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Anomaly detection

Anomaly detection illustration

Identify and discover anomalies in data

Anomaly Detection

Today’s organisations generate huge volumes of data daily. If data is used correctly, it can be a huge strategic asset to a company and help them to make faster, more intelligent decisions.

However, many organisations still rely on retrieving and analysing data from disparate, siloed systems. This method of detecting anomalies in data has its limitations and prevents organisations from zooming out and seeing the true scale of the picture.

Organisations need to adopt a context-driven view of data, one that uncovers connections between silos of data so the data can be used in a more meaningful way.

What is anomaly detection?

Anomaly detection is the process of recognising events or data that are significantly different from the majority. In a given dataset, it is the identification of data that presents irregular patterns.

The difference between traditional linear models and Knowledge Graphs in detecting anomalies

Many analytics solutions designed to present anomalies are based on-linear models and simply present an enormous list of risks flagged as ‘high, ‘medium’ or ‘low’, with no context behind why the data is risky. This means that each risk has to be investigated, which is very time-consuming, inefficient and costly.

Anomaly Detection using Knowledge Graphs

Engine B’s Anomaly Detection Knowledge Graphs enable organisations to visualise unexpected changes or deviations from an expected pattern in a dataset and see reasons for the anomaly. This means there are far fewer anomalies to investigate and an audit, for example, can be completed much quicker with more intelligence behind decisions.

Our Anomaly Detection Knowledge Graphs also identify anomalies within different data types, such as structured, semi-structured or unstructured data.

Use Knowledge Graphs to fight financial crime

Our Fraud Detection Knowledge Graphs help organisations to detect fraud and fight financial crime by identifying more instances of fraud that would otherwise not have been possible to see in traditional analyses tools.

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