Loss Reserving: An Inflation-Adjusted Model for Claims Provision in General Insurance

This internship report will provide an extensive summary of my time spent working as an intern actuarial analyst in the Financial Services team at KPMG Portugal, with the focus on "Loss Reserving: An Inflation-Adjusted Model for Claims Provisions in General Insurance." KPMG Portugal is an expert company in offering services related to audit, tax, and consulting. The branch of Financial Services is dedicated to risk management, regulatory compliance, and actuarial services. The profession of actuarial uses actuarial techniques comprised of statistical and mathematical models, actuarial analysts forecast and estimate future liabilities to ensure clients' financial stability and regulatory compliance, being used in many decisions, giving its clients the power to mitigate risks, and achieve long-term financial sustainability. Due to its importance and real-world relevance in the insurance sector, a non-life insurance/general insurance related project was the focus of my project. This model was focused on claims reserving, using statistical methodologies to obtain claims provisions, showing the expected future payments of past claims and their financial impact. Insurers must comprehend how inflation affects future payments of past claims to properly set aside reserves, mitigate risks, and establish effective pricing tactics. Claims provisions are obtained using sophisticated computations that take into account five distinct approaches and use the three inputs, those methodologies are Chain Ladder, variants of the Link-Ratio deterministic, Grossing Up Factors, and Grossing Up Worst Factors. Additionally, the model incorporates inflation, yielding a more precise measurement. This model improves the accuracy and dependability of actuarial forecasts, demonstrating the role that precise claims provision calculations play in risk management and regulatory compliance. By looking at claims provision with an inflation-adjusted viewpoint, we can not only fill a significant gap in current actuarial methods but also support the overall aim of enhancing financial forecasting precision in the insurance sector. The model was intensely tested for statistical significance during the process. The model was later fitted with an inflation adjustment, taking into consideration past and future inflation for more efficient and practical results, after careful considerations and testing. The future inflation was obtained using ARIMA forecasting and Exponential smoothing forecasting, with the help of R-Studio and Microsoft Excel using time series analysis. In addition, the report mentions some of the nature of actuarial work at KPMG, emphasizing the importance of teamwork and communication with other teams, such as finance, compliance, and data analytics. Additionally, the report discusses the link between aligning actuarial assumptions with dynamic market conditions and the importance of maintaining data integrity.