AI-Powered Pension Ecosystems: Transforming Claims, Payments, and Member Services
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I4P115Keywords:
AI, Pension Systems, Automation, Claims, Payments, Member ServicesAbstract
Pension systems provide significant protection for financial futures, especially for older individuals who need to receive payment immediately. However, these systems are struggling to meet growing demands, often due to older processes. The main areas of concern regarding the management of traditional pension systems are: inefficiencies tend to translate into higher operational costs; the risk of fraud; payment process delays; and the need for individualized service delivery and communication. These are weakening member confidence, and overwhelming financial institutions with the long-term security responsibility. AI can enhance the effectiveness of the pension systems by making them more accessible to users and assist in integrating aspects of automation, intelligence, and user-friendliness with variables of intelligence. AI can decrease claims processing, completely remove fraud processing, begin to forecast payment profiles, and enhance customer service through digital agents. Machine Learning and Natural Language Processing can also be used to validate AI-based claims processing, so less human input is needed to establish claim validity. Predictive Analytics ensures timely payments, and AI enables cost-effective, personalized customer service via accessible chatbots. AI can also enhance pensions by improving efficiency, reducing costs, and ensuring compliance. Pensions AI ecosystems foster trust through transparency and faster responses. Amid financial digital transformation, AI will help reinvent pensions as sustainable, equitable, and responsive
References
1. Anand, S., 2022. Automating Prior Authorization Decisions Using Machine Learning and Health Claim Data. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(3), pp.35-44.
2. coinchoose.com, 2018. Proposed Pension Ecosystem Architecture. Available at: https://www.coinchoose.com/news/akropolis-a-blockchain-based-pension-ecosystem/
3. docs.oracle.com, 2019. Pension Data Flow and Integration. Available at: https://docs.oracle.com/cd/F13810_02/hcm92pbr29/eng/hcm/hgpe/concept_UnderstandingthePensionPlanBusinessProcess-e35a75.html?pli=ul_d269e159_hgpe
4. Fritz-Morgenthal, S., Hein, B. and Papenbrock, J., 2022. Financial risk management and explainable, trustworthy, responsible AI. Frontiers in artificial intelligence, 5, p.779799.
5. Kannan, S., 2022. The Role Of AI And Machine Learning In Financial Services: A Neural Networkbased Framework For Predictive Analytics And Customercentric Innovations. Migration Letters, 19(6), pp.985-1000.
6. Malempati, M., 2022. Transforming Payment Ecosystems Through The Synergy Of Artificial Intelligence, Big Data Technologies, And Predictive Financial Modeling. Big Data Technologies, And Predictive Financial Modeling (November 07, 2022).
7. Malempati, M., Sriram, H.K., Dodda, A. and Challa, S.R., 2022. Leveraging Artificial Intelligence for Secure and Efficient Payment Systems: Transforming Financial Transactions, Regulatory Compliance, and Wealth Optimization. Abhishek and Challa, Srinivas Rao, Leveraging Artificial Intelligence for Secure and Efficient Payment Systems: Transforming Financial Transactions, Regulatory Compliance, and Wealth Optimization (December 23, 2022).
8. Mazumdar, A.C. and Jyoti, A., 2019. Automation of financial services using artificial intelligence with human touch. International Journal of Modern Engineering & Management Research.
9. mdpi.com, 2022. Financial Fraud Detection Based on Machine Learning. Available at: https://www.mdpi.com/2076-3417/12/19/9637
10. Mehrotra, A., 2019, April. Artificial intelligence in financial services–need to blend automation with human touch. In 2019 International Conference on Automation, Computational and Technology Management (ICACTM) (pp. 342-347). IEEE.
11. Nicoletti, B., 2020. Platforms for insurance 4.0. In Insurance 4.0: Benefits and Challenges of Digital Transformation (pp. 173-259). Cham: Springer International Publishing.
12. researchgate.net, 2020. Pre-Processing and Security Workflow. Available at: https://www.researchgate.net/figure/Pre-processing-workflow-including-functions-and-packages-used-to-implement-each-step_fig1_346128735
13. Truby, J., Brown, R. and Dahdal, A., 2020. Banking on AI: mandating a proactive approach to AI regulation in the financial sector. Law and Financial Markets Review, 14(2), pp.110-120.
14. Wewege, L., Lee, J. and Thomsett, M.C., 2020. Disruptions and digital banking trends. Journal of Applied Finance and Banking, 10(6), pp.15-56.