Prediction of Outstanding Claims Liability in Non-Life Insurance: An Application of Adaptive Grey Model

Mujiati Dwi Kartikasari; Hani’atul Maghfuroh

doi:10.20885/EKSAKTA.vol2.iss2.art4

Submitted

August 15, 2020

Accepted

September 4, 2021

Published

September 17, 2021

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Abstract

In order to assess the solvency of non-life insurance companies, the prediction of outstanding claims liability is very important. Prediction of outstanding claims liability is usually done by using a run-off triangle data scheme. However, if data are not available to form the scheme, the prediction of outstanding claims liability cannot be made. Another alternative for predicting of outstanding claims liability is to use time series analysis. This research uses an adaptive grey model that has the advantage of being free of assumptions of data patterns and a minimum amount of data used to predict is small (at least 4 data). To determine the accuracy of the adaptive grey model, we compare the prediction of outstanding claims liability using a grey model classic. Based on the analysis results, the adaptive grey model is better than the classic gray model in predicting outstanding claims liability.

Keywords

Outstanding Claims Liability Adaptive Grey Model Non-Life Insurance Time Series

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How to Cite

Kartikasari, M. D., & Maghfuroh, H. (2021). Prediction of Outstanding Claims Liability in Non-Life Insurance: An Application of Adaptive Grey Model. EKSAKTA: Journal of Sciences and Data Analysis, 2(2), 109–115. https://doi.org/10.20885/EKSAKTA.vol2.iss2.art4

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References

R. Kaas, J. Dhaene, M. Goovaerts, M. Denuit, Modern Actuarial Risk Theory using R Second Edition, Springer 2008.
M.V. Wuthrich, M. Merz, Stochastic Claims Reserving Methods in Insurance, John Wiley & Sons 2008.
I. Badounas, G. Pitselis, Loss Reserving Estimation With Correlated Run-Off Triangles in a Quantile Longitudinal Model, Risks 8(14) (2020).
F. F. Adam, Claim Reserving Estimation by Using the Chain Ladder Method, KnE Social Sciences (2018) 1192-1204.
R. N. Suwandani, Y. Purwono, Implementation of Gaussian Process Regression in Estimating Reserve for Motor Vehicle Incurance Claim, Journal of Asian Multicultural Research for Economy and Management Study 2(1) (2021) 38-48.
K. Antonio, J. Beirlant, T. Hoedemakers, R Verlaak, Lognormal Mixed Models for Reported Claims Reserves, North American Actuarial Journal 10(1) (2006) 30-48.
C. Lin, Forecasting and Analyzing the Competitive Diffusion of Mobile Cellular Broadband and Fixed Broadband in Taiwan with Limited Historical Data, Economic Modelling 35 (2013) 207-213.
D. Julong, Introduction to Grey System Theory, The Journal of Grey System 1 (1989) 1-24.
H. Dang, Y. Huang, C. Wang, T. Nguyen, An Application of the Short-Term Forecasting with Limited Data in the Healthcare Traveling Industry, Sustainability 8 (2016) 1037-1051.
C. Zor, F. Cebi, Demand prediction in health sector using fuzzy grey forecasting, Journal of Enterprise Information Management 31(1) (2018).
A. D. L. Foucade, S. Gabriel, E. Scott, K. Theodore, C. Metivier, A Survey of Selected Grey Forecasting Models with Application to Medical Tourism Forecasting, Theorotical Economics Letters 9(4) (2019) 1079-1092.
Y. Zhao, M. Shou, Z. W, Prediction of the Number Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models, International Journal of Environmental Research and Public Health 17(12) (2020).
X. Yang, J. Zou, D. Kong, G. Jiang, The analysis of GM (1, 1) grey model to predict the incidence trend of typhoid and paratyphoid fevers in Wuhan City, China, Medicine 97(34) (2018).
B. M. Iqelan, Forecasts Breast Cancer Referrals Using Grey Prediction Model GM(1,1), Applied Mathematical Sciences 11(54) (2017) 2647-2662.
K. Li, T. Zhang, Forecasting Electricity Consuption Using an Improved Grey Prediction Model, Information 9(8) (2018) 204.
S. Guefano, J. G. Tamba, T. E. W. Azong, L. Monkam, Methodology for Forecasting Electricity Consumption by Grey and Vector Autoregressive Models, MethodsX 8 (2021).
F. Song, J. Liu. T. Zhang, J. Guo, S. Tian, D. Xiong, The Grey Forecasting Model for the Medium-and Long-Term Load Forecasting, Journal of Physics: Conference Series 1654 (2020).
L. Zhao, X. Zhou, Forecasting Electricity Demand Using a New Grey Prediction Model with Smoothness Operator, Symmetry 10 (2018).
B. Zeng, C. Li, Forecasting the Natural Gas Demand in China Using a Self-Adapting Intelligent Grey Model, Energy 112 (2016) 810-825.
N. geng, J. Li, H. Gao, S. Zhao, Forecasting Hydrogen Energy Production in China base on Multi Grey Model, IOP Conference Series: Earth and Environmental Science 811 (2021).
F. Pourofoghi, J. S. Ardabili, D. D. Salokolaei, A New Approach for Finding an Optimal Solution for Grey Transportation Problem, International Journal of Nonlinear Analysis and Applications 10 (2019).
C. Liu, T. Shu, S. Chen, S. Wang, K. K. Lai, L. Gan, An improved grey neural network model for predicting transportation disruptions, Expert Systems with Application 45 (2016) 331-340.
H. Cy, L. Cy, C. Ci, Application of Grey Theory to the Field of Economic Forecasting, Journal of Global Economics 3(1) (2014).
E. Kayacan, B. Ulutas, O. kaynak, Grey System Theory-Based Models in Time Series Prediction, Expert Systems with Applications 37 (2010) 1784-1789.
D. Li, C. Yeh, C. Chang, An Improved Grey-Based Approach for Early Manufacturing Data Forecasting, Computers & Industrial Engineering 57 (2009) 1161-1167.
C. Chang, D. Li, W. Dai, C. Chen, Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging, Hindawi Publishing Corporation Mathematical Problems in Engineering 2013 (2013).
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Insurance Bureau, Indonesian Insurance, Capital Market and Financial Institutions Supervisory Agency 2007.
Insurance Bureau, Indonesian Insurance, Capital Market and Financial Institutions Supervisory Agency 2008.
Insurance Bureau, Indonesian Insurance, Capital Market and Financial Institutions Supervisory Agency 2009.
Insurance Bureau, Indonesian Insurance, Capital Market and Financial Institutions Supervisory Agency 2010.
OJK, Insurance Statistics, Financial Services Authority 2014.
OJK, Insurance Statistics, Financial Services Authority 2015.
OJK, Insurance Statistics, Financial Services Authority 2016.
OJK, Insurance Statistics, Financial Services Authority 2017.
OJK, Insurance Statistics, Financial Services Authority 2018.
OJK, Insurance Statistics, Financial Services Authority 2019.
D.C. Montgomery, C.L. Jennings, M. Kulahci, Introduction to Time Series Analysis and Forecasting, John Wiley & Sons 2015.

