Citation:

Kirti Vedi, 2026. "A Review of Human Factors in AI-Powered Underwriting Systems: Trust, Cognitive Load, and Decision Quality", ESP Journal of Engineering & Technology Advancements  6(1): 1-11.

Abstract:

Human factors significantly affect the productivity of AI-related underwriting systems, with trust, cognitive load, and quality of decision being the leading factors. The implementation of AI has made the data collection process automatic, improved the decision-making accuracy, and opened up avenues for sophisticated risk assessment. The insights that AI can provide from large amounts of unprocessed data cannot be compared to the human skills for understanding the context, making good judgment, and doing personal customer interactions, which are still indispensable. The ability to provide user-friendly interfaces, clear outputs, and adjustable processes diminishes the mental effort and accelerates the decision-making process, thus nurturing human-machine collaboration effectively. The inclusion of Cognitive Load Theory and Need for Cognition makes it possible for AI systems to adapt to users of different skill levels, hence bettering the users' understanding and trust. The uses of robot-advisory, fraud detection, personalized recommendations, and algorithmic trading are good examples of how interpretability and accountability can be integrated into the AI systems. Nevertheless, these improvements come with issues such as invasion of privacy of data, absence of governing laws, difficulties in merging systems, moral dilemmas, and bias in algorithms. Solving these problems will ensure that AI-powered underwriting will always be accurate, quick, and ethically sound. In the end, the trust, fairness, and performance that lasts will be the result of the AI systems being designed to support human thinking rather than to take over.

References:

[1] N. Malali, “The Role Of Devsecops In Financial Ai Models: Integrating Security At Every Stage Of Ai/Ml Model Development In Banking And Insurance,” IJETRM, vol. 6, no. 11, p. 218, 2022, doi: 10.5281/zenodo.15239176.

[2] V. Thaker, S. Rawal, H. Shukla, I. Dave, A. Jani, and M. Thaker, “The Dual Impact of Artificial Intelligence on Human Cognition: A Comprehensive Review,” J. Int. Commer. Law Technol., vol. 6, no. 1, pp. 34–35, Aug. 2025, doi: 10.61336/Jiclt/25-01-04.

[3] B. R. Ande, “Federated Learning and Explainable AI for Decentralized Fraud Detection in Financial Systems,” J. Inf. Syst. Eng. Manag., vol. 10, no. 35, pp. 48–56, 2025.

[4] J. Han and D. Ko, “Trust Formation, Error Impact, and Repair in Human–AI Financial Advisory: A Dynamic Behavioral Analysis,” Behav. Sci. (Basel)., vol. 15, no. 10, p. 1370, Oct. 2025, doi: 10.3390/bs15101370.

[5] Kishore Subramanya Hebbar, “AI-DRIVEN REAL-TIME FRAUD DETECTION USING KAFKA STREAMS IN FINTECH,” Int. J. Appl. Math., vol. 38, no. 6s, pp. 770–782, Oct. 2025, doi: 10.12732/ijam.v38i6s.433.

[6] L. Aquilino, C. Di Dio, F. Manzi, D. Massaro, P. Bisconti, and A. Marchetti, “Decoding Trust in Artificial Intelligence: A Systematic Review of Quantitative Measures and Related Variables,” Informatics, vol. 12, no. 3, p. 70, Jul. 2025, doi:10.3390/informatics12030070.

[7] S. Dodda, N. Kamuni, J. S. Arlagadda, V. S. M. Vuppalapati, and P. Vemasan, “A Survey of Deep Learning Approaches for Natural Language Processing Tasks,” Int. J. Recent Innov. Trends Comput. Commun., vol. 9, no. 12, Dec. 2021, doi: 10.1109/TNNLS.2020.2979670.

[8] W. Almutairi and I. Almatrodi, “Trust Under Bounded Rationality: Exploring Human-AI Interaction in Decision-Making Through Large Language Models,” Sage Open, vol. 15, no. 4, Oct. 2025, doi: 10.1177/21582440251380135.

[9] R. Palwe, “Adaptive human: AI decision support for high-stakes financial advice,” Int. J. Comput. Artif. Intell., vol. 6, no. 2, pp. 385-392, Jul. 2025, doi: 10.33545/27076571.2025.v6.i2e.226.

[10] S. Narne, S. Dodda, T. Adedoja, M. M. T. Ayyalasomayajula, and S. Chintala, “AI-Driven Decision Support Systems in Management: Enhancing Strategic Planning and Execution,” Int. J. Recent Innov. Trends Comput. Commun., vol. 12, no. 1, pp. 268–276, 2024.

