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. Stress testing and scenario analysis were conducted to assess the model’s resilience under various conditions. Key IFRS17 metrics such as Present Value of Future Cash Flows (PVFCF), Risk Adjustments, and Contractual Ser- vice Margins (CSM) were calculated, offering a comprehensive approach to actuarial loss reserving. 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. Stress testing and scenario analysis were conducted to assess the model’s resilience under various conditions. Key IFRS17 metrics such as Present Value of Future Cash Flows (PVFCF), Risk Adjustments, and Contractual Ser- vice Margins (CSM) were calculated, offering a comprehensive approach to actuarial loss reserving.
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. Stress testing and scenario analysis were conducted to assess the model’s resilience under various conditions. Key IFRS17 metrics such as Present Value of Future Cash Flows (PVFCF), Risk Adjustments, and Contractual Ser- vice Margins (CSM) were calculated, offering a comprehensive approach to actuarial loss reserving. 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. Stress testing and scenario analysis were conducted to assess the model’s resilience under various conditions. Key IFRS17 metrics such as Present Value of Future Cash Flows (PVFCF), Risk Adjustments, and Contractual Ser- vice Margins (CSM) were calculated, offering a comprehensive approach to actuarial loss reserving.