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Facilitating the First Notice of Loss Process Using AI

Facilitating the First Notice of Loss Process Using AI (PDF)

The processing of First Notice of Loss (FNOL) documents, particularly when they contain handwritten reports, can be a daunting task. Insurance adjusters require copies of these documents for claims processing, and the complexity of handling FNOL documents, especially those with handwritten content, can swiftly turn into a bottleneck.

First Notice of Loss

Manually transcribing information from these reports into computer systems is time-consuming, and it often leads to complications. What happens when the handwriting in the report is illegible? And if the report is distorted in some way? What if errors occur during the transcription of handwritten forms into the system?

This article explores how the power of artificial intelligence (AI) through unstructured data processing (UDP) can be harnessed to swiftly and accurately extract crucial information from FNOL documents on a large scale. By incorporating AI to streamline FNOL document processing, insurance providers can achieve the following:

  1. Significantly reduce claims processing costs.
  2. Minimize (or eliminate) manual data extraction.
  3. Drastically expedite claims processing.

But First, What Exactly is First Notice of Loss in the Context of Insurance?

FNOL, which stands for First Notice of Loss, is often referred to as the first notification of loss. It typically marks the initial step in the claims process when an insured individual contacts their insurance company to report a loss, theft, or damage to an insured asset. During the FNOL process, insurers request specific information, such as:

  • Insurance policy number
  • Details of the loss, including location, date, and time
  • Personal information for all involved parties
  • Specifics related to the loss, theft, or damage
  • Documentation or descriptions of the loss or damage

As insurance policies can now be purchased and renewed online, often without direct human interaction, the FNOL report may serve as the first direct interaction between a customer and their insurance provider. Given that individuals are typically distressed after a loss, ensuring a positive, efficient, and smooth FNOL experience is vital.

The Importance of Accuracy in FNOL Document Processing

FNOL documents are crucial for insurance adjusters to process claims. Errors and omissions due to inaccurate or challenging-to-extract information can lead to delayed processing, increased costs, and disgruntled customers. For instance, an incident report with illegible handwriting that reads, “Hit my hand on the dashboard,” might be mistakenly recorded as “Hit my head on the dashboard,” whether due to unclear handwriting or human error.

Such errors not only impact the claims process but could also frustrate customers if they become aware of the mistake. Inconsistent or erroneous First Notice of Loss documents at the intake stage can have a ripple effect throughout the claims processing workflow. Using the earlier example, if an adjuster files a claim for an injured head when it was, in fact, an injured hand, the insurance company may wrongly deny claims related to hand injury treatment, assuming it’s unrelated.

This necessitates the original adjuster to rectify the error, consuming more time and incurring additional costs. Meanwhile, the customer’s frustration mounts, as they’re not promptly and accurately reimbursed for an accident that wasn’t their fault in the first place.

Accurate First Notice of Loss for Customer Satisfaction

Inaccurate First Notice of Loss forms and supporting documents result in slower response times, delays in claims processing, and subpar customer service. According to Accenture, 41% of individuals who file an insurance claim switch to a different insurance provider within a year. Furthermore, customers who have submitted a claim in the past 24 months are almost twice as likely to switch insurance companies within the following year compared to those who haven’t.

JD Power states that the primary reason people change auto insurance providers is poor customer service from the provider. These findings emphasize a straightforward truth: for a positive customer experience, claims must be accurate from the outset.

The Cost Efficiency of Accurate FNOLs

In addition to enhancing customer satisfaction and retention, precise FNOLs also translate into cost savings. When intake forms are accurate from the beginning, adjusters don’t need to spend time identifying and rectifying errors. Inaccuracies can also result in time-consuming and costly litigation. If initial errors propagate, it becomes even more expensive as lawyers may need to litigate cases based on incorrect initial information.

Moreover, distinguishing between a head and hand injury can potentially cost insurance companies tens of thousands of dollars in settlements if the error remains uncorrected.

The Variable and Unstructured Nature of FNOL Documents

Adjusters may need to review initial documents from all parties involved in the loss, including reports from various authorities such as police departments, fire departments, security agencies, park rangers, and other entities. Additionally, other individuals involved in the incident, such as contractors and witnesses, may also submit forms. These supporting documents can be disorganized, handwritten, or lacking a standardized structure in information presentation.

The structure and formatting of these documents, as well as the types of data they contain (text, images, tables, etc.), exhibit a high level of variability and a lack of a discernible structure from one claim to another. While humans are adept at interpreting unstructured information, they tend to be slow, error-prone, and costly. Unfortunately, most conventional document processing solutions, like intelligent document processing (IDP) or robotic process automation (RPA) combined with optical character recognition (OCR), struggle with semi-structured and unstructured documents.

Challenges in Processing and Analyzing Unstructured Data

All the data encountered can be classified into two categories: structured and unstructured. Structured data resembles filling out a form with dropdown menus, where there are predefined limitations on the data points and a consistent structure. In contrast, unstructured data lacks these constraints, as it does not originate from a form or a database with preset fields.

For instance, a customer sending information about a loss via text message constitutes unstructured data. Although it contains valuable information, most software struggles to interpret it. Can your Customer Relationship Management (CRM) system transform a text message into a database entry and draw conclusions regarding the next steps in the claims process?

Police reports are another instance of unstructured data, as they may lack standardized fields across states or even within cities in the same state. Additionally, the information within each field can be unstructured, such as event descriptions or variable formatting, like different state names (CA, California, etc.) or incident codes.

Software, algorithms, AI, and machine learning tools have significantly advanced in deciphering the placement of data. Voice recognition technology in automated customer service lines is a prime example. Advanced systems can detect voice inflections, distinguish between high- and low-pitched tones, and extract keywords from phrases that may not precisely match predefined response options.

This same capability extends to written data and images within a computer system. AI can swiftly examine a scanned document and accurately parse the contained data, routing it to the appropriate recipients or repositories with the assistance of artificial intelligence.

The Evolution of AI in Auto Insurance FNOL

The auto insurance industry has long recognized the value of technology in enhancing customer service and satisfaction. In recent years, the focus has shifted towards leveraging Artificial Intelligence (AI) for more efficient and cost-effective operations. AI holds the potential to automate the First Notice of Loss (FNOL) process, a crucial aspect of claims handling.

Automating FNOL using AI offers several advantages. It enables insurance companies to mitigate risk by gaining quicker and more precise insights into the claims process. Furthermore, AI can enhance customer service by providing prompt and accurate responses to claim inquiries. Additionally, AI streamlines the FNOL process, resulting in more efficient and cost-effective operations.

The Impact of AI-Powered FNOL in the Insurance Industry

The potential of AI-driven FNOL is already evident in the insurance sector. Many companies have adopted AI-driven FNOL systems capable of swiftly and accurately analyzing data. These systems can predict customer behavior, create personalized interactions, improve customer service, and reduce the time required to process claims through AI utilization.

Looking ahead, AI-driven FNOL will become even more robust. It will be capable of handling complex claims, such as those involving multiple vehicles or parties, and provide comprehensive incident information. AI will also be instrumental in more effective data analysis, enabling insurers to assess risk with greater accuracy and detect fraud. The auto insurance industry has embarked on a path toward harnessing AI’s potential for enhancing customer service, streamlining operations, and ultimately delivering a more efficient and satisfying customer experience.

If you’re having trouble filing an insurance claim, this article may be helpful: If My Car Insurance Claim Is Denied, What Should I Do?

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