We recently released a report on the state of artificial intelligence in the insurance sector. It was evident that most insurance companies want to use AI but need assistance to integrate it into their businesses properly. Please watch for future publications that will help you identify the correct use cases and begin implementing AI technologies effectively. In this blog post, I’ll discuss one of these technologies, called computer vision technology (CVT). The reason why I’m focusing on CVT specifically is because even though it’s a recent entrant in the insurance industry, it can transform how insurance companies process claims, identify fraud, and evaluate risk.
Human Vision Serves As An Inspiration For CVT
A branch of artificial intelligence called CVT enables computers and systems to extract useful information from digital photos, videos, and other visual inputs. Computer vision simulates the ability of the human brain to recognize visual information. It trains machines on various visual data using pattern recognition algorithms. After processing the input images, the machine/computer labels the objects and looks for patterns in those objects. Human vision has an advantage over computer vision because it has existed longer. Human sight has the advantage of lifetimes of context that have trained us on how to distinguish objects, determine their distance from the viewer, determine whether they are moving, and whether an image is incorrect. Instead of using retinas, optic nerves, and the visual cortex, computer vision trains machines to perform these tasks using cameras, data, and algorithms.
Effective CVT Use Improves Premiums And Customer Experience
Thanks partly to CVT, insurers can bring operational efficiency, and, yes, cut costs but most importantly also deliver a better customer experience. Insurance companies can use CVT to speed up claims decisions and streamline claims processes by reducing the need for manual evaluations. Moreover, CVT lessens the possibility of human error by being less fallible and more consistent than humans. Vehicle damage assessment, roof underwriting, wildfire risk assessment, and factory surveillance are some use cases for CVT, particularly when paired with aerial imagery from satellites and drones. Insurance carriers are already experimenting with CVT. For example, the auto damage estimator from Liberty Mutual quickly evaluates vehicle damage following an accident and offers repair estimates. And The Hartford and Kin Insurance rely on these technologies for property details such as the size of a residential property, type of roof construction and its condition, the presence of solar panels, whether there are overhanging trees, a swimming pool, yard debris, or vegetation encroachment that could pose a fire danger — and more.
CVT In Insurance: The Future Is Bright
Despite CVT’s many benefits, there are some challenges in operationalizing it. The expense of implementing and maintaining the technology is the most significant barrier. For CVT to be successful, a sizable investment in hardware, software, and training is necessary. CVT algorithms constantly change, necessitating regular technology updates to keep up with business changes. Besides these, there are also challenges in training the algorithms from quality images and videos. Synthetic data for CVT partly solves this problem. In any case, I am optimistic about CVT’s future in the insurance sector. I believe the technology will continue to develop, increasing precision and output. It will likely become more efficient and affordable as hardware and software become more widely available. Connect with me through an inquiry or guidance session to discuss the state of AI in insurance and how you can make the most of CVT.