Customer-Centric Insurance Solutions: AI-Powered Claims Processing and Fraud Prevention
Keywords:
AI-powered claims processing, Fraud detection, Natural Language Processing (NLP), Anomaly detection algorithms, Blockchain technology, Insurance sector efficiencyAbstract
Artificial intelligence (AI) has the capacity to completely transform the way claims are processed and fraud is prevented in the insurance industry. This paper presents an AI-driven system that utilizes sophisticated technologies including Natural Language Processing (NLP) and anomaly detection algorithms to automate the workflows of claims processing and fraud detection. The model, which was trained and validated using a comprehensive dataset of insurance claims, obtained a notable accuracy rate of 94.6% in detecting fraudulent claims. Additionally, it successfully shortened the average processing time for claims by 30%, from 10 days to 7 days. Through the incorporation of blockchain technology, the system guarantees the accuracy and openness of data, hence improving the dependability and credibility of the claims process. This AI-powered technology greatly enhances customer happiness by accelerating the resolution of claims and safeguarding insurers against fraudulent activities, consequently boosting overall operational efficiency in the insurance industry.
Downloads
References
Dhieb, N., Ghazzai, H., Besbes, H. and Massoud, Y., 2019, September. Extreme gradient boosting machine learning algorithm for safe auto insurance operations. In 2019 IEEE international conference on vehicular electronics and safety (ICVES) (pp. 1-5). IEEE.
Association of Certified Fraud Examiners, 2018. Insurance Fraud Handbook. Austin, TX, USA.
Corum, D., 2015, February. Insurance research council finds that fraud and buildup add up to $7.7 billion in excess payments for auto injury claims. Insurance Research Council, Malvern, PA, USA. [Online]. Available: https://www.insurance-research.org/sites/default/files/downloads/IRC%20Fraud%20News%20Release.pdf
Wang, S., Wang, J., Wang, X., Qiu, T., Yuan, Y., Ouyang, L., Guo, Y., and Wang, F.-Y., 2018. Blockchain-powered parallel healthcare systems based on the ACP approach. IEEE Transactions on Computational Social Systems, 5(4), pp.942-950.
Pieroni, A., Scarpato, N., Di Nunzio, L., Fallucchi, F., and Raso, M., 2018. Smarter city: Smart energy grid based on blockchain technology. International Journal of Advanced Science, Engineering and Information Technology, 8(1), pp.298-306.
Samaniego, M. and Deters, R., 2017, June. Internet of smart Things–IoST: Using blockchain and clips to make things autonomous. In 2017 IEEE International Conference on Cognitive Computing (ICCC) (pp. 9-16). IEEE.
Chen, T. and Guestrin, C., 2016, August. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785-794).
Kowshalya, G. and Nandhini, M., 2018, April. Predicting fraudulent claims in automobile insurance. In 2018 2nd International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 1338-1343). IEEE.
Supraja, K. and Saritha, S.J., 2017, August. Robust fuzzy rule based technique to detect frauds in vehicle insurance. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 3734-3739). IEEE.
Badriyah, T., Rahmaniah, L. and Syarif, I., 2018, October. Nearest neighbour and statistics method based for detecting fraud in auto insurance. In 2018 International Conference on Applied Engineering (ICAE) (pp. 1-5). IEEE.
Long, J.-M., Yan, Z.-F., Shen, Y.-L., Liu, W.-J. and Wei, Q.-Y., 2018, October. Detection of epilepsy using MFCC-based feature and XGBoost. In 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (pp. 1-4). IEEE.
Chen, Z., Jiang, F., Cheng, Y., Gu, X., Liu, W. and Peng, J., 2018, January. XGBoost classifier for DDoS attack detection and analysis in SDN-based cloud. In 2018 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 251-256). IEEE.
Dinh, T.T.A., Liu, R., Zhang, M., Chen, G., Ooi, B.C. and Wang, J., 2018. Untangling blockchain: A data processing view of blockchain systems. IEEE Transactions on Knowledge and Data Engineering, 30(7), pp.1366-1385.
Kim, K., You, Y., Park, M. and Lee, K., 2018, July. DDoS mitigation: Decentralized CDN using private blockchain. In 2018 10th International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 693-696). IEEE.
Chakraborty, S., Aich, S. and Kim, H.-C., 2019, February. A secure healthcare system design framework using blockchain technology. In 2019 21st International Conference on Advanced Communication Technology (ICACT) (pp. 260-264). IEEE.
Gatteschi, V., Lamberti, F., Demartini, C., Pranteda, C. and Santamaria, V., 2018. To blockchain or not to blockchain: That is the question. IT Professional, 20(2), pp.62-74.
Lamberti, F., Gatteschi, V., Demartini, C., Pranteda, C. and Santamaria, V., 2017. Blockchain or not blockchain, that is the question of the insurance and other sectors. IT Professional, early access, doi: 10.1109/MITP.2017.265110355.