[11] C. Patel, “Integration of AI in Customer Relationship Management (CRM) for Improved Sales Outcomes,” Int. J. Emerg. Res. Eng. Technol., vol. 6, no. 4, pp. 137–145, 2025, doi: 10.63282/3050-922X.IJERET-V6I4P117.

[12] S. R. P. Madugula and N. Malali, “AI-powered life insurance claims adjudication using LLMs and RAG Architectures,” Int. J. Sci. Res. Arch., no. April, 2025, doi: 10.30574/ijsra.2025.15.1.0867.

[13] Y. Macha, “A Data-Driven Framework for Medical Insurance Cost Prediction Using Efficient AI Approaches,” Int. J. Res. Anal. Rev., vol. 11, no. 4, pp. 887–893, 2024.

[14] S. K. Tiwari, “Quality Assurance Strategies in Developing High-Performance Financial Technology Solutions,” Int. J. data Sci. Mach. Learn., vol. 05, no. 01, pp. 323–335, Jun. 2025, doi: 10.55640/ijdsml-05-01-26.

[15] S. Bhattacharya, G. Castignani, L. Masello, and B. Sheehan, “AI revolution in insurance: bridging research and reality,” Front. Artif. Intell., vol. 8, Apr. 2025, doi: 10.3389/frai.2025.1568266.

[16] J. Tase, “Generative AI in Insurance Industries: Transforming Workflows and Enhancing Customer Experience,” Int. Res. J. Eng. Technol., vol. 11, no. 07, pp. 511–524, 2024.

[17] B. R. Cherukuri, “AI-powered personalization: How machine learning is shaping the future of user experience,” Int. J. Sci. Res. Arch., vol. 12, no. 1, pp. 3111–3126, Jun. 2024, doi: 10.30574/ijsra.2024.12.1.0961.

[18] R. J. S. K. Das and Y. Makin, “Behavioral Risk Tolerance in U.S. Retirement Planning Vs. Property Insurance: A Comparative Analysis,” Int. J. Appl. Math., vol. 38, pp. 41–70, 2025.

[19] T. Kumar and V. Shah, “Leadership in digital transformation: Enhancing customer value through AI-driven innovation in financial services marketing,” Int. J. Sci. Res. Arch., vol. 15, no. 3, pp. 618–627, Jun. 2025, doi: 10.30574/ijsra.2025.15.3.1767.

[20] V. Prajapati, “Exploring Machine Learning Models for Fraud Identification Through Credit Cards in Financial Market,” in 2025 Global Conference in Emerging Technology (GINOTECH), 2025, pp. 1–6. doi: 10.1109/GINOTECH63460.2025.11076669.

[21] S. B. Karri, S. Gawali, S. Rayankula, and P. Vankadara, “AI Chatbots in Banking: Transforming Customer Service and Operational Efficiency,” in Advancements in Smart Innovations, Intelligent Systems, and Technologies, 2025, pp. 61–81. doi: 10.3233/FAIA251498.

[22] D. R. Jaiswal, “Impact of AI in the General Insurance underwriting factors,” Cent. Eur. Manag. J., vol. 31, pp. 697–705, 2023, doi: 10.57030/23364890.cemj.31.2.72.

[23] A. Parupalli, “The Evolution of Financial Decision Support Systems : The Evolution of Financial Decision Support Systems : From BI Dashboards to Predictive Analytics,” KOS J. Bus. Manag., vol. 1, no. 1, pp. 1–8, 2025.

[24] H. V. R. Yeddula, “The Transformative Impact Of Ai On Insurance Underwriting: A Technical Analysis,” Int. J. Res. Comput. Appl. Inf. Technol., vol. 8, no. 1, pp. 2214–2229, Feb. 2025, doi: 10.34218/IJRCAIT_08_01_161.

[25] A. R. Bilipelli, “Forecasting the Evolution of Cyber Attacks in FinTech Using Transformer-Based Time Series Models,” Int. J. Res. Anal. Rev. | Ijrar.Org, vol. 10, no. 3, pp. 383–389, 2023, [Online]. Available: https://www.ijrar.org/papers/IJRAR23C3692.pdf

[26] Srinivasa Rao Kurakula, “The Role of AI in Transforming Enterprise Systems Architecture for Financial Services Modernization,” J. Comput. Sci. Technol. Stud., vol. 7, no. 4, pp. 181–186, 2025, doi: 10.32996/jcsts.2025.7.4.21.

