Car Insurance Claim Process – The insurance industry is notoriously bad for customer experience. Not China though. Over the past few years, China’s tech giants have made great strides in becoming a hub for insurance innovation. Just look at one example: WeSure, an insurance system based on the messaging app WeChat, celebrated more than 55 million users on its second anniversary. This means that the biggest challenge for Chinese insurance companies today is not only to digitize their business, but to go beyond traditional offerings and even integrate insurance with other financial services. To compete, insurers are transforming the industry using AI, IoT and big data.
Customer Satisfaction Score (CSAT) and Net Promoter Score (NPS) are the most important metrics for any insurance company. In the US, however, they are lagging far behind as insurance fails to keep up with expectations as other industries have grown. With the claims process the biggest influencer on customer satisfaction, let’s take a look at how technology can make a big difference to costs, efficiency and customer experience.
Car Insurance Claim Process
Why do insurance companies struggle with digitization and automation in the first place? Leaving aside the usual reasons such as unwillingness to change among employees or lack of budgetary and technical resources, one big reason comes from the nature of insurance – insurance processes are usually very flexible and are not designed to be easily integrated into digital performance. .
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For example, requirements data exists in a variety of formats (images, handwritten documents, voice memos) and is shared through multiple channels (email, document attachments, phone calls, chat), making it very difficult to accurately receive and analyze high quality without personal attention from an agent. And when it comes to decision-making, it is often more nuanced than an off-the-shelf system can handle – it is necessary to understand the context of each individual case.
Does this mean that the insurance industry can never be automated and that we need human effort for all parts of the process? Of course not. But it requires more advanced techniques that mimic human perception and judgment such as AI, Machine Learning and ML-based robotic process automation.
“Claims automation is truly the holy grail of insurance. Basically, it works on the three most important metrics that insurers care about: retention, expense and loss ratios. If we can solve common pain points in claims through automation, we can quickly improve all three metrics.”
Let’s see where and how automation helps to improve the process. And we will start from the first step in the communication between insurer and policyholder.
Steps You Can Take For Auto Insurance Claim
A First Notice of Loss or FNOL is the first notification to an insurance company that property has been lost, stolen or damaged. It is a document that describes the incident and the damage, based on the client’s personal account of what happened.
Most insurance companies still receive FNOLs over the phone, and it often takes a long time for a call center operator to gather all the information from the insured, usually with several follow-up calls.
Today, electronic FNOLs are common, when instead of calling the insurer or handing over documents in person, the customer can use a chatbot or mobile app to fill in the necessary information, upload media files and document review, and let the insurer process the claim. . faster and with better accuracy.
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If you use modern claims processing software, ask your service provider which FNOL collection systems they have integrations with. Some, like Guidewire and Snapsheet, will have FNOL digital interfaces out of the box. If nothing in their catalog works for you, you can choose a third-party FNOL procurement system like those from Capgemini and Wipro and integrate their APIs with your IT efforts.
A Car Insurance Claim Estimate Before The Tow Truck Is Called
If you rely on an outdated system for your operations and don’t have the resources for a full upgrade, you can still connect to FNOL receiving suppliers using legacy EDI integration, such as Netsmart or OneShield. Also, sometimes the cheaper and better option will be to build your own FNOL acquisition capability that better suits your workflow.
Digitized and structured FNOLs are critical to business automation success, and in the following sections you will see them. However, even if eFNOL provides a better customer experience, it may not be particularly useful for insurance companies that still need to follow their usual process of digitizing handwritten documents and photo evidence, transcribing audio and video reports and communicating with customers via e-mail due to lack of information. Let’s look at the solution to these problems.
For decades, organizations have used Optical Character Recognition (OCR) to process physical documents, essentially converting handwritten and typed text into machine-coded text. Although it provides high accuracy for typed text, OCR uses manually created templates and can make small mistakes in important information, such as a person’s name, date or price, which will render the digital copy useless. This means that files processed with traditional OCR must be checked manually, which is far from automatic.
A modern alternative to OCR is Intelligent Document Processing (IDP), also called Cognitive Document Processing (CDP) or ML OCR. This AI-based technology uses natural language processing, computer vision and deep learning to improve document quality, classify documents and generate unstructured data that can be transformed into usable, structured data.
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IDPs are often used in robotic process automation, enabling many common business tasks to be performed using defined workflows. For example, an RPA robot using IDP will be able to analyze documents sent by a customer, extract relevant data from text and media, and send it for further processing, such as manual checks or fraud detection algorithms, all without human intervention.
Smart document management typically works on top of RPA software. Read our article where we discuss RPA implementation options separately. The good thing about IDP systems is that they do not have to be aimed at the insurance business and the supplier options are wide. Depending on where you are, you have two implementation options.
Use an ML-based RPA system. Solutions such as UiPath or Automation Anywhere have their own ability to process cognitive documents and will require minimal integration effort on your part.
Implement a third-party IDP. Whether you’re looking to complement your existing RPA operations or upgrade the traditional OCR you’ve been using, consider vendors like Appian, InData Labs or insurance-focused Infrrd.
How To File An Auto Insurance Claim
Claims settlement is the process of organizing a large number of claims on an emergency basis. In the event of catastrophic events or simply during peak season when insurance companies are faced with a large intake of claims, they need to quickly and confidently identify which claims need to be settled first and by whom. This is where predictive analytics can come in handy.
Based on historical data, predictive analytics uses statistics and machine learning techniques to identify probabilities of future events. Because of all the data accident carriers collect and store, they can use predictive analytics to distinguish simple claims that can be automatically accepted from complex claims that need to be adjusted accordingly.
You probably already use some form of analytics. (Use our article to determine how far you are in analytics maturity.) But predictive analytics requires a lot more investment than any of its predecessors.
Introduce analytics technology. Get integrated data management tools, such as BI dashboards and data warehouses, that allow non-technical people in your company to access insights and use them to make decisions.
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Foster a data-driven culture. People will only be able to use these tools when they are embedded in their existing operations and reflect their needs. For this, be sure to train your employees so they know where and how to find data insights.
Hire machine learning experts for the team. Hire data science professionals who will drive automation by creating data pipelines, building models and integrating technologies.
However, FNOLs are not necessarily the only sources of immediate claims data. Internet of Things devices are a valuable source of real-time data that can help speed up the claims processing process.
Internet-connected products such as water sensors, smoke detectors or sensors in the car provide a large amount of information – and later knowledge – to the insurance companies.
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Homeowners in the United States file more than 14,000 water damage claims per day with an average cost of up to $15,000. Not only can this damage be prevented by using alerts from sensors, resulting in fewer claims, but data from these sensors can also . applied to predictive analytics models and helps resolve such requirements faster.
Data from IoT devices can also automate submission of FNOL. Not only can surveyors assess damages more accurately than insurance, but they can also do so instantly. In car accidents, telematics data on speed, braking, impact direction and more will detail the actual events and speed up decision-making and detect fraud.
The infrastructure is covered by IoT platforms such as Cisco IoT, AWS IoT Platform or Microsoft Azure IoT. You can review our comparison to make a choice.
But you will also have to deal with the cognitive layer – which devices to use and target. The easiest solution would be to establish a partnership with the device manufacturer and offer sensor installation as part of your service. See examples of HSB, Progressive and Hippo.
Hail Damage Car Insurance Claim
In most cases, damage assessment is handled manually at the car workshop and/or by the repairer
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