Artificial intelligence is the frontrunner of all technological innovations today. Starting from agriculture to cosmology, from small mom and pop stores to multi-million dollar retail chains, AI is making all our endeavors more effective and precise. For many industry verticals, the application of AI is solving problems that did not have solutions until now. One such industry where the AI solutions seem to have found perfectly complementing problem statements is P&C insurance. Custom AI in Insurance is one of the most innovative applications of this new genie that mankind has developed.
The use of custom AI models in the P&C insurance industry is gradually becoming a quintessential part of assisting insurers in the cumbersome process of underwriting, customer service, claims settlement, policy renewal, and fraud detection.
The applications of AI in Insurance
The entire value chain of the insurance industry is undergoing a mega-disruption through the implementation of AI.
According to a survey conducted, 79% of insurance executives believe that AI will change the way carriers interact with policyholders.Accenture Newsroom
Major insurance segments like underwriting, policy renewal, risk management, and claims settlement are also settling into a different Modus Operandi.
The biggest implication of AI in Insurance will be the automation of data collection and analysis. Insurance carriers rely heavily on the ground-truth of the properties and assets they insure, so much so that the bottom-line is decided by the accuracy of data one is able to collect. Traditionally, data collection has been done through physical site visits and inspections whether it be for underwriting, policy renewal or settling of claims. However, with the help of geospatial imagery and custom AI, this process can be automated to reap required property insights and data within a matter of a few minutes and with an accuracy as high as 98%.
Below is a brief overview of how AI is influencing the different aspects of P&C Insurance.
Underwriting and Quoting
One of the biggest applications of AI is in preparing quotes within minutes using updated property insights from aerial imagery. Risk assessments are simplified as field visits are not required anymore. A custom AI model can process multiple data sources and provide all the relevant property attributes required for policy underwriting.
Policy renewal involves knowing the changes a property has undergone in a given period of time. With the use of aerial and satellite data, the custom AI model can provide these temporal changes within minutes. There is no need to carry out lengthy site inspections before renewing a policy for your customer.
Roof damages, post-fire status, or the changes in the material features of a property are some of the parameters that insurers find useful to assess claims. Custom AI models not only provide damage scores and severity index, but also a comparative study of a given property in two different temporal states. This helps insurers to detect fraudulent claims and also to access reliable data while understanding risk patterns.
AI technologies used in P&C Insurance
AI is a vast field that has numerous segments with different scope and applications. Understanding these segments and their uses is a prerequisite for ascertaining how AI can be utilized in a given industry. To understand this below is a small introduction to the major AI segments that are used in P&C insurance.
Convolutional neural networks (CNNs)
A convolutional neural network is a category of deep neural networks which is primarily used to analyze visual imagery. These have found applications in image and video processing, medical science, natural language processing, and financial solutions. In P&C insurance, these are used to process aerial and satellite imagery to extract data and insights.
CNNs are generally used to perform the following tasks–
Semantic segmentation, or image segmentation, is the process of clustering parts of an image together which may belong to the same class. It is a form prediction at the pixel-level as the categorization of the pixels is defined. In P&C insurance, this AI technique can be used to make predictions of objects in aerial imagery.
Object detection involves detecting objects of a certain class or category in a digital image or videos. It is one of the most useful image processing techniques. Object detection is used for face-recognitions, pedestrian detection, tracking objects, etc. In P&C insurance, this AI technique can be used to identify various property attributes and objects like solar panels, HVAC systems, swimming pools, trampoline, etc.
Artificial neural networks (ANNs)
An artificial neural network is designed to simulate the way the human brain analyzes and processes information. The data structures and functions of these neural networks try to replicate the associative memory in a biological brain. ANNs are used for speech recognition, social network filtering, medical diagnosis, etc. In P&C insurance, this AI technique can be used to perform image processing and feature extraction.
Outputs which custom AI generates for Insurers
Remote property intelligence is an innovative use of geospatial imagery and AI to identify property features and attributes. Roof surface area, roof material, roof size, roof type, number of solar panels, and many other geospatial attributes related to a property are extracted by specially trained custom AI models. The data thus generated is fed into a number of insurance workflows.
Change detection using custom AI models and geospatial imagery involves measuring the differentials of property attributes between two or more time periods. Change detection is used to perform a before-vs-after analysis for a given property or area of interest to understand the magnitude of effects a particular factor might have on property features. These factors range from disasters and daily weather changes to artificial agencies. The resolution of details that can be obtained through change detection is considerably high. For example, through this method, one can even detect remotely whether a couple of tiles are missing from the roof of a property.
The challenges in building custom AI for your insurance carrier
Building an AI for your business is not an easy task given the kind of resources and expertise it requires. Also, the uniqueness of the pain points of each organization makes it difficult to develop a standardized module of AI. Customization is one of the core problems. To this end, organizations usually hire a team of Data Analysts, Computer vision experts, and engineering candidates to build an in-house AI model. However, the return on investment of this effort may not be very straightforward unless planned the right way. Major challenges that carriers need to overcome are highlighted below.
The other alternative is to outsource all the efforts to an insurtech company. Usually, these companies have standard off the shelf models and may develop original custom AI models at steep prices. Thus, this road has its own obstacles and the major ones of these are as follows.
So, how does one go about building custom models?
Building a custom AI for insurance applications is difficult but with the right planning and direction, it can be achieved in a very short time. To help you in this process, below are three simple steps to guide you and streamline this process of getting a custom AI model for your carrier.
3 steps to build a custom AI model for Insurance
1. Planning the AI solution
- Before you can build the right custom AI, you need to explore and understand where AI and computer vision can be applied in your firm’s scope of activities to gain advantages and speed up the process as a whole.
- If you are trying to outsource the development process, enlist a trustworthy vendor that can help you build the right solution without any bulky pricing schemes.
- Create a roadmap along with timelines marked on it so as to monitor the AI development
2. Developing AI technology
- To develop a more coherent and relevant custom AI model, you may have to collate disparate data sources like geospatial imagery, historic appraisal data, and other third-party data.
- Once the datasets are conjoined, mapping, labeling and mapping of data points are to be carried out.
- Training of the custom AI model and development is to be done to ensure that the model will process the data as per your needs
3. Deployment of custom AI model
- Ensure the model has been trained to provide results that are highly accurate before deployment.
- Test your custom AI model on beta projects with the designated user teams
- Before deployment, keep iterating and refining the AI model to achieve the highest level of accuracy possible.
The right way to build custom models for your carrier
Building a unique and custom AI can be a very daunting task. Outsourcing the entire process also isn’t free of hassles. In response to this conundrum, we have chalked a way out for you. Attentive AI is dedicated to building highly accurate custom AI models exclusively for you. Contact us to know how you can plan your AI roadmap in the most efficient and cost-effective way.