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detect Fraudulent Claims in Real Time
Use machine learning and experience-based rule sets to reduce your fraudulent claim detection efforts.
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DATA INPUT
STRUCTURED DATA BASIS
A wide variety of claims data is converted into a structured format (Eucon Claims data structure) via different interfaces, thus enabling an E2E approach. Thanks to this modular approach, Claims Radar can be used flexibly with existing structured data.
DETECTION
REAL-TIME DETECTION USING ARTIFICIAL INTELLIGENCE AND RULE SETS
Claims Radar uses smart check processes to detect fraudulent claims in real time. The check processes are supported by machine learning and experience-based rule sets. This allows known fraud patterns to be recognized and correlations concealed in the data to be uncovered and taken into account for future checks.
CONTROL
RESPONSIVENESS THROUGH INTUITIVE USER INTERFACE
Claims Radar features a user-friendly and customizable interface with an overview of all relevant claims. The intuitive navigation enables efficient viewing and processing of all fraudulent claims.
EVALUATION OF RESULTS
CONTINUOUS IMPROVEMENT
Augmented intelligence components are used to capture the test result for the user and, with this feedback, ensure continuous improvement of the self-learning system as well as of the rule sets.
Selected References
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