Article | “Machine Learning – Insurance Applications Use Cases”

Source: Vitalflux | December 16, 2018 Author: Ajitesh Kumar In this post, you will learn about some of the following insurance applications use cases where machine learning or AI-powered solution can be applied: Insurance advice to consumers and agents Claims processing Fraud protection Risk management Insurance Advice to Consumers: Machine learning models could be trained to recommend the tailor made products based on the learning of the consumer profiles and related attributes such as queries etc from the past data.

Such models could be integrated with Chatbots (Google Dialog flow, Amazon Lex etc) applications to create intelligent digital agents (Bots/apps) which could understand the intent of the user, collect appropriate data from the user (using prompts) and use the underlying model to recommend the tailor-made products.

Alternatively, traditionally speaking, consumers could be asked to provide their details using inquiry form and the form submission part of applications could invoke the model to get the recommendation for tailor-made products.

The following represents a quick application/technology architecture covering different components of applications including machine learning models deployed on AWS infrastructure:   Insurance Advice to Agents: Machine learning models could be trained to recommend the tailor-made products to agents in relation to health, home, commercial etc to provide accurate information to their clients.

These become more useful when there are new products under a different category and it gets difficult to train all of the agents working in different locations.

A Chatbot application integrated with a machine learning model trained to recommend an appropriate product (especially new ones) based on consumer queries would prove very handy for the agents.

The above diagram represents a quick application architecture covering different components including machine learning models.

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