
1
Achieved 85% accuracy on unseen data, with an MCC score of 71.

2
Reduced probability of lapsing from 16% to 7%.
Client: Leading South East Asia-based insurance provider catering to small and medium businesses
OPPORTUNITY: The client wanted to develop a model to predict lapses at the policy level. The aim of the model was creating cost-effective interventions to target high-value customers with high likelihood of lapse.
SOLUTION:
SGA decided to design a classification model to predict probability of lapse, using the following approach:
VALUE DELIVERED: