Fraud

AI-powered early detection identifies up to 80% of losses, and analytics-driven insights can play a pivotal role in informing new rules and preventative actions.

The digitization of fraud

The pandemic has seen fraudsters develop new schemes to exploit the transition of bank workforces to remote operations and the broad population shift to digital channels.  SparkBeyond’s Discovery identifies criminal activity and alerts at-risk cohorts and individuals by detecting subtle signals in vast amounts of data.

The platform uses anomaly detection variables to uncover previously undetected, fast-changing fraud patterns trends (account takeover, money-laundering), flagging and identifying suspicious call and customer patterns, enabling teams to react swiftly to reduce first- and third-party fraud.

For insurance clients, Discovery reduces the number of ‘false positives’ and improves investigator efficiency by enabling automated decision making for simple claims and claims adjudication for potentially fraudulent claims.  Pharma teams can use track-and-trace information to reduce fraud across the supply chain – from manufacturers, logistics companies and wholesalers to pharmacies and hospitals.  In CPG, Discovery can identify subtle fraud patterns (purchases/returns) and bogus warranty claims, and enhance quality assurance programs by analyzing default patterns.

A major US card issuer and online bank wanted to improve its fraud loss detection and prevention capabilities.  SparkBeyond integrated with the client’s secure cloud to ingest anonymized customer data and its fraud solution was deployed to address $30+ million of cases.  Outcomes:

  • Consistent identification of compromised cards 1+ day in advance.
  • 8% of total fraud losses mitigated through daily card reissue.
  • $5 million in impact (10x+ ROI) within 6 months.

AI-driven analytics are helping combat COVID-driven growth in global fraud by analyzing vast amounts of data and revealing previously undetectable, ultra-subtle anomalies and signals. 

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Business Insights

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Predictive Models

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Micro-Segments

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Business Automation
Rules

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Root-Cause
Analysis

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