Liang, X., Zhao, J., Shetty, S., Liu, J. and Li, D., 2017, October. Integrating blockchain for data sharing and collaboration in mobile healthcare applications. In 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) (pp. 1-5). IEEE.
Lamberti, F., Gatteschi, V., Demartini, C., Pelissier, M., Gomez, A. and Santamaria, V., 2018. Blockchains can work for car insurance: Using smart contracts and sensors to provide on-demand coverage. IEEE Consumer Electronics Magazine, 7(4), pp.72-81.
Raikwar, M., Mazumdar, S., Ruj, S., Gupta, S.S., Chattopadhyay, A. and Lam, K.-Y., 2018, February. A blockchain framework for insurance processes. In 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS) (pp. 1-4). IEEE.
Sun, C., Li, Q., Li, H., Shi, Y., Zhang, S. and Guo, W., 2019. Patient cluster divergence based healthcare insurance fraudster detection. IEEE Access, 7, pp.14162-14170.
Vassiljeva, K., Tepljakov, A., Petlenkov, E. and Netsajev, E., 2017, May. Computational intelligence approach for estimation of vehicle insurance risk level. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 4073-4078). IEEE.
Muller, D. and Te, Y.-F., 2017, December. Insurance premium optimization using motor insurance policies–A business growth classification approach. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 4154-4158). IEEE.
Dhieb, N., Ghazzai, H., Besbes, H. and Massoud, Y., 2019, December. A very deep transfer learning model for vehicle damage detection and localization. In 2019 31st International Conference on Microelectronics (ICM) (pp. 158-161). IEEE.
Sowcik, M., Lindsey, J.L., and Rosch, D.M., 2012. A collective effort to understand formalized program review. Journal of Leadership Studies, 6(3), pp.67-72.
Wu, K. and Senior, H., 2016. Business librarians and new academic program review. Journal of Business & Finance Librarianship, 21(2), pp.114-134.
Coombs, V., 2017. Institutions should link program reviews to strategic plans. Inside Higher Ed. [Online]. Available: https://www.insidehighered.com/blogs/call-action-marketing-and-communications-higher-education/institutions-should-link-program
Bowker, L., 2016. Language and quality assurance: A case study highlighting the effects of power, resistance, and countertactics in academic program reviews. TTR, 29(2), pp.177-193.
Dougherty, K. and Reddy, V., 2013. Performance funding for higher education: What are the mechanisms? What are the impacts?. ASHE Higher Education Report, 39(2).
Novodvorsky, I., Marchello, E. and Elfring, L., 2022. Assessment work in an academic professional development center. In The Palgrave Handbook of Academic Professional Development Centers (pp. 97-108). Springer, Cham.
Siems, A.L. and Bell, M., 2018. Utilizing specialty in training examinations and annual program review to improve program curriculum. Academic Pediatrics, 18(5), p.E16.
Harnisch, T.L., 2011. Performance-based funding: A re-emerging strategy in public higher education financing (Policy Brief). American Association of State Colleges and Universities.
Dickeson, R.C., 2010. Prioritizing academic programs and services: Reallocating resources to achieve strategic balance. John Wiley & Sons.
Deming, D.J. and Figlio, D., 2016. Accountability in US education: Applying lessons from K-12 experience to higher education. Journal of Economic Perspectives, 30(3), pp.33-56.
Higher Learning Commission, (N.D.). Quality initiative. [Online]. Available: https://www.hlcommission.org/accreditation/quality-initiative.html
Permenter, A., 2016. Academic program review: From critical processes to implementation. Southern Association of Colleges and Schools Commission on Colleges, 121st Annual Meeting. [Online]. Available: http://www.sacscoc.org/2016amho/cs%20handouts/cs-62_permenter.pdf
Kuh, G.D., et al., 2011. Student success in college: Creating conditions that matter. John Wiley & Sons.
Drexel University, (N.D.). Academic program review. [Online]. Available: https://drexel.edu/pheal/assessmenteffectiveness/academic-review/
Nugent, E. and Larocco, S., 2014. Comprehensive review of an accelerated nursing program: A quality improvement project. Dimensions of Critical Care Nursing, 33(4), pp.226-233.
University of Oklahoma, 2009. Academic program review, chapter 3 – academic affairs. [Online]. Available: https://www.okhighered.org/state-system/policy-procedures/chapter%203-%202010%20final3.pdf#page=74
Backlund, P., et al., 2011. NCA program review standards: Background, application, and data. Communication Education, 60(3), pp.279-95.
Halonen, J.S. and Dunn, D.S., 2017. Avoiding the potholes of program review. The Chronicle of Higher Education.
