Why Machine Vision?
Machine Vision is on its way to cause a major disruption in the insurance sector. Shrinking of insurance revenues and decline of underwriting profits are on the rise because of which more and more insurance carriers are focusing on Machine Vision based tools/framework across business applications. The field of machine vision is constantly evolving. High-value predictions that can guide better decisions and smart actions in real time without human intervention is the need of hour.
Machine vision as a technology helps analyze large chunks of data through automated process and is gaining a lot of prominence and recognition. Machine vision has changed the way data extraction and interpretation works by involving automatic sets of generic methods that have replaced the traditional statistical techniques.
Significance of Data in Machine Vision
Implementing machine learning into business operations can be frightening, but it is now necessary to move business ambitions forward and data is the key. Without data, there is very little that machines can learn. Besides collating the data, equally important is data annotation because it makes the work of the machine vision program much easier.
Data being the heart of insurance industry, has always played a key role, and today, carriers have access to more of it than ever before. With so many data sources emerging on a day-to-day basis, processing of the data and deriving analytical insights from it are the only task left. With the help of data, machine vision is capable to provide predictive insights that will help the insurance carriers in better decisions-making and take smart actions in real time. This will help the insurance carriers in maintaining their competitive edge, while boosting business operations and customer satisfaction, and solving business challenges across the value chain.
Transformation of Insurance Industry through Machine Vision
Machine vision offers the ability to automate, scale, and enhance risk evaluation while seeing gains in operational efficiency and cost reduction. Insurers now have access to an unprecedented quantity of image and video data. The carriers are beginning to invest in machine vision technology to process this data, programmatically analysing risk factors and making sense of these vast image stores. Machine vision represents the leading edge of AI. Since insurance has always been data- intensive, it is perfectly poised to be significantly impacted by AI.
Machine vision help insurers automate, scale, and enhance risk evaluation while seeing gains in operational efficiency and cost reduction. It will enable insurers to redefine how they should work, how they should create innovative products and services, and how they should deliver customer experiences. Machine vision will allow insurers to redefine existing processes, create innovative products, and transform customer experiences. Machine vision is going to unlock trapped value in new and existing datasets, leveraging the data by creating ways across the entire value chain.
Changing Insurance Perception via Machine Vision–Potential Use Cases
Underwriting is a key area in Insurance business. Effective underwriting requires both selecting and classifying risk and charging right rate to cover related expenses while earning reasonable profits. Machine vision helps in consolidating insights from images whether originating from massive volumes of highly varied data such as satellite images to find attributes the insurer may be interested in. Accordingly, basis data, risk can be assessed leading to cost reduction, higher quality of care, and fraud detection.
Cognitive Claim Processing
Machine vision recognize the damage and facilitate the claim in lesser time with more accuracy. Based on the machine learning algorithm the images of the damaged subject matter are analysed, the severity of the damage is assessed and accordingly the findings including the claim amount is calculated. Not only this, it also predicts the future premium.
Analysis of Documents
In insurance, documents are generally filled out in a person’s handwriting by different users across the value chain. These handwritten documents are often typed for further processing which is time-consuming leading to huge operational cost. Moreover, it might cause chances of errors that might trigger underwriting losses. With Machine Learning algorithms, patterns in handwritten documents can be analyzed leading to saving of huge operational cost, better quality, and faster turnaround.
The price of a building depends on various factors i.e. location, type of building, number of floors, and other factors. Based on the satellite images and other images (as a part of pre-inspection process), the machine vision algorithm checks the properties of the house and generates the optimal price for this building. This price can be used, for example, to optimize the offers in price comparison pages.
Any defect in the parts whether of home appliances or in factory/workshop can lead to major loss. With the help of machine vision, the images can be captured from time to time and applied to machine learning algorithms. This could identify defective parts ahead of time and prevent losses at an early stage. Machine vision is one of the best risk management techniques.
Fraud Detection in Claims Processing
Customers send insurance claims reports as text documents or pictures. Fraudsters assume that particularly low-value damages are not thoroughly examined. Neural networks are used to identify and filter out patterns of past fraud cases or accumulations of conspicuous, current damage reports. The selected damage reports are reported to the claim handlers and checked manually by insurance fraud specialists.
Machine Vision–The Roadmap Ahead
Machine Vision has already started penetrating in the insurance sector. Whenever the possibilities of Artificial Intelligence are explored in the Insurance space, Machine Vision is an inevitable choice for the carriers. However, for machine vision to run smoothly, the availability of image data is something that sometimes bring challenges. Due to this reason, machine vision is not welcomed openly by all the insurance carriers. Hence the insurance space has a relative lack of case studies for this type of AI software. This may indicate a lack of demand from the insurance industry or a lack of traction on the part of the technology’s use in insurance. However, Machine Vision is going to have a huge impact on all aspects of the insurance industry across value chain.