When providing insurance, there are very few points of contact between customers and the insurance provider. Because of this, AutoInland (an Insurance company in west Africa with a focus on Auto Insurance) needs to make every point of contact count; one of the most valuable of these is filing and settlement of claims.
AutoInland believes that to achieve better service level standards, they need to anticipate future demands in terms of claims request volume. This will allow them to better manage their resources while keeping the customer experience positive and the levels of satisfaction high.
The objective of this hackathon is to develop a predictive model that determines if a customer will submit a vehicle insurance claim in the next three months. This solution will help streamline financial planning at AutoInland and allow them to better serve their customers by understanding which customers are likely to submit a claim.
The error metric for this competition is the F1 score, which ranges from 0 (total failure) to 1 (perfect score). Hence, the closer your score is to 1, the better your model.
F1 Score: A performance score that combines both precision and recall. It is a harmonic mean of these two variables. Formula is given as: 2PrecisionRecall/(Precision + Recall)
Precision: This is an indicator of the number of items correctly identified as positive out of total items identified as positive. Formula is given as: TP/(TP+FP)
Recall / Sensitivity / True Positive Rate (TPR): This is an indicator of the number of items correctly identified as positive out of total actual positives. Formula is given as: TP/(TP+FN)
Where:
TP=True Positive
FP=False Positive
TN=True Negative
FN=False Negative