References

R. Kaas, J. Dhaene, M. Goovaerts, M. Denuit, Modern Actuarial Risk Theory using R Second Edition, Springer 2008.

M.V. Wuthrich, M. Merz, Stochastic Claims Reserving Methods in Insurance, John Wiley & Sons 2008.

I. Badounas, G. Pitselis, Loss Reserving Estimation With Correlated Run-Off Triangles in a Quantile Longitudinal Model, Risks 8(14) (2020).

F. F. Adam, Claim Reserving Estimation by Using the Chain Ladder Method, KnE Social Sciences (2018) 1192-1204.

R. N. Suwandani, Y. Purwono, Implementation of Gaussian Process Regression in Estimating Reserve for Motor Vehicle Incurance Claim, Journal of Asian Multicultural Research for Economy and Management Study 2(1) (2021) 38-48.

K. Antonio, J. Beirlant, T. Hoedemakers, R Verlaak, Lognormal Mixed Models for Reported Claims Reserves, North American Actuarial Journal 10(1) (2006) 30-48.

C. Lin, Forecasting and Analyzing the Competitive Diffusion of Mobile Cellular Broadband and Fixed Broadband in Taiwan with Limited Historical Data, Economic Modelling 35 (2013) 207-213.

D. Julong, Introduction to Grey System Theory, The Journal of Grey System 1 (1989) 1-24.

H. Dang, Y. Huang, C. Wang, T. Nguyen, An Application of the Short-Term Forecasting with Limited Data in the Healthcare Traveling Industry, Sustainability 8 (2016) 1037-1051.

