Insurance companies routinely conduct assessments to estimate loss reserves, crucial for anticipating liabilities arising from claim settlements. These estimations are particularly sensitive to the temporal dynamics of claims processing, encompassing the duration from filing to resolution. In this study, advanced cross-sectional regression techniques are employed, leveraging Cargo Insurance market data to gauge reported loss reserves. The comprehensive model integrates various influencing factors such as written premiums, paid claims, reInsurance issued premiums, inflation rates, and return on investment. Notably, the analysis unveils a non-significant negative association between inflation rates and loss reserves. Additionally, a negative correlation is observed between paid claims and loss reserves, while a statistically significant positive relationship emerges between written premiums and loss reserves, shedding light on intricate patterns within the Insurance market.