Industry: BFSI
The client stands as a prominent player in India’s private general insurance sector, providing a wide range of insurance plans and tailored policies, encompassing health, travel, motor insurance, and more. Their extensive network comprises 131 offices and over 90,000 intermediaries distributed across the country.
Business Challenges
The vehicle damage assessment required a physical inspection, leading to lengthy 10-12-day insurance settlement procedures involving extensive documentation and discussions with inspectors.
Tableau lacks advanced features like Natural Language Processing (NLP) and Machine Learning, limiting the efficiency and ease of report creation.
Our Solutions
Celebal Technologies and the client collaborated to create RAPID, an AI-powered image analytics platform.
Celebal’s team developed image detection algorithms for identifying vehicle damage using Azure ML and Cognitive Services. Over 10 million sample images were trained on the Microsoft Azure Cognitive Vision model, followed by model calibration and tuning.
Data fetched from Azure Blob in Databricks underwent pre-processing using Computer Vision. An ensemble of Azure Custom Vision and custom-built YOLO-V3 models were used, along with Mask R-CNN, for accurate damage assessment, instant reports, and repair cost estimation presented via a dedicated customer application.
Technologies Used
Business Impacts
By collaborating with Celebal Technologies and leveraging AI-powered vehicle damage detection, the leading insurance provider successfully addressed the challenges in their claims processing workflow. This innovative solution not only improved efficiency but also elevated customer satisfaction, positioning the insurance provider as a frontrunner in the industry. The case study demonstrates the transformative impact of AI technology in the insurance sector and its potential to revolutionize claims management.