C. Zor, F. Cebi, Demand prediction in health sector using fuzzy grey forecasting, Journal of Enterprise Information Management 31(1) (2018).

A. D. L. Foucade, S. Gabriel, E. Scott, K. Theodore, C. Metivier, A Survey of Selected Grey Forecasting Models with Application to Medical Tourism Forecasting, Theorotical Economics Letters 9(4) (2019) 1079-1092.

Y. Zhao, M. Shou, Z. W, Prediction of the Number Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models, International Journal of Environmental Research and Public Health 17(12) (2020).

X. Yang, J. Zou, D. Kong, G. Jiang, The analysis of GM (1, 1) grey model to predict the incidence trend of typhoid and paratyphoid fevers in Wuhan City, China, Medicine 97(34) (2018).

B. M. Iqelan, Forecasts Breast Cancer Referrals Using Grey Prediction Model GM(1,1), Applied Mathematical Sciences 11(54) (2017) 2647-2662.

K. Li, T. Zhang, Forecasting Electricity Consuption Using an Improved Grey Prediction Model, Information 9(8) (2018) 204.

S. Guefano, J. G. Tamba, T. E. W. Azong, L. Monkam, Methodology for Forecasting Electricity Consumption by Grey and Vector Autoregressive Models, MethodsX 8 (2021).

F. Song, J. Liu. T. Zhang, J. Guo, S. Tian, D. Xiong, The Grey Forecasting Model for the Medium-and Long-Term Load Forecasting, Journal of Physics: Conference Series 1654 (2020).

L. Zhao, X. Zhou, Forecasting Electricity Demand Using a New Grey Prediction Model with Smoothness Operator, Symmetry 10 (2018).

B. Zeng, C. Li, Forecasting the Natural Gas Demand in China Using a Self-Adapting Intelligent Grey Model, Energy 112 (2016) 810-825.

N. geng, J. Li, H. Gao, S. Zhao, Forecasting Hydrogen Energy Production in China base on Multi Grey Model, IOP Conference Series: Earth and Environmental Science 811 (2021).

F. Pourofoghi, J. S. Ardabili, D. D. Salokolaei, A New Approach for Finding an Optimal Solution for Grey Transportation Problem, International Journal of Nonlinear Analysis and Applications 10 (2019).

C. Liu, T. Shu, S. Chen, S. Wang, K. K. Lai, L. Gan, An improved grey neural network model for predicting transportation disruptions, Expert Systems with Application 45 (2016) 331-340.

H. Cy, L. Cy, C. Ci, Application of Grey Theory to the Field of Economic Forecasting, Journal of Global Economics 3(1) (2014).

E. Kayacan, B. Ulutas, O. kaynak, Grey System Theory-Based Models in Time Series Prediction, Expert Systems with Applications 37 (2010) 1784-1789.

D. Li, C. Yeh, C. Chang, An Improved Grey-Based Approach for Early Manufacturing Data Forecasting, Computers & Industrial Engineering 57 (2009) 1161-1167.

C. Chang, D. Li, W. Dai, C. Chen, Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging, Hindawi Publishing Corporation Mathematical Problems in Engineering 2013 (2013).

Insurance Bureau, Indonesian Insurance, Capital Market and Financial Institutions Supervisory Agency 2006.

Insurance Bureau, Indonesian Insurance, Capital Market and Financial Institutions Supervisory Agency 2007.

Insurance Bureau, Indonesian Insurance, Capital Market and Financial Institutions Supervisory Agency 2008.

Insurance Bureau, Indonesian Insurance, Capital Market and Financial Institutions Supervisory Agency 2009.

Insurance Bureau, Indonesian Insurance, Capital Market and Financial Institutions Supervisory Agency 2010.

OJK, Insurance Statistics, Financial Services Authority 2014.

OJK, Insurance Statistics, Financial Services Authority 2015.

OJK, Insurance Statistics, Financial Services Authority 2016.

OJK, Insurance Statistics, Financial Services Authority 2017.

OJK, Insurance Statistics, Financial Services Authority 2018.

OJK, Insurance Statistics, Financial Services Authority 2019.

D.C. Montgomery, C.L. Jennings, M. Kulahci, Introduction to Time Series Analysis and Forecasting, John Wiley & Sons 2015.

Prediction of Outstanding Claims Liability in Non-Life Insurance: An Application of Adaptive Grey Model

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