AI for analyzing car accident damage and processing of insurance claims

The Problem

A report by McKinsey states that 5-10% of insurance claims are considered fraudulent, costing US insurers more than USD 40 billion every year. Insurance companies need to employ a large number of personnel to handle and validate claims for insurance from consumers. Insurance companies need a solution that could automate the process, to not only save on time and cost, but to also reduce fraudulent claims.

Solution

Machine Learning models created in Kranium can be used to understand the process of identifying and recognizing damaged parts, assessing the extent of damage, predicting the kind of repair needed, and estimating the total cost. This can be achieved with the help of Image/Video Annotation for Computer vision to train ML models. The ML models can extract, analyze, and offer insights that result in a quick inspection process that takes into consideration the road, weather, lighting, speed, damage type, accident severity, and traffic with greater accuracy.