Fraud Detection & Prevention Fraud Detection & Prevention

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In order to identify incorrect billings, fraud, and misuse

e.g. in healthcare, in telecommunications, or energy suppliers, INFONEA offers explorative analyses based on the connection of customer, contract and process data.

For which contracts are fraud cases more likely? Are there patterns and anomalies in fraud cases – apart from single notifications of claims – on the levels of contracts, customers, brokers, or partners?

For an early recognition of fraud and misuse cases, companies require:

  • analyses of alarms from operational systems,
  • a quality check of the misuse detection,
  • the connection of data sources such as notification of claims and risk scores, data regarding claim settlements and fraud detection, contract and customer data, claim history, payment behavior, and complaint management,
  • the connection with external data such as accident and population statistics,
  • analyses of patterns and anomalies.

Your benefit

  • Risk minimization by means of the detection of fraud and misuse cases
  • Increase of customer satisfaction with dedicated fraud detection: honest customers can be served quicker, and benefit costs, caused by fraud, can be minimized
  • Reduction of cases of wrongly assumed fraud („false positives“)
  • By means of Self-Service BI, insurance analysts can analyze the course of claim notifications and fraud cases, detect fraud patterns, track, evaluate and report them, and share them between users. By means of the connection with external data, patterns and trends can be identified over the course of time.
  • Improvement of the risk scoring by extending and sharpening the used indicators with downstream expert analyses (closed loop in a learning system)
  • Risk Management: holistic claim reporting incl. fraud KPIs and internal efforts
  • Fraud Prevention: learning from fraud patterns and continuous further development of agreements and internal processes

Contact us!We like to present you the details of this application example.
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