The process of rating and pricing risks have evolved from essentially cost-based models to more sophisticated algorithms leveraging various parameters. Today, machine learning, new sources of data, and pricing optimization techniques allow insurers to differentiate their tariffs and be more reactive to market changes.

In a highly competitive market, Bulstrad decided to enhance its rating strategy and process. Through its machine learning pricing and rating engines initiatives, the company has become more efficient and more responsive to market changes. Bulstrad now provides online rating capabilities to agents and underwriters and increasingly to policyholders, leveraging algorithms trained on abundant data, allowing for motor insurance portfolio optimization and quick response to competitor product strategies and dynamic market conditions.