About Client

A general insurance company sought to improve its assessment of break-in risks associated with auto insurance leads, aiming to enhance decision-making for rejection or acceptance using predictive acquisition.


The client aimed to develop a predictive acquisition model to evaluate the risk of break-in leads and improve underwriting processes.


  • Over 100 variables were created, including factors such as the lag between inspection and lead creation, leads generated during holidays or long weekends, and flags for previous policy break-ins.
  • A rejection model was developed using random forest methodology to score leads based on their risk of fraudulent claims post-conversion.
  • Leads approved at this stage were assigned a policy number.


The predictive acquisition model demonstrated high precision and recall, enabling the client to better assess risks associated with each lead and make informed decisions regarding rejection or acceptance.

  • Performance in predicting rejections:
    • Precision: 79%
    • Recall: 60%
    • F1-Score: 68%
  • Performance in predicting approvals:
    • Precision: 81%
    • Recall: 92%
    • F1-Score: 86%
  • Random Forest was trained on 75% of the dataset, and 25% was reserved for validation. Two-stage prediction models were developed to optimize performance.
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