Every year, a major Indian insurance provider faced the same costly problem, customers letting their policies lapse without warning. These unexpected dropouts hurt both revenue and customer relationships. Traditional methods couldn't predict which policies were at risk until Data Science provided the solution. By analyzing customer behavior patterns with supervised machine learning and propensity models, the insurer gained something remarkable: the ability to spot potential lapses before they happened. The system identified high-risk customers and even suggested personalized retention strategies for each case. Now, instead of reacting to cancellations, the company could proactively save policies contacting the right customers at the right time with the right offer. Watch the demo to understand the case better.
