Anomaly detection is a technique used in data analysis and machine learning to identify patterns, events, or observations that deviate significantly from the expected or normal behavior within a dataset. These deviations, known as anomalies or outliers, may indicate errors, outliers, or potential issues in the data.
The primary goal of anomaly detection is to distinguish normal patterns from abnormal ones in order to identify instances that require further investigation. Anomalies can represent interesting and valuable insights, such as security breaches, system failures, fraudulent activities, or rare events. The detection of anomalies is applicable across various domains, including finance, cybersecurity, healthcare, manufacturing, and more.
There are different approaches to anomaly detection, including:
Statistical models assess whether a data point falls outside the expected range based on statistical measures like mean, median, standard deviation, or other distribution properties.
Machine Learning Models
Machine learning algorithms, such as clustering, classification, or regression models, can be trained to recognize patterns in normal data and identify anomalies.
Unsupervised learning methods, like clustering or autoencoders, don't require labeled data. They aim to identify patterns in the data without prior knowledge of what constitutes normal or anomalous behavior.
In supervised learning, models are trained on labeled datasets, distinguishing between normal and anomalous instances. This trained model can then be used to identify anomalies in new, unseen data.
Density Based Analysis
Density-based methods, like Local Outlier Factor (LOF), evaluate the density of data points in a neighborhood. Anomalies are often located in regions with lower data density.
There are multiple applications of anomaly detection.
Identifying unusual patterns or activities that may indicate a security breach.
Detecting fraudulent transactions or unusual patterns in financial data.
Identifying abnormal medical conditions or outliers in patient data.
Detecting defects or anomalies in production processes.
Anomaly detection is a crucial component in various industries where identifying and addressing abnormal patterns quickly can lead to improved efficiency, reduced risks, and enhanced overall system performance.
Unfortunately, traditional tools and approaches to data and analytics do not scale to deliver solutions like this.
There are too many delays in the process, and the systems often used are not performant enough to process high volumes of data with low latency. In addition, traditional business intelligence tools are not rich and flexible enough to meet the business demands.
This technology stack needs to be re-invented for the cloud, with tools and architectural patterns that are built for real-time advanced use cases and predictive analytics:
We are Ensemble, and We help financial services businesses build and run sophisticated data, analytics and AI systems that drive growth, increase efficiency, enhance their customer experience and reduce risks.