for item valuation in
the insurance industry
An essential but challenging task for insurance companies is determining the value of various items. The value determination itself can play a role in deciding the amount of insurance costs or be used in the event of loss or damage to an item.
Insurance is an industry that is highly dependent on accuracy and precision. Trust in a factual and accurate valuation of the item is of paramount importance to a customer seeking to insure a valuable item. Evaluations, however, can be fraught with difficulty when there are many parameters to consider or when information is lacking. By using Deep Learning in valuation processes, insurance companies can efficiently and instantly analyze images and provided details and combine them with historical data from previous valuations.
The opportunity for deep learning
With Deep Learning, it is possible to summarize data and find patterns in it that are difficult or impossible to see with the human naked eye. It also opens up the possibility of combining and drawing conclusions from different types of information, such as combining images and tabular data. Once a deep learning model is trained and ready to use, it can identify exactly what it should focus on in the incoming images and tabular data to provide the right result.
Platform model to use
Based on what needs to be evaluated and how to perform the evaluation, we recommend either table data regression, image regression, or a combination of both.
How does the model work?
Like any modern Deep Learning, it takes a bit of experimentation to find the optimal solution for the problem at hand. We will focus on using both data sources. To understand and extract information from the images, a conventional neural network is used, and to find relevant features from the table data, a fully connected neural network is used. Both models are available as pre-built models at zerocode.ai.
Normally, combining different networks for different data types is a challenge only for advanced Deep Learning users. Thanks to zerocode.ai's intuitive interface, this can be done in a few seconds without any programming knowledge.
To train the model, historical data with images and their actual rating are required.
How much data is needed is hard to say without trying it out, because it depends on the task and the variety of data points. A good starting point is a table with at least 100 data points.