Success Story / Gen AI for CFO

Transforming Insurance Applications with AI for Efficient Fraud Detection.

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.

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Project Background

A leading Insurance Application Enterprise sought to transform its SaaS platform by incorporating AI to enhance anomaly and error detection, particularly in identifying and reporting fraudulent activities within insurance claims. The company aimed to expand the capabilities of its existing insurance applications to improve fraud detection across multiple functions.

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.
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