy an anomaly detection software solution using machine learning algorithms.
Data source selection
ML-powered anomaly detection systems offer several advantages over traditional solutions.
Wider data pool
Algorithm training for anomaly detection is a time-consuming and computationally demanding process, as the datasets should be large enough to provide sufficient examples of outliers.
A common trick for training optimization is to select a smaller subset of essential features (such as IP address, transaction data, or payment method) and discard irrelevant attributes, depending on your scenario.
The challenging trade-off between ML algorithms' data hunger and strict data management legislation can be a massive downside in highly regulated industries such as finance and medicine.
Ensure that your ML-based anomaly detection solution complies with all major standards and regulations applicable to your industry, such as GDPR, HIPAA, and PCI DSS.
Anomalies, by their very nature, are much less abundant than standard data points with normal behavior. This can make training datasets unbalanced and algorithms potentially biased.
You can use synthetic minority oversampling or majority undersampling techniques to artificially reduce the number of outliers compared to normal data instances and therefore ensure a more balanced dataset.
ML-based anomaly detection systems have shown their potential in proactively addressing risks in different industries and applications, from fraud prevention and cybersecurity to advanced diagnostics and real-time asset monitoring. Furthermore, anomaly detection with machine learning has proved superior to its more traditional, rule-based counterparts, thanks to a successful mix of reactivity, scalability, and accuracy. Despite some algorithm training and compliance challenges, machine learning in anomaly detection can make the famous motto "prevention is better than cure" a reality. If you aim at enhancing your risk management capabilities, consider implementing a machine learning-based solution expertly crafted by Itransition.
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