Applying Image and Video Analytics to Insurance
Sep 7, 2022
In these times of large volumes of data processing, businesses must be intelligent about extending analytics tools to determine valuable insights about their consumers. Image recognition systems are already prevailing in many enterprises.
Application of Image and Video Analytics in Insurance
1. Real-time Damage Assessment
When an assessor evaluates a damage claim, it can sometimes use one to four weeks for the assessor to attend to, examine and appraise the charges for the damaged object. Some insurance companies have turned towards adopting the technology for automated damage reports. The owner can take and forward pictures shortly after the incident to the claims administrator. The compensability of the losses can be determined based on the detected/missing parts. This information about repair expenses and logistics can be given to both the insurer and the object owner. The analytical technology is expected to decrease the method significantly from many weeks to a single day.
2. Use Drones to Take Photos of House Roofs
The application of drones in Property & Casualty insurance will soon become the conventional procedure for pricing, examination, and damage evaluation. A drone can take hundreds of photographs in 10 to 20 minutes for pricing schemes. For example, after a hurricane, roofing contractors are engaged quite speedily, so it will take numerous weeks to have one to probe your roof.
The application of drones presents agility and assistance. Also, this method is more reliable for insurance company representatives to alleviate the uncertainties of claiming roofs. Insurance companies have revealed apps that empower an insurance representative to take digital photos of a home and present the images back to the insurance operation, which offers a comprehensive risk assessment statement. The policyholder can utilize the same app for interior examination.
3. Radiological Imaging Diagnostics
Some preeminent healthcare organizations are starting to apply deep learning to distinguish pathologies in radiological concepts such as bone fractures and conceivably cancerous lesions. While the purpose is not to substitute a radiologist, the method points to efficiency by eradicating the need for humans to do tiresome and time-consuming tasks. For the most part, many reliable systems are presently on par with individual performance and are practiced only in active research environments.
Challenges in Applying Image Recognition in Insurance
1. Accuracy Level
Employing image recognition in the insurance enterprise still faces many difficulties. The most significant factor is the precision level. Presently, the highest accuracy rate is around 90%, a minute error in image disclosure can cause the enterprise to handle a complicated legal challenge and customer discontent. On the other hand, a misleading claim that the machines neglect to discover may incur considerable financial losses.
2. Regulatory Concerns
Insurance rates are not sanctioned to be extravagant, inadequate, or unreasonably discriminatory. Some regulations prohibit bias based on age, nationality, or sexual orientation. Some image information may be found inapplicable for insurance submission portal valuations and claims. Even when no evidence raises regulatory attention, the citizens still want to know what varieties of data are derived from the images and what algorithms are practiced. Such regulatory matters and reputation uncertainties should be analyzed before moving forward.
3. Data Security
Data can be replicated, processed, and used for nefarious purposes by hackers. For example, if facial identification is used to recognize a customer, the domain can be infiltrated so the hacker can locate private data. To moderate such risks, every business needs to embrace a modern security program that helps distinguish the real threats. An enterprise should have a companywide information security policy beyond the IT service. This strategy involves employee exit tactics (HR), project protocol, on- and off-site information storage manners, instruction, policies, and procedures.
Conclusion
Additional developments are required to augment model accuracy, so the models can enhance users’ analysis and decision planning. At the same time, image identification provides many possibilities for insurers. Outfitted with both industry expertise and technical abilities, insurers can help join image recognition with a risk evaluation and decision-making in a significant way. They can assist in composing image recognition models that can explain more complex insurance-related matters and help authenticate image recognition outcomes using existing prototypes based on alternative information sources.
