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INSIS AI Life Underwriter

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Overview

  • Machine learning- based solution
  • Can be integrated with existing policy management and underwriting systems
  • Can be implemented as part of an algorithmic underwriting system with having incorporated our mortality risk estimation model as primary risk-driving engine
  • Native component of INSIS policy Management System
  • Out-of-the-box with INSIS Core Insurance Platform
  • Supports standalone implementation (can integrate within existing enterprise system landscape and operational business processes)

    INSIS Machine Learning Framework: handles the entire core insurance model logistics autonomously. The ML pipeline is designed for scalable, high-performance core insurance machine learning tasks. Allows the automation of machine learning models and algorithms to optimize core insurance business processes.

    Machine Learning Framework Implementation: quantified ML implementation with ML Test Score to be measured and improved during implementation and over production time. To

    Proprietary Framework: raw format data can be stored to be subsequently used for training models; multitenancy; allows models to be built automatically; input and output data can be shared to independent applications (consumers) on premises or in the cloud; performance can be evaluated and monitored; models can be compared with a baseline; models can be deployed with ongoing validation of performance; stage models into the production system for testing without disturbing system operation; can handle hot hand-offs to allow seamless integration of new models in production; automated fallback; better model management guaranteeing that the models are functioning in their allocated document environment.

    Model management framework: provides implementation, focused on generating value from Big data without high IT investment.


Key Features

  • INSIS Mortality Risk Estimation System
  • INSIS Underwriting Rule Inference System
  • INSIS Product Recommendation System
  • INSIS Case Prioritization System
  • INSIS Application Approval Prediction System
  • INSIS Document Classification and Information Extraction System

Key Benefits

  • Reduced time to issue
  • Increased customer acceptance
  • Reduced operational process and handling costs
  • Reduced number of queries for sales and applicants
  • Increased consistency of risk assessment
  • Easy development of additional sales channels
  • Higher conversion rates to spur new business
  • Reduced claims

Product/Service details