Anomaly detection technique can be applied to a wide spectrum of business needs where unusual behaviour can have significant business implications. Typical use cases where the technique could be used are enhancement of fraud prevention, detection of IT security breaches and predictive maintenance of machinery.
Through continuous data monitoring and pattern analysis our versatile AI based framework is able to identify outliers in a large scale dataset, correlating it and delivering insights to the specific business needs in a very short period of time.
By leveraging real-time detection functionality and machine learning algorithms, the framework can discover unknown and unknowable activity in a wide range of business use cases and can be adopted to the specific business requirements.
Typical use cases with high business value are:
Detect intrusion into networks, prevent theft of source code or intellectual property.
Banking & Finance
Flag abnormal transaction, deposits or loan applications and detect cyber intrusions.
Insurance & TELCO
Automated detection of fraudulent tariff plan applications or fraudulent insurance claims.
Detect abnormal machine behavior to prevent cost overruns.
Fraud Detection Enhancement
AI powered anomaly detection automatically detects new, previously unknown patterns and abnormalities in loan applications, insurance claims, transactions and other scenarios effectively complementing and strenghtening standard 2 layers of fraud prevention - rule based fraud prevention detecting simple known fraud patterns and scoring models detecting complex known fraud patterns.
decrease of application fraud in large TELCO company in Europe