A claim is an oxymoron in the insurance industry (Insurance Claim) – neither the insurance companies nor the customers want to file claims. However, both parties want different things when the claims are eventually filed.
The customer wants the claims processing to be quick, prompt communication, speedy resolution, and a personal touch, if possible.
The insurance company wants efficient, accurate resolution. And eliminate the risk of overpaying, fraud and litigation. But why does claims document automation matter in the insurance realm?
About 87% of policyholders believe that how claims are processed impacts their decisions to stick with the insurer.
On the one hand, claims processing is perhaps the most visible of all insurance activities, which impacts customer satisfaction and retention. And on the other hand, insurance fraud is a huge tiger waiting to be tamed. The total cost of insurance fraud was more than $40 billion annually in the US. Insurance claims processing is not the only problem plaguing the insurance industry. Some other all-too-familiar critical issues are
- The time spent manually copying and pasting data across multiple systems.
- Overpayments are due to claims processing inaccuracies.
- Very slow claims resolution leading to customer grievances.
- Higher operation costs.
So, what is the first step towards a better claims experience? AI-based automation.
Artificial Intelligence in the Insurance Industry
Before integrating AI-driven claims processing, let’s understand how conventional claims processing functions.
In conventional claims processing, the customer claiming the insurance must produce all the necessary documents to verify and substantiate the veracity of the request. The primary steps in claims processing are claims adjudication, EOBs, and settlement. Although this appears simple, it is easier said than done.
A ton of paperwork, document verification, data analysis, and fact-checking are required before the claim can be settled. And this process is riddled with manual errors during verification and review, paving the way for elaborate claims fraud. That’s the reason why companies are leveraging the benefits of AI.
AI-enabled claims processing – The Process
The integration of AI in the insurance business model can add value to both customers and insurance companies.
For instance, imagine your vehicle was involved in a minor accident. With the embedded telematics devices, your vehicle will send information about the suspected damage to the system. The same system will seek confirmation from the customer to verify the accident.
The system will use predictive and advanced analytics to decide whether the claim can be processed or if human intervention is required.
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.
