How to process a claim with AI?
AI insurance claims processing can happen within a few minutes, from information extraction from documents to claims to process.
Although we have taken the example of vehicle damage AI-enabled insurance claims, the same process is replicated in other claims. Along with NLP – Natural Language Processing – and OCR – Optical Character Recognition – techniques, it is possible to capture and extract critical information from both hand-written and printed documents.
Furthermore, NLP-driven chatbots can be used to assess the claimed damage by analyzing the photos and videos of the damage.
Examples of AI-enabled claims processing
Several key players in the insurance industry are exploring the benefits of machine learning and claims management to improve processing.
New AI-based platforms are being developed to analyze damage in real-time using 3-D imagery. Additionally, AI-based chatbots are being used to streamline the customer response system by simplifying claims submission and photo and video updation of the scene.
Using NLP solutions, insurance companies are also tightening and identifying fraudulent claims.
Quality data: The foundation of AI-driven claims processing
AI provides insurance companies the ability to take critical decisions about complicated claims by scrutinizing customer data, behavior analysis, and claim documentation to ascertain whether the claim is genuine or fraudulent.
However, the biggest hurdle in achieving automation is developing a robust ML-based claims processing solution that can be smoothly integrated into their existing systems. And the first step in developing machine learning-based models that can accurately predict claims is gathering high-quality data.
Your automation process can yield tangible results only when high-quality data is used to train the ML models. Integrating custom solutions within your legacy systems or implementing a framework that automates claims processing is easy. But, when you are not working with quality, verified, and labeled data, you will not be able to take the first step toward AI automation.
How to get quality data at a lower cost?
The insurance industry gains a lot from artificial intelligence and machine learning technology. But machine learning thrives on data, and to acquire quality data at a lower cost; you need to look at outsourcing.
Outsourcing your data requirements to a premium provider will help you gain a development kickstart. You need large quantities of third-party data, claims records such as consumer information, medical claims, photos of damage databases, medical treatment documents, repair invoices, and more.
Shaip is the leading data provider of well-labeled data specific to insurance automation and claims processing. With a reliable training data provider such as Shaip, you can focus on developing, testing, and deploying automated claims processing solutions.