We are the company that benefits both the clients and the Carriers/Insurance companies.
For the carrier/insurance company:
Automating Underwriting Simplifying the form filling process in underwriting and recommending the desired insurance products based on company profile.
Business Value-Add- Increasing number of clients per agent Reduces the time Improves efficiency Eases the time-consuming process
Technologies Used- Chatbot AI NLP Google Home Conversational UI
For the Clients:
Predictive Modeling Sensors are implanted at the location. A predictive machine learning model is built from the sensor data for alerting the customers with a warning SMS message and an email.
Business Value-Add Dynamic. Automated. Predicts a hazard prior and alerts the customers so evading damage to property. Usage based Insurance (Pay-per-use).
Technologies Used- Machine Learning Algorithms Sensor Data IoT SMS and E-mail APIs
src/MarshRewrite This is the folder where the code for all the algorithms that were used have been uploaded along with the email and sms notification python files.
src This folder consists of the website that we will be using on our side. This gives us all the information about: How well the algorithms are working and highlights which company needs the immediate attention. The variation of each gaseous hazard as a graph. SMS and Email notifications are sent as soon as the components cross the hazard levels.
chatbot This automates the underwriting process that Marsh is currently doing manually. bot.html is the main executable file and index.js and style.css are supporting files.
How to run Run on python3, Hackathon.py. It automatically runs another python script automaticEmail.py and Send_sms.py and Web.py
To check the live dashboard, the website is hosted on this link: https://radhikab41.github.io/predict-hers/