The Innovative Development of the IFRS17 Formulated Brighton Mahohoho Inflation-Adjusted Automated Actuarial Loss Reserving Model: Harnessing Advanced Random Forest Techniques for Enhanced Data Analytics in Fire Insurance

This paper presents the development and implementation of the IFRS17 Formulated Brighton Mahohoho Inflation-Adjusted Automated Actuarial Loss Reserving Model, lever-aging advanced Random Forest techniques to enhance data analytics in fire insurance. The methodology encompasses the simulation of synthetic fire insurance data with key variables such as claim frequency, severity, and inflation rates. Exploratory Data Analysis (EDA) and data visualization techniques were employed to assess relationships and trends, aligning the model with IFRS17 compliance standards. Random Forest regression models were developed to predict claim frequency, severity, and inflation adjustments, integrating these predictions to estimate future loss reserves. Robust evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), ensured model accuracy.