A comprehensive analysis of keywords co-occurrence network and the most cited journals on data mining techniques in insurance industry using scientometrics approach

Hajiyan, H.; Zarjini, A.

doi:10.22056/ijir.2024.01.06

Document Type : Promotional-Science Article

Authors

H. Hajiyan ¹
A. Zarjini ²

¹ Department of Computer Science, Faculty of Science, Ontario University of Technology, Oshawa, Ontario, Canada

² Department of Technology Management, Faculty of Management and Economics, Islamic Azad University, Science and Research Unit, Tehran, Iran

https://doi.org/10.22056/ijir.2024.01.06

Abstract

BACKGROUND AND OBJECTIVES: Data mining is known as a process of discovering patterns in large datasets through a combination of statistical tools and techniques. In recent years, data mining and its applications in different businesses have increasingly grown. Insurance industry is one of the data-driven businesses whose survival is so dependent on satisfying customers besides achieving the highest benefit. Information or data is a vital asset of the insurance industry ;accordingly, using data mining techniques to discover patterns behind large datasets is a need. Having seen the increasingly high rate of information technology and recorded data in data-driven businesses, lots of industries like the insurance industry have been urged to use state-of-the-art data mining techniques to turn raw data into useful information using Big Data Analytics.
METHODS: Looking at the current research on data mining applications in the insurance industry proves the fact that we should recognize the state-of-the art techniques in data mining and set new strategies to focus on Big Data Analytics more. Big Data Analytics consists of the algorithms which are more efficient and less time-consuming so it can help to identify patterns and rules in complex datasets. For this purpose, this paper presents a comprehensive literature review regarding the usage of data mining techniques in the insurance industry by the scientometrics approach. For this purpose, first we searched and gathered bibliometrics files of recent researches from Web of Science and Scopus into four different scenarios. In each scenario, we looked up for different keywords regarding “Data Mining”, “Insurance Industry”, and “Risk Management” to make sure that all the results would be specifically focused on the research topic. Then, we used R programming software to analyze the results of each scenario based on keywords co-occurrence in the given research.
FINDINGS: The results of keywords co-occurrence and a word cloud of recent research confirm that insurance companies should focus on Big Data Analytics instead of traditional data processing to get information systematically from too large or complex datasets. Big Data Analytics has been used for several years, but in recent years many data-driven businesses, like the insurance industry, have used its techniques associated with risk and risk factor identification. Risk management in the insurance industry has been widely considered in recent researches. Therefore, in this paper, some high-ranked journals and the most significant researches have been identified and recommended in order to pave the way for future researches in this field.
CONCLUSION: We hope that the comprehensive literature review provided in this paper can help the researchers to focus on the relative journals and researches published then get into more details. For this purpose, the lists of all journals and conferences besides the most cited researches are provided in the experimental section of this paper. Also, the ranking list of different countries from all around the world related to data mining and Big Data Analytics in the insurance industry is presented. The results show that Iran is the 15^th country that uses data mining techniques and it is the 17^th country in the world focusing on risk management in the insurance industry.

Keywords

Main Subjects

Comparative studies in the field of insurance

References

Abbas, N.N.; Ahmed, T.; Shah, S.H.U.; Omar, M.; Park, H.W., (2019). Investigating the applications of artificial intelligence in cyber security. Scientometrics.,

Bauder, R.; Da Rosa, R.; Khoshgoftaar, T., (2018). identifying medicare provider fraud with unsupervised machine learning. 2018 IEEE International conference on information reuse and integration (IRI)., 158(18): 5-22 (18 Pages).

Boodhun, N.; Jayabalan, M., (2018). Risk prediction in life insurance industry using supervised learning algorithms. Complex. Intell. Syst., 3(2).

Boroufar, A.; Rezaeian, A.; Shokohyar, S., (2017). Identifying the customer behavior model in life insurance sector using data mining. Sci. Res. Q. Manage. Res. Iran., 121: 1189-1211 (23 Pages). [In Persian]

Chen, J.; Su, Y.; Si, H.; Chen, J., (2018). Managerial areas of construction and demolition waste: A scientometric review. Int. J. Environ. Res. Public. Health., 285-292 (8 Pages).

