Insurance, traditionally, has been slow to adopt new technologies. However, with the application of AI/ML and Hyperautomation, the industry is now on the verge of a seismic, tech-driven shift. Infusing AI/ML, especially during critical scenarios like property damage assessment during a catastrophe, has completely revolutionized the entire process by making it faster, more accurate, and more efficient. Consider a scenario when a natural disaster like a hurricane hits a sudden surge in property damage claims. Traditional on-site inspections and manual processes may not be adequate for insurers and claims adjusters to manage the high volume of claims, making it almost impossible to quickly assess and estimate losses accurately, especially when the surveyors and other staff are limited.
That’s where AI/ML-powered solution holds promise. Insurers or adjusters can send a link to a mobile app that allows affected parties or to take photos of the damaged property or other assets, and AI/ML engine quickly does a thorough damage assessment, and quickly sends repair estimates in the form of loss reports. More importantly, the loss reports can be analyzed further by AI/ML technology to detect human errors and recommend solutions as well.
Navigating Challenges in Damage Assessment and Loss Calculation
Property damage assessment and loss estimation are the cornerstones of property insurance claims processing. These are critical aspects for insurers and adjusters to provide accurate and prompt service to policyholders. Traditional methods require assessors to manually inspect properties, assess the damage, and estimate repair costs. This approach, unfortunately, is both time-consuming and error-prone as, most of the time, they are paper-based processes.
However, this is not the only challenge that insurers face in the process:
#1: Inaccuracies in the loss estimation reports provided by surveyors due to the possibility of human errors.
#2: Accurate loss estimation report requires meticulous evaluation of various inputs, including owner data, inspection requests, and property information. Any error or oversight during these steps can significantly impact the accuracy of the final report.
#3: The sheer volume of repetitive and mundane tasks, coupled with unstructured data, only adds to the complexity of the process.
#4: Allocation of the right adjuster type for a particular damage type also requires to be streamlined to minimize the cost and improve the speed of inspection.
Key Benefits of Automating Damage Assessment
The combination of AI/ML is fundamentally altering how insurers handle the most vital aspects of the claims settlement process. The umbrella of new-age technologies enabling insurers to inculcate efficiency at each level of claims processing, helping them provide a seamless customer experience:
Here are some key advantages it offers:
#A: Faster Claims Assessment & Processing
The property damage assessment and loss estimation process are complex and usually involve manual data entry, physical verification, and a high degree of subjectivity.
AI-powered algorithms, such as image data processing, AI annotations, computer vision, and predictive analytics, enable insurers to quickly and efficiently analyze large volumes of data and identify patterns and anomalies. Frauds. Thus, they can make informed decisions on damage assessment. With AI-powered tools, insurers can reduce the claims settlement cycle from weeks to days with minimal human intervention.
#B: Maximizes Cost Savings
Combining AI/ML, predictive analytics, and automation can improve property loss estimation and damage assessment activities, enabling insurers to speed up claims processing, increase operational efficiency, and reduce errors and fraudulent activities. The automated allocation of adjusters to properties can also minimize the allocation time and reduce the cost-to-serve. Overall, these benefits can translate into significant cost savings for insurers, enabling them to offer more competitive pricing and better customer service. They can stay ahead of the curve and deliver policyholders a more efficient and reliable service.
#C: Increases Accuracy With Better Risk Assessment
Using AI/ML can provide a more precise evaluation of underwriting risks, analysing various factors like property damage risks based on location, building materials, and weather patterns. It enables insurers to make informed decisions and offer more effective risk management solutions to their clients.
Powered with computer vision and AI/ML technology, insurers can better detect damages, dents, and breakages using a 360-degree overview of images or video feeds. AI/ML engines can more accurately evaluate the extent of damage, recommend repairs, generate loss estimation reports, and declare a total loss.
Furthermore, advanced AI/ML models can identify the impact and severity of damage and calculate a detailed itemized estimate for repairs. These models can quickly analyze vast amounts of structured and unstructured data, including images and videos, to accurately determine the extent of the damage and create a comprehensive estimate of the repair costs.
#D: Improves Adjuster’s Credibility With More Accurate Loss Estimation Reports
The insurance industry relies heavily on trust, and clients rightfully expect accurate and efficient claims processing. Any error or discrepancy in loss estimation reports may lead to multiple reviews by QA teams, site revisits, and disagreements between insurers and claimants, eventually resulting in the loss of credibility among customers. With the help of Machine Learning (ML)-powered solutions, it is possible to validate the accuracy of reports in minutes quickly, making it easier for adjusters and carriers to prepare fast and accurate loss estimate reports. This not only boosts the productivity of QA teams but also increases the market reputation of insurers.
#E: Enhances Customer Experience
Claims settlement is a critical moment for Insurers as the experience they deliver determines customer loyalty and trust, which also influences referral customers in the long run. As products and pricing in the insurance industry are almost identical, customer experience can be the main differentiator.
Integration of AI/ML has the potential to change the way the claims process is managed, resulting in significantly improved efficiency, speed, and accuracy. From initial loss notification to damage assessment, AI and automation can enhance the customer experience at every stage, making it faster, more convenient, and hassle-free.
The Crux
The collaboration of data, AI, and ML, coupled with deep learning techniques, is unleashing a new era of growth for the insurance industry. With these game-changing technologies at their disposal, insurers can tackle the challenges of loss estimation and damage assessment while providing their customers with a seamless and satisfying claims experience.
Case in Focus
One of the leading independent insurance adjusting firms in the USA faced challenges owing to incorrect data provided by surveyors and other human errors. The reports often lacked accuracy in identifying house roof material type (Hip, Gable, and Shed) and type of roof shingles. The AI roadmap presented by Damco resolved this challenge by precisely pinpointing errors in the identification of roof material. As a result, their claims processing speed increased, resulting in an improved claims settlement experience for claimants.
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