Detect anomalies in data
ADF (Anomaly detection framework) platform utilizes artificial intelligence algorithms and technologies to detect anomalies, suspicious actions, events, and unwanted behavior in large and complex data. The platform has a proven track-record across various domains.
Monitoring and analyzing of anomalous trends, patterns and events in processes is typically being done manually or based on a set of pre-defined rules. Such an approach leads to identifying mostly known patterns with large amount of irrelevant cases leaving hidden risks and unknown trends unspotted and unresolved.
ADF framework detects anomalous trends, patterns and events directly from all available data sources in real-time. That allows to uncover hidden anomalous trends or unknown patterns effectively and adjust continuously to anomalous behavior evolvement.
Based on previous deployments, we can see that detection time to uncover both known and novel types of anomalies and threats is reduced to minutes or seconds.
The system constructs a wide variety of time series and data clusters from small and large datasets. A rich algorithmic library then finds significant outliers, which are then analysed and reported.
Various streams of data from different systems are used as an input source for AI algorithms to detect anomalies. Based on all available data we find new types of anomalous patterns.
All detected anomalies are reported and presented in a user-friendly way to allow further analysis that focuses on relevant cases only.
The users can give their feedback, hence regulate the number of anomalies received as well as teach the system what is an anomaly and what is not.
Despite implemented rule-based system and background check for new customers, our client suffered from subscription fraud (customers not paying for services and hardware) and was struggling to reduce it.
We have applied our own machine ;learning fraud detection framework to enhance risk management and empowered fraud prevention process with AI.
We have completed comprehensive journey from idea and proof-of-concept to deployment of ML fraud prevention process, preventing roughly 30 % frauds, which secured ROI within 1st year.
Area: Telco, credit risk
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