As an actuarial analyst, the report establishes a defensible, data-driven framework to predict claim frequency for a motor portfolio, enabling pricing, underwriting, and portfolio steering decisions to be made with evidence rather than intuition. With assumed Ni∼Poisson(Viλ(Xi)) with exposure Vi​ as an offset and covariates x={weight, distance, age, carage, gender}. The work delivers validated rating relativities for pricing, risk segmentation that underwrites can act on, and an operational High-risk flag with a documented threshold. Together these support tariff setting, portfolio monitoring, fairness checks, and communication with non-technical stakeholders.

As an actuarial analyst, the report establishes a defensible, data-driven framework to predict claim frequency for a motor portfolio, enabling pricing, underwriting, and portfolio steering decisions to be made with evidence rather than intuition. With assumed Ni∼Poisson(Viλ(Xi)) with exposure Vi​ as an offset and covariates x={weight, distance, age, carage, gender}. The work delivers validated rating relativities for pricing, risk segmentation that underwrites can act on, and an operational High-risk flag with a documented threshold. Together these support tariff setting, portfolio monitoring, fairness checks, and communication with non-technical stakeholders.