The insurance industry leads the way in its AI implementation. For each and every insurance actor, artificial intelligence and image recognition present opportunities to offer an enhanced user experience, to optimize costs, or even to free up staff from time-consuming and low-added-value tasks
Computer Vision for Insurance
Computer vision offers the ability to automate, scale, and enhance risk evaluation while seeing gains in operational efficiency and cost reduction. Insurers now have access to an unprecedented quantity of image and video data. The carriers are beginning to invest in machine vision technology to process this data, programmatically analyzing risk factors and making sense of these vast image stores. Machine vision represents the leading edge of AI. Since insurance has always been data-intensive, it is perfectly poised to be significantly impacted by AI.
Computer vision helps insurers automate, scale, and enhance risk evaluation while seeing gains in operational efficiency and cost reduction. It will enable insurers to redefine how they should work, how they should create innovative products and services, and how they should deliver customer experiences. Machine vision will allow insurers to redefine existing processes, create innovative products, and transform customer experiences. Machine vision is going to unlock trapped value in new and existing datasets, leveraging the data by creating ways across the entire value chain.
Application of Computer Vision for the Insurance Industry
Vehicle Damage Assessment
Inspection is usually the first step in a damage insurance claims process, whether it’s an automobile, mobile phone, or property. Assessing the damages to calculate an estimate of repair costs can be a challenging task for insurance providers. Deep Learning models can be used to detect the different types, areas, and severity of damage with greater accuracy and automate the claims process. The machine learning model will be trained on thousands of images of damaged cars labeled according to the severity of the damage and paired with the repair costs to fix it.
It reduces the time it takes for customers to receive their payouts and avoids claims leakage, saving insurers money. TagX can label images and video of damaged cars, phones and other claimed properties of customers for such automated models
Insurance Claims Processing
Computer vision brings to insurers in terms of reduced insurance claims processing and settlement cycle time. It also lowers cost per claim, increases appraisal accuracy, and reduces adjuster travel time and costs. It all results in fewer fraudulent claims, enhanced customer satisfaction, and easy adoption of insurance smart devices.
Insurers are widely using NLP to improve their claims processing and customer servicing operations. NLP is being used to scan existing policies and structure the framework of new policies to make the insurance process more efficient. NLP is also used for scanning ambiguities in claim reports for quick fraud detection.
Analysis of Natural disaster damage
Computer vision helps Manage risk and reduce costs to aid in processing damage assessment. Using aerial imagery and geospatial applications, it helps to assess property damage throughout the evacuated areas. Identify homes that have been completely destroyed or even partially damaged to calculate insurance claims. This prevents fraudulent claims of damaged property from weather-related events. During the catastrophic Hurricane Harvey, insurance agencies used drones to inspect roads, railway tracks, oil refineries, and power lines in Houston. This made the process accurate with no scope for human error.
Conclusion
There are a lot of new applications of computer vision algorithms in the insurance industry. But only those insurance companies that are on top of their data and ensuring it is ready for AI will have a real advantage over their competitors. TagX can help in the analysis and categorization of images in an effective and scalable manner.
When it comes to processing and analyzing insurance applications, and insurance claims, reviewing medical records for identifying risk, or even gauging customer sentiment, having high-quality annotated data will help drive success across many areas where AI is being employed.
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