Marketing Intelligence Assignment

Client Needs & Objectives

Context:

  • Massive clients databases are not fully exploited
  • The use of a machine learning modelling enables to offer additional options and products depending on clients’ characteristics
  • A good modelling of customers’ specifications allows for efficient targeting and high probability of complementary options underwriting

Objectives:

Exploitation of customers databases in order to refine the insurance offer of products and complementary options according to clients’ profiles

Scope:

  • Insurance policies proposals
  • Fraud detection
  • Credit risk

Techniques:

  • Logistic regression
  • Neural network
  • Random forest

Our Approach

  • Segmentation of the database in 2 sub-bases: the first sub-base as a learning tool and the second one for model testing
  • Initialization of coefficients and calibration of parameters via machine learning modelling in the learning base to optimize the predictive power on the test base
  • Extension of the model to the entire base
  • Exploitation of the data with different machine learning models
  • Selection of the best model through cross-validation
  • Focus on the three products or additional options  the most likely to be underwritten

Main Deliverables

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