Abstract
Vanbreda Risk & Benefits, a large Belgian insurance broker and risk consultant, allocates a substantial amount of time and resources to answer contract related questions from customers. This requires employees to manually search the relevant parameters in the contracts. In this paper, a solution is proposed and evaluated that automatically extracts insurance parameters from contracts using regular expressions and Natural Language Processing. While Natural Language Processing has been used in insurance for optimising premiums, detecting fraudulent claims, or underwriting, limited work has been done regarding parameter extraction. The proposed solution has been developed on 127 different contracts and two different contract types in terms of accuracy and time performance. Moreover, the automatic parameter extraction has been compared to manual parameter extraction. We conclude that automatic parameter extraction using regular expressions achieves better accuracy than manual extraction on top of being significantly faster, allowing Vanbreda Risk & Benefits to invest more time into providing better customer service.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Zappa, D., Borrelli, M., Clemente, G.P., Savelli, N.: Text Mining in Insurance: From Unstructured Data to Meaning. Variance, Press. https://www.variancejournal.org/articlespress/. Accessed 23 March 2021 (2019)
Wang, Y., Xu, W.: Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud. Decis. Support Syst. 105, 87–95 (2018)
Mosley Jr., R.C.: Social media analytics: data mining applied to insurance Twitter posts. In: Casualty Actuarial Society E-Forum. p. 1 (2012)
Benavides, T.: Practical Human Resources for Public Managers: A Case Study Approach. CRC Press (2011)
Donepudi, P.K.: AI and machine learning in banking: a systematic literature review. Asian J. Appl. Sci. Eng. 6, 157–162 (2017)
Kankanhalli, A., Charalabidis, Y., Mellouli, S.: IoT and AI for smart government: a research agenda. Gov. Inf. Q. 36, 304–309 (2019). https://doi.org/10.1016/j.giq.2019.02.003
Lamberton, C., Brigo, D., Hoy, D.: Impact of robotics, RPA and AI on the insurance industry: challenges and opportunities. J. Financ. Perspect. 4 (2017)
Balasubramanian, R., Libarikian, A., McElhaney, D.: Insurance 2030—The Impact of AI on the Future of Insurance. McKinsey Co. (2018)
Ly, A., Uthayasooriyar, B., Wang, T.: A survey on natural language processing (nlp) and applications in insurance. arXiv Prepr. arXiv2010.00462 (2020)
Kolyshkina, I., Rooyen, M.: Text mining for insurance claim cost prediction. In: Williams, G.J., Simoff, S.J. (eds.) Data Mining. LNCS (LNAI), vol. 3755, pp. 192–202. Springer, Heidelberg (2006). https://doi.org/10.1007/11677437_15
Liao, X., Chen, G., Ku, B., Narula, R., Duncan, J.: Text mining methods applied to insurance company customer calls: a case study. North Am. Actuar. J. 24, 153–163 (2020)
Nuruzzaman, M., Hussain, O.K.: IntelliBot: a dialogue-based chatbot for the insurance industry. Knowl.-Based Syst. 196, 105810 (2020)
Yogish, D., Manjunath, T.N., Hegadi, R.S.: Review on natural language processing trends and techniques using NLTK. In: Santosh, K.C., Hegadi, R.S. (eds.) Recent Trends in Image Processing and Pattern Recognition. pp. 589–606. Springer Singapore, Singapore (2019)
Loza Mencía, E.: Segmentation of legal documents. In: Proceedings of the 12th International Conference on Artificial Intelligence and Law. pp. 88–97. Association for Computing Machinery, New York, NY, USA (2009). https://doi.org/10.1145/1568234.1568245
Shah, P., Joshi, S., Pandey, A.K.: Legal clause extraction from contract using machine learning with heuristics improvement. In: 2018 4th International Conference on Computing Communication and Automation (ICCCA), pp. 1–3 (2018)
Chalkidis, I., Androutsopoulos, I., Michos, A.: Extracting contract elements. In: Proceedings of the 16th Edition of the International Conference on Articial Intelligence and Law, pp. 19–28 (2017)
McKie, J.X., Liu, R.: PyMuPDF. https://pypi.org/project/PyMuPDF/
Shinyama, Y., Guglielmetti, P., Marsman, P.: PDFMiner. https://pdfminersix.readthedocs.io/en/latest/
Hunt, J.: Regular expressions in python. In: Advanced Guide to Python 3 Programming. UTCS, pp. 257–271. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-25943-3_22
Bird, S., Klein, E., Loper, E.: Natural language processing with Python: analyzing text with the natural language toolkit. O’Reilly Media, Inc. (2009)
McKinney, W.: Data structures for statistical computing in python. In: van der Walt, S. and Millman, J. (eds.) Proceedings of the 9th Python in Science Conference, pp. 56–61 (2010). https://doi.org/10.25080/Majora-92bf1922-00a
pandas development team, T.: pandas-dev/pandas: Pandas (2020). https://doi.org/10.5281/zenodo.3509134
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Goossens, A., Berth, L., Decoene, E., Van Veldhoven, Z., Vanthienen, J. (2022). Automatically Extracting Insurance Contract Knowledge Using NLP. In: Abramowicz, W., Auer, S., Stróżyna, M. (eds) Business Information Systems Workshops. BIS 2021. Lecture Notes in Business Information Processing, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-031-04216-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-04216-4_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-04215-7
Online ISBN: 978-3-031-04216-4
eBook Packages: Computer ScienceComputer Science (R0)