Anomaly detection

In data perspective, any data point that deviates from the rest of the data is called anomaly. Anomaly detection is finding out these unusual patterns and deviations in data that don’t confide to the norm. Anomaly detection will reveal any critical event like fraud, breaches, system failures, malfunctions, etc, which is why it’s used in fraud detection, network monitoring, and predictive maintenance.

Example for anomaly detection is monitoring bank transactions for identifying suspicious and fraudulent transactions. Let’s say a customer only makes small purchases using his credit card. Suddenly, there is a high volume transaction made using his account. ML-based real-time anomaly detection can raise alerts to the risk team and the customer to identify if it's really the customer who made it.

Why is anomaly detection important?

Anomaly detection is important as it helps businesses stay alert and proactively look out for unusual activities that could mean danger. 

Early detection of issues: identifying issues early reduces cost it requires to fix the damage, be it machinery failure or a cybersecurity attack. It also prevents unnecessary downtime of equipment, helping you run operations smoothly. Many companies have saved in millions using advanced AI-based anomaly detectors, particularly in the manufacturing and operations sectors. 

Better risk management: for financial units and institutions, risk mitigation is important. Anomaly detection helps enforce proper risk mitigation practices alerting everyone when there is a likely breach or fraud.

Can be life-saving: anomaly detection also plays a role in healthcare, wearable, and digital monitoring devices. For example, anomaly detection can tell you when bodily measurements like blood pressure, heartbeat, etc., fall below or go beyond critical thresholds. Combined with an alerting system, the detector could alert healthcare practitioners to bring the first-aid on time.

Lead to performance improvements: Any fault or irregularity with machinery or systems could be fixed on time.

Anomaly detection methods

Following are the methods used to detect anomalies in datasets.

Statistical methods

This is when you use statistical analysis to detect anomalies. Most of the statistical methods here assume that data follows standard distributions across its range and a deviation from this value is considered an anomaly. But not every dataset may have a normal distribution, so results will not be accurate all the time.

Some common statistical methods like chi-square test, Gaussian mixture model, Grubbs test, z-score method, etc. For example, the z-score method here finds out the mean value of the data and checks how each data point is away from it.

Statistical methods for anomaly detection are more suitable if your data is uni or low variate with fewer dimensions. Otherwise, there will be complex calculations involved, which can get difficult when there is a real-time requirement or when the data scales.

Time series analysis

Time series analysis is suitable for detecting anomalies on time-dependent data. Example: monitoring stock price changes. You could use time series methods like STL, ARIMA, moving averages, or exponential smoothing for anomaly detection. It uses data visualization tools, statistical analysis, and machine learning for flagging anomalous values. 

Time series analysis checks for deviations in datasets from the regular trend, seasonality. For example, moving averages rule out short-lived fluctuations in the dataset so significant fluctuations are clearly visible. Hence, with time series methods, it’s possible to differentiate between a real anomaly and actual market fluctuations.

Machine learning anomaly detection

Compared to the above traditional methods, machine learning techniques are more sophisticated, making it apt for real-time anomaly detections with complex constraints. They could pick up patterns and trends automatically no matter how minute it is, and suggest recommendations without explicit programming. For example: Maps suggesting alternative routes based on real-time traffic (detection of too much vehicles higher than the threshold).

ML algorithms can detect anomalies even if there are multiple variables, each sharing complex relationships with one another. They have better stability even with growing data compared to previous methods. If you want accurate anomaly detections with less false positives and have large and multivariate data sets, ML and AI methods can be your go-to.

Some common ML techniques employed for anomaly detection are isolation forests, k-means clustering, etc. 

Anomaly detection for real-world problems

Real-world anomaly detection use cases are much more complex - from intrusion detection to patient monitoring to smart city and grid planning.

Every second is precious here and reacting fast can prevent damages and unauthorized access. Besides, there is huge, unstructured real-time data involved from IoT devices, sensors, transactional platforms, SaaS applications, etc. 

That’s why the need for a stable, flexible, and ensemble anomaly detection system is slowly increasing. You will need an ensemble approach that combines both statistical and AI-based anomaly detection, along with good data management architecture. These systems will be a guard, helping you make fast decisions, saving costs, and improving operational efficiencies.