Chen, J.; Su, Y.; Si, H.; Chen, J., (2018). Managerial areas of construction and demolition waste: A scientometric review. Int. J. Environ. Res. Public. Health., 4: 145-154 (10 Pages).

Cui, H.; Li, Q.; Li, H.; Yan, Z., (2016). Healthcare fraud detection based on trustworthiness of doctors. 2016 IEEE Trustcom/BigDataSE/ISPA., 20(4): 65-94 (30 Pages).

Duman, E.A.; Sağıroğlu, Ş., (2017). Health care fraud detection methods and new approaches. International conference on computer science and engineering (UBMK)., 15(11).

Ghorbani, A.; Farzai, S., (2018). Fraud detection in automobile insurance using a data mining based approach. Int. J. Mechatron. Electr. Comput. Technol., 15.

Ghuse, N.; Pawar, P.; Potgantwar, A., (2017). An improved approch for fraud detection in health insurance using data mining techniques. Int. J. Sci. Res. Netw. Secur. Commun.

Herland, M.; Bauder, R.A.; Khoshgoftaar, T.M., (2019). The effects of class rarity on the evaluation of supervised healthcare fraud detection models. J. Big. Data.

Herland, M.; Khoshgoftaar, T.M.; Bauder, R.A., (2018). Big data fraud detection using multiple medicare data sources. J. Big. Data., 8(27): 3764-3771 (8 Pages).

Hessami, F., (2018). Business risk evaluation and management of Iranian commercial insurance companies. Manage. Sci. Lett., 5: 27-32 (6 Pages).

Jahangiri, M.; Ahmadi Varzaneh, M.; Naderi Dehkordi, M., (2014). Application of data mining in insurance industry and customer classification. National conference of computer engineering and information technology management., 6: 1-33 (33 Pages). [In Persian]

Joudaki, H.; Rashidian, A.; Minaei-Bidgoli, B.; Mahmoodi, M.; Geraili, B.; Nasiri, M.; Arab, M., (2015). Using data mining to detect health care fraud and abuse: A review of literature. Global. J. Health. Sci., 5(1): 1-21 (21 Pages).

Kara, M.E.; Fırat, S.Ü.O.; Ghadge, A., (2020). A data mining-based framework for supply chain risk management. Comput. Ind. Eng., 8(2): 91-102 (12 Pages).

Kašćelan, V.; Kašćelan, L.; Novović Burić, M., (2016). A nonparametric data mining approach for risk prediction in car insurance: A case study from the Montenegrin market. Econ. Res. Ekonomska. Istraživanja., 7(1): 194-202 (9 Pages).

Kenyon, D.L., (2018). An intelligent method of predicting insurance claims fraud. Univ. Pretoria., 139.

Kirlidog, M.; Asuk, C., (2012). fraud detection approach with data mining in health insurance. Procedia. Social. Behav. Sci., 29(1): 545-558 (14 Pages).

Koh, H.C.; Tan, G., (2011). Data mining applications in healthcare. J. Healthcare. Inf. Manage.

Kumar, M.; Ghani, R.; Mei, Z.S., (2010). Data mining to predict and prevent errors in health insurance claims processing. Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, ACM., 62: 989-994 (6 Pages).

Li, Y.; Yan, C.; Liu, W.; Li, M., (2018). A principle component analysis-based random forest with the potential nearest neighbor method for automobile insurance fraud identification. Appl. Soft. Comput., 19(2): 64-72 (9 Pages).

Liao, S.H.; Chu, P.H.; Hsiao, P.Y., (2012). Data mining techniques and applications–A decade review from 2000 to 2011. Expert. Syst. Appl., 65-74 (10 Pages).

Lin, W.; Wu, Z.; Lin, L.; Wen, A.; Li, J., (2017). An ensemble random forest algorithm for insurance big data analysis. IEEE., 70: 1000-1009 (10 Pages).

Liu, Y.; Peng, J.; Yu, Z., (2018). Big data platform architecture under the background of financial technology: In the insurance industry as an example. Proceedings of the 2018 international conference on big data engineering and technology, ACM., 39(12): 11303-11311 (9 Pages).

