Please watch the following extended video to see how Butterfly AIs significantly improve and simplify the decision making processes within insurance industry while reducing cost and time to market, presenting two examples of health insurance application and car insurance claims (22 minutes):
A brand new next generation AI-powered service helping insurance companies and their underwriters and decision experts to make binary and multi-class decisions by performing binary or multi-class predictions and classifications for example or sanity checking their existing predictions/classifications. By doing those, Butterfly AI will assist insurance underwriters to automate the process of loss estimation and risk evaluation while reducing underwriting workflows. It can help by analysing the risk associated with customer profile and to reduce the repetitive tasks.
Some more examples:
1. Predict if a driver will involve in an accident and file an insurance claim: Butterfly AI can help auto insurance companies assess the risk level associated with each driver (e.g. a new applicant). Knowing the key data features of thousands of past customers’ profile and journeys including number of past insurance claims, the number of times they have been in 500 m radius of an accident when it happened while driving during past year, the top three most frequent travel routes, number of accident hot spots within the top three routes, age, gender, number of years the driver has held a driving incense, past speeding fines, number of license penalty points, highest speed limits across driver’s most regular commute routes, travel regularly in early mornings (yes or no), travel regularly late night (yes or no), the weather status across the regular routes during the usual times of commute and during those past recorded journeys (rainy, cloudy, windy, lightening, storm, extreme cold and etc.), the lighting status across the top travel routes, the make of the car, the number of similar car models involved in accidents in recent months across the driver’s regular travel routes, history of driving under influence of alcohol, the known distracting factors that led to past accidents across driver’s regular travel routes (for example a direct blinding sunlight on the front screen during specific time of the day in parts of the regular routes or sudden black ice during the cold season on the road across those regular travel routes, etc.), MOT health score, millage on the car, number of breakdowns in past months, smoker, eyesight score, heart disease history, stroke history, high blood sugar, high blood pressure, diabetes, medication taken that may make driver drowsy or lose concentration, number of drivers with similar age, license, travel pattern and health status to the target driver who have been involved in accidents across target driver’s top travel routes, and having verified, labelled and recorded whether those past target drivers during those recorded journeys were ultimately involved in any accident or not (the target and label of the prediction), predict whether a new insurance applicant or existing customer (driver) will involve in an accident or not (and present the probability and certainty) provided that the data features (describe above) are available for that insurance applicant.
and any other binary or multi-class insurance classification or prediction use cases.