Detect fraudulent claims and pay
valid ones almost immediately with

Veritas Propensity
of Fraud Model

By reshaping your claims environment, you will prevent
and detect fraud instantly, effectively and efficiently.

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How?

Our VERITAS platform uses cohesive toolsets which includes machine learning (a collection of artificial intelligence (AI) algorithms trained with our historical data) to determine risk rule models and behavioural analytics enriched by automated multiple real-time data source inputs to predict and detect fraud while reducing false positives in order to preserve the customer experience.

Want to understand more?

Download our case study

IDENTIFY

  • Low risk claims to be paid almost immediately
  • Possible fraudulent claims

REDUCE

  • Client friction
  • False positive investigations
  • Admin burden

DETECT

  • Fraudulent trends
  • Syndicated activities
  • Opportunistic fraud
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COLLECTIVELY SAVED
OUR CLIENTS

R35 125 082.00

BENEFIT BY IDENTIFYING
FRAUDULENT BEHAVIOUR

The VERITAS fraud model uses one algorithm with hundreds of patterns, identifying low risk claims in seconds, whilst flagging possible fraudulent claims. The fraud model is constantly updated with real-time investigation and fraud scheme data.

  • Access supportive anti-fraud control measures
  • Prevent and detect fraud through sophisticated artificial intelligence solutions and machine learning fraud models
  • Rapid business decisions making with better outcomes
  • Instant cost-effective innovation without making use of internal resources responsible for day to day operations
  • Partnership for life
Read our War Stories

OPERATIONAL BENEFITS

  • Less false positive results
  • Less client friction and reputational damage
  • Holistic mitigation of fraud risk
  • Almost immediate payment of low risk claims
  • Competitive advantage due to increased sales
  • Product re-engineering
  • Enhanced decision making tool
  • 360 degree on daily risk profile
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Evaluate your claims against Veritas – reality doesn’t have to be painful.