Version 1
: Received: 1 October 2024 / Approved: 2 October 2024 / Online: 2 October 2024 (11:59:44 CEST)
How to cite:
Surya, H. A.; Sukono, S.; Napitupulu, H.; Ismail, N. A Systematic Literature Review of Insurance Claims Risk Measurement Using Hidden Markov Model. Preprints2024, 2024100113. https://doi.org/10.20944/preprints202410.0113.v1
Surya, H. A.; Sukono, S.; Napitupulu, H.; Ismail, N. A Systematic Literature Review of Insurance Claims Risk Measurement Using Hidden Markov Model. Preprints 2024, 2024100113. https://doi.org/10.20944/preprints202410.0113.v1
Surya, H. A.; Sukono, S.; Napitupulu, H.; Ismail, N. A Systematic Literature Review of Insurance Claims Risk Measurement Using Hidden Markov Model. Preprints2024, 2024100113. https://doi.org/10.20944/preprints202410.0113.v1
APA Style
Surya, H. A., Sukono, S., Napitupulu, H., & Ismail, N. (2024). A Systematic Literature Review of Insurance Claims Risk Measurement Using Hidden Markov Model. Preprints. https://doi.org/10.20944/preprints202410.0113.v1
Chicago/Turabian Style
Surya, H. A., Herlina Napitupulu and Noriszura Ismail. 2024 "A Systematic Literature Review of Insurance Claims Risk Measurement Using Hidden Markov Model" Preprints. https://doi.org/10.20944/preprints202410.0113.v1
Abstract
In the rapidly evolving field of insurance, accurate risk measurement is crucial for effective claims management and financial stability. Therefore, this research presented a systematic literature review (SLR) on insurance claims risk measurement using Hidden Markov Model (HMM). Bibliometric analysis was conducted using VOSviewer and ResearchRabbit software to map research trends and collaboration networks in this topic. The review explored the implementation of HMM in predicting the frequency and magnitude of insurance claims, with a focus on the statistical distribution methods used. In addition, the research emphasized the influence of the number of hidden states in HMM on claims behavior, both in terms of frequency and magnitude, and provided interpretations of these hidden dynamics. Data sources for the review comprised three databases, namely Scopus, ScienceDirect, and Dimensions. The article selection process followed PRISMA guidelines, resulting in five key articles relevant to the topic. The results offered insights into the application of HMM for forecasting the frequency and severity of insurance claims and opened avenues for further investigation on distribution models and hidden state modeling.
Keywords
Insurance claims; risk measurement; SLR; bibliometric analysis; Hidden Markov Model
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
