Risk Brackets and Data Insurance companies spend a large part of their effort on calculating the rates to give each customer based on their insurance needs. A key component of this effort is calculating the customer’s risk factor. Because different people may use their insurance plans more or less than others, insurance companies calculate each customer’s risk and modify their rates accordingly. This means that when two people both want plan “A” for insurance, the company looks at their driving history, accidents, and other factors to determine their “premiums” or how much extra they need to pay to offset their risks … Continue reading Case Study: Insurance Risk Prediction

Problems with current retention models Customer retention is the act of keeping customers paying and using your services. For subscription based business models like phone service providers this area of focus is responsible for a significant amount of their profit. Customer acquisition is expensive and difficult, but customer retention is typically easier and ensures that the customer is paying for longer, making each customer acquisition more and more profitable as they stay longer. Typical retention programs are informed through the work of multiple data scientists and several years of statistical calculation and model building. This method of human engineering is … Continue reading Case Study: Customer Retention

OneClick.ai supports the easy exporting of any model created through use of our AI to your external API uses. This paper will go over how to export our models as an API. This assumes you have already read the OneClick Platform Walkthrough After the model training process, on the model review page, there is an option to enable API for a each model. Upon clicking that button you can then select to activate or deactivate the API. Only one API can be active at any given time for trial accounts. The tutorial project always has an API running. This cannot … Continue reading Accessing Models through API