Transforming Insurance Applications with AI for Efficient Fraud Detection.

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Anomaly and Error Detection - AI-powered identification and reporting of errors and outliers in large datasets, such as internal claims, expenses and invoices.
Company: Insurance Application Enterprise Co. | Location: US
Technology: Azure ML, Azure AI, Azure ML Studio, Azure Power Automate, Azure Cognitive Services.

Project Background

A Fortune 500 financial enterprise sought to leverage Generative AI to empower their CFO with more dynamic, real-time financial insights and optimize funds management. While their existing analytical engine delivered accurate predictions, it lacked the flexibility and dynamic reporting capabilities that Generative AI could provide.

Challenge

  • The SaaS application, built on Azure PaaS, required an architectural upgrade to leverage Azure AI and ML services for more effective anomaly detection.
  • Large datasets containing patient, hospital and insurance company information required extensive processing for accurate claims handling and fraud identification.
  • The current analytics system lacked sufficient training to detect a wide range of potential fraud scenarios, miscalculations, data errors, or outliers.
  • Errors during claims processing, often due to miscalculations or missing data, led to multiple resubmissions and increased overhead costs.

Perennial’s Implementation

  • Focused on selecting and fine-tuning the most accurate algorithms within Azure ML services to achieve near-perfect anomaly detection.
  • Utilized Azure Monitor Services to enable real-time data analysis of streaming data, flagging outliers before applying Azure ML algorithms.
  • Trained Azure ML algorithms using historical data to reach nearly 100% efficiency in detecting fraud.
  • Implemented Azure Power Automate and Stream Analytics for real-time cross-functional data analytics, ensuring timely detection of anomalies.
  • Deployed Azure Cognitive Services, incorporating Vision and Speech APIs for biometric verification.
  • Updated the cloud architecture to fully support AI and ML capabilities for early detection of fraud, missing data and calculation errors during claims processing.

Key Highlights

  • Achieved real-time fraud detection while managing high volumes of transactional and claims data.
  • Reduced insurance claim processing time by 70% through the use of biometric data verification.
  • Eliminated human error in data entry and claims calculation.
  • Increased claims handling efficiency by 35%, improving the management of errors and inconsistent data.
Success Story

Re-engineering Financial Processes with IDP & RPA for Enhanced Automation.

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