Morriss-Olson, M., 2016. Feasibility checklist: The science of bringing new academic programs to life. Academic Impressions. [Online]. Available: https://www.academicimpressions.com
University of Illinois at Urbana-Champaign, (N.D.). Academic program review. [Online]. Available: https://provost.illinois.edu/assessment/academic-program-review/#sthash.c04mu9za.dpbs
Turner, L.N., 2016. Quality assurance in online graduate education: Program review processes and assessment techniques used in higher education. Graduate Theses and Dissertations.
Mattingly, K.D., Rice, M.C. and Berge, Z.L., 2012. Learning analytics as a tool for closing the assessment loop in higher education. Knowledge Management & E-Learning: An International Journal, 4(3).
Khan, S., Shakil, K.A. and Alam, M., 2017. Big data computing using cloud-based technologies, challenges and future perspectives. Arxiv Preprint Arxiv:1712.05233.
Murumba, J. and Micheni, E., 2017. Big data analytics in higher education: A review. The International Journal of Engineering and Science, 6(6), pp.14-21.
Mary, T.A.C. and Rose, P.J.A.L., 2020. The impact of graduate student’s perceptions towards the usage of cloud computing in higher education sectors. Universal Journal of Educational Research, 8(11), pp.5463-5478.
Emmanuel, I. and Stanier, C., 2016. Defining big data. Proceedings of the International Conference on Big Data and Advanced Wireless Technologies, pp.1-6.
Khanra, S., et al., 2020. Big data analytics in healthcare: A systematic literature review. Enterprise Information Systems, 14(7), pp.878-912.
Rosenbluth, G., et al., 2015. Variation in printed handoff documents: Results and recommendations from a multicenter needs assessment. Journal of Hospital Medicine, 10(8), pp.517-524.
Ashaari, M.A., et al., 2021. Big data analytics capability for improved performance of higher education institutions in the era of IR 4.0: A multi-analytical SEM & ANN perspective. Technological Forecasting and Social Change, 173, p.121119.
Cui, Y., et al., 2023. A survey on big data-enabled innovative online education systems during the COVID-19 pandemic. Journal of Innovation & Knowledge, 8(1), p.100295.
Naqishbandi, T.A. and Ayyanathan, N., 2020. Clinical big data predictive analytics transforming healthcare:-An integrated framework for promise towards value-based healthcare. Advances in Decision Sciences, Image Processing, Security and Computer Vision: International Conference on Emerging Trends in Engineering, Springer International Publishing, vol. 2, pp.545-561.
Alam, A., 2022. Cloud-based e-learning: Scaffolding the environment for adaptive e-learning ecosystem based on cloud computing infrastructure. Computer Communication, Networking and Iot: Proceedings of 5th ICICC 2021, Singapore: Springer Nature Singapore, vol. 2, pp.1-9.
Prottas, N., 2023. The power of words. Journal of Museum Education, 48(1), pp.1-6.
Komperla, R. C. A., 2021. AI-enhanced claims processing: Streamlining insurance operations. ISSN: 1539-1590 | E-ISSN: 2573-7104 Vol. 3 No. 2 (2021).
Eling, M., Nuessle, D., and Staubli, J., 2021. The impact of artificial intelligence along the insurance value chain and on the insurability of risks. The Geneva Papers on Risk and Insurance-Issues and Practice, pp.1-37.
Sinha, K. P., Sookhak, M., and Wu, S., 2021. Agentless insurance model based on modern artificial intelligence. In 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI) (pp.49-56). IEEE.
Koster, O., Kosman, R., and Visser, J., 2021. A checklist for explainable AI in the insurance domain. In International Conference on the Quality of Information and Communications Technology.
Volosovych, S., Zelenitsa, I., Kondratenko, D., and Szymla, 2021. Transformation of insurance technologies in the context of a pandemic. Insurance Markets and Companies, 12(1), pp.1-13.
Sarella, N. K., and Mangam, V. T., 2024. AI-driven natural language processing in healthcare: Transforming patient-provider communication. Indian Journal of Pharmacy Practice, 17(1), pp.21-26.
Dhieb, N., Ghazzai, H., Besbes, H., and Massoud, Y., 2020. A secure AI-driven architecture for automated insurance systems: Fraud detection and risk measurement. IEEE Access, 8, pp.58546-58558.
Baabdullah, A. M., et al., 2021. SMEs and artificial intelligence (AI): Antecedents and consequences of AI-based B2B practices. Industrial Marketing Management.
Balasubramanian, R., Libarikian, A., and McElhaney, D., 2021. Insurance 2030 - The impact of AI on the future of insurance. McKinsey & Company.
Ramagundam, S., 2021. Next gen linear TV: Content generation and enhancement with artificial intelligence. International Neurourology Journal, 25(4), pp.22-28.
Doultani, M., Bhagchandani, J., and Lalwani, S., 2021. Smart underwriting - A personalized virtual agent. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp.49-56). IEEE.
Wu, J. T., Dernoncourt, F., Gehrmann, S., et al., 2018. Behind the scenes: A medical natural language processing project. International Journal of Medical Informatics, 112, pp.68-73.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