Luan, T.; Yan, Z.; Zhang, S., (2019). Fraudster detection based on modularity optimization algorithm. 2019 IEEE 23rd International conference on computer supported cooperative work in design (CSCWD), IEEE., 5: 16568-16575 (8 Pages).

Massi, M.C.; Ieva, F.; Lettieri, E., (2020). Data mining application to healthcare fraud detection: Two-step unsupervised clustering method for outlier detection with administrative databases. BMC. Med. Inf. Decis. Making., 31-35 (5 Pages).

Mingers, J.; Leydesdorff, L., (2015). A review of theory and practice in scientometrics. Eur. J. Oper. Res., 422-427 (6 Pages).

Obodoekwe, N.; Van der Haar, D.T., (2019). A comparison of machine learning methods applicable to healthcare claims fraud detection. International conference on information technology & systems, Springer., 20: 1-11 (11 Pages).

Pandey, P.; Saroliya, A.; Kumar, R., (2016). Analyses and detection of health insurance fraud using data mining and predictive modeling techniques. Soft. Comput. Theor. Appl., 246(1): 1-19 (19 Pages).

Pawar, M.P., (2016). Review on data mining techniques for fraud detection in health insurance. IJETT., 548-557 (10 Pages).

Phua, C.; Lee, V.; Smith, K.; Gayler, R., (2010). Comprehensive survey of data mining-based fraud detection research. arXiv. Prepr. arXiv., 2: 41-49 (9 Pages).

Qarakhani, M.; Abolqasmi, M., (2019). News of the insurance world. NoorMags., 3(2). [In Persian]

Rahmani, M.; Haji Zain al-Abidini, M., (2014). Applications of data mining in information science and epistemology. Q. J. Inf. Manage. Epistemology., 1009-6119 (5111 Pages). [In Persian]

Raikwar, M.; Mazumdar, S.; Ruj, S.; Gupta, S.S.; Chattopadhyay, A.; Lam, K.Y., (2018). A blockchain framework for insurance processes. 2018 9th IFIP international conference on new technologies, mobility and security (NTMS), IEEE., 1-4 (4 Pages).

Rawte, V.; Anuradha, G., (2015). Fraud detection in health insurance using data mining techniques. 2015 International conference on communication, information & computing technology (ICCICT), IEEE.

Sheshasaayee, A.; Thomas, S.S., (2017). A purview of the impact of supervised learning methodologies on health insurance fraud detection. Inf. Syst. Des. Intell. Appl., Springer: 978-984 (7 Pages).

Waghade, S.S.; Karandikar, A.M., (2018). A comprehensive study of healthcare fraud detection based on machine learning. Int. J. Appl. Eng. Res., 13(6): 4175-4178 (4 Pages).

Wang, Y.; Xu, W., (2018). Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud. Decis. Support. Syst., 105: 87-95 (9 Pages).

Wuyu, S.; Cerna, P., (2019). Risk assessment predictive modelling in Ethiopian insurance industry using data mining. Software. Eng., 6(4).

Yao, J.; Zhang, J.; Wang, L., (2018). A financial statement fraud detection model based on hybrid data mining methods. 2018 International conference on artificial intelligence and big data (ICAIBD), IEEE., 57-61 (5 Pages).

Letters to Editor

IJIR Journal welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in IJIR should be sent to the editorial office of IJIR within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.

[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.

[3] Letters can be no more than 300 words in length.

[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.

[5] Anonymous letters will not be considered.

[6] Letter writers must include their city and state of residence or work.

[7] Letters will be edited for clarity and length.

Name *

Email Address *

Affiliation *

Comments *

Security Code *

Iranian Journal of Insurance Research

A comprehensive analysis of keywords co-occurrence network and the most cited journals on data mining techniques in insurance industry using scientometrics approach

References

References

Letters to Editor

Send comment about this article

Volume 13, Issue 1 - Serial Number 47
November 2024
Pages 71-86

A comprehensive analysis of keywords co-occurrence network and the most cited journals on data mining techniques in insurance industry using scientometrics approach

References

References

Letters to Editor

Send comment about this article

Volume 13, Issue 1 - Serial Number 47November 2024Pages 71-86

Volume 13, Issue 1 - Serial Number 47
November 2024
Pages 71-86