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
Keywords:
origin of life, energy dissipation, entropy production, constrained dynamic optimization, efficient decentralized decision making, stochastic dependence, stochastic processes, alternative for time series financial models, parameter dependence method of construction, k-Markovianity., double slit, pattern evolution, Particle pattern, Transition pattern, Non-interference pattern, non-parallel double slit, hybrid pattern, curved-double slit, Arc interference pattern, Point-symmetry interference pattern, nonparallel-curve-double slit, Butterfly-shape pattern, Optical Butterfly phenomenaAbstract
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.
References
L. Breiman (2001) Random forests. 45(1), 5-32.
R.J. Hyndman, A.B. Koehler (2006) Another look at measures of forecast accuracy. 22(4), 679-688.
(2019) IFRS 17: Insurance Contracts. [URL]
R.J. Meyer, et al. (2021) The role of machine learning in insurance. 88(1), 75-98.
A. Smith, B. Jones (2020) Impact of IFRS 17 on actuarial practice. 34(2), 122-135.
A. Tay, R. Sembiring (2020) Data analytics in insurance: The future of actuarial science. 43(1), 22-38.
R. Scarth, S. Jain, R. Roberto (2020) A practitioner's introduction to stochastic reserving.
R. L. Bornhuetter, R. E. Ferguson (1972) The Actuary and IBNR. 59, 181-195.
C. Czado, T. Gneiting, L. Held (2012) Predictive Model Assessment for Count Data Models Based on Probabilistic Forecasts. 79(3), 823-848.
P. D. England, R. J. Verrall (2002) Stochastic Claims Reserving in General Insurance. 8(3), 443-518.
A. Gabrielli (2019) Inflation and Non-Life Insurance: Pricing and Reserving. 2019(7), 587-607.
A. Gunzburger, R. Jung (2018) Time Series Methods for Inflation Adjustment in Loss Reserving. 12(2), 245-260.
T. Hastie, R. Tibshirani, J. Friedman (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
B. Huang, M. Jiang, P. Liang (2022) Machine Learning Approaches for Property and Casualty Insurance: A Survey. 35(4), 58-79.
(2017) IFRS 17 Insurance Contracts.
S. M. Lundberg, S. I. Lee (2017) A Unified Approach to Interpreting Model Predictions. 4765-4774.
T. Mack (1993) Distribution-Free Calculation of the Standard Error of Chain Ladder Reserve Estimates. 23(2), 213-225.
R. Mayers, J. Wang, Z. Quan (2021) Random Forest Models in Property and Casualty Insurance Loss Reserving. 88(2), 47-65.
A. Olivieri, E. Pitacco, M. Scheel (2018) IFRS 17 and Risk Adjustment: The Actuarial Viewpoint. 8(1), 27-49.
M. R. Powers (2007) Insurance Inflation and the Effectiveness of Reserving Methods. 32(1), 121-138.
Q. Quan, J. Wang, T. Jiang (2017) Insurance Claim Prediction Using Random Forests. 45(1), 95-112.
R. Tibshirani (1996) Regression Shrinkage and Selection via the Lasso. 58(1), 267-288.
M. V. Wüthrich, M. Merz (2008) Stochastic Claims Reserving Methods in Insurance.
R. Beran (2008) An introduction to the bootstrap. 288-294.
B. Efron (1985) Bootstrap confidence intervals for a class of parametric problems. 72(1), 45-58.
B. Mahohoho (2024) Automated Actuarial Data Analytics-Based Inflation Adjusted Frequency Severity Loss Reserving Model. 14(3), 341-393.
W.O. Owais, A.A. Dahiyat (2021) Readiness and challenges for applying IFRS 17 (insurance contracts): The case of Jordanian insurance companies. 277-286.
M. Arce, B. Giner, M.A. Taleb (2023) Due process as a legitimating mechanism: Participation and responsiveness in the development of IFRS 17: Insurance contracts. (6), 107150.
A. Dahiyat, W. Owais (2021) The expected impact of applying IFRS (17) insurance contracts on the quality of financial reports. 7(3), 581-590.
P. Longoni (2019) IFRS 17 Insurance Contracts and Firm Value. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3589560
W. Yousuf, J. Stansfield, K. Malde, N. Mirin, R. Walton, B. Thorpe, J. Thorpe, C. Iftode, L. Tan, R. Dyble, A. Pelsser (2021) The IFRS 17 contractual service margin: a life insurance perspective. e2.
Y.S.M. Alzobaidy, B.N.A. Al-Mashhadani (2021) The effect of measuring insurance contracts according to IFRS 17 on the financial solvency of insurance companies in Iraq. 21(1).
P. Koskipalo (2022) IFRS 17–Insurance contracts: Implementing the standard and its effect on measuring insurance contracts and financial reporting.
J. W. Creswell (2014) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches.
G. Biau (2012) Analysis of a Random Forests Model. 13, 1063-1095.
J. Hughes (2020) Simulated Data in R: A Practical Approach for Applied Statistics.
J. Han, M. Kamber, J. Pei (2012) Data Mining: Concepts and Techniques.
F. Provost, T. Fawcett (2013) Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking.
P. Christoffersen (2012) Elements of Financial Risk Management.
J. Hull (2018) Risk Management and Financial Institutions.
G. James, D. Witten, T. Hastie, R. Tibshirani (2013) An Introduction to Statistical Learning with Applications in R.
P. R. Hinton, C. Brownlow, I. McMurray, B. Cozens (2004) SPSS Explained.
A. Field (2013) Discovering Statistics Using IBM SPSS Statistics.
L. van der Maaten, G. Hinton (2008) Visualizing data using t-SNE. 9(Nov), 2579-2605.
S. Boyd, L. Vandenberghe (2004) Convex Optimization.
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