[27] M. Fox, G. Vaidyanathan, and J. L. Breese, “The Impact Of Artificial Intelligence On Musicians,” Issues Inf. Syst., vol. 25, no. 3, pp. 267–276, 2024, doi: 10.48009/3_iis_2024_121.

[28] N. Malali, “Exploring Artificial Intelligence Models for Early Warning Systems with Systemic Risk Analysis in Finance,” in 2025 International Conference on Advanced Computing Technologies (ICoACT), 2025, pp. 1–6. doi: 10.1109/ICoACT63339.2025.11005357.

[29] F. M. Cau and L. D. Spano, “Exploring the Impact of Explainable AI and Cognitive Capabilities on Users’ Decisions,” pp. 1–44, May 2025.

[30] Tanmaykumar Vithaldas Shah, “Leadership in digital transformation: Enhancing customer value through AI-driven innovation in financial services marketing,” Int. J. Sci. Res. Arch., vol. 15, no. 3, pp. 618–627, Jun. 2025, doi: 10.30574/ijsra.2025.15.3.1767.

[31] H. Tejeda, A. Kumar, P. Smyth, and M. Steyvers, “AI-Assisted Decision-making: a Cognitive Modeling Approach to Infer Latent Reliance Strategies,” Comput. Brain Behav., vol. 5, no. 4, pp. 491–508, Dec. 2022, doi: 10.1007/s42113-022-00157-y.

[32] A. Parupalli, “The Evolution of Financial Decision Support Systems : From BI Dashboards to Predictive Analytics,” KOS J. Bus. Manag., vol. 1, no. 1, pp. 1–8, 2023.

[33] Ruhul Quddus Majumder, “A Review of Anomaly Identification in Finance Frauds Using Machine Learning Systems,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 5, no. 10, pp. 101–110, Apr. 2025, doi: 10.48175/IJARSCT-25619.

[34] C. Xu and S.-E. Cho, “Factors Affecting Human–AI Collaboration Performances in Financial Sector: Sustainable Service Development Perspective,” Sustainability, vol. 17, no. 10, p. 4335, May 2025, doi: 10.3390/su17104335.

[35] H. P. Kapadia and K. C. Chittoor, “AI Chatbots for Financial Customer Service: Challenges & Solutions,” J. Adv. Futur. Res., vol. 2, no. 2, pp. 1–7, 2024.

[36] A. Pathak and V. Bansal, “AI as decision aid or delegated agent: The effects of trust dimensions on the adoption of AI digital agents,” Comput. Hum. Behav. Artif. Humans, vol. 2, no. 2, p. 100094, Aug. 2024, doi: 10.1016/j.chbah.2024.100094.

[37] Y. Bao, W. Gong, and K. Yang, “A Literature Review of Human–AI Synergy in Decision Making: From the Perspective of Affordance Actualization Theory,” Systems, vol. 11, no. 9, p. 442, Aug. 2023, doi: 10.3390/systems11090442.

[38] P. K. Peddamukkula, “The Impact of AI-Driven Automated Underwriting on the Life Insurance Industry,” Int. J. Innov. Resarch Sci. Eng. Technol., vol. 7, no. 5, Jun. 2024, doi: 10.15680/1a1fxe57.

[39] D. B. Vuković, S. Dekpo-Adza, and S. Matović, “AI integration in financial services: a systematic review of trends and regulatory challenges,” Humanit. Soc. Sci. Commun., vol. 12, no. 1, p. 562, Apr. 2025, doi: 10.1057/s41599-025-04850-8.

[40] S. Sachan, F. Almaghrabi, J.-B. Yang, and D.-L. Xu, “Human-AI collaboration to mitigate decision noise in financial underwriting: A study on FinTech innovation in a lending firm,” Int. Rev. Financ. Anal., vol. 93, p. 103149, May 2024, doi: 10.1016/j.irfa.2024.103149.

[41] N. Rahul, “Transforming Underwriting with AI: Evolving Risk Assessment and Policy Pricing in P&C Insurance,” Int. J. AI, BigData, Comput. Manag. Stud., vol. 4, no. 3, 2023, doi: 10.63282/3050-9416.IJAIBDCMS-V4I3P110.

[42] C. S. Pareek, “Unmasking Bias: A Framework for Testing and Mitigating AI Bias in Insurance Underwriting Models,” J. Artif. Intell. Mach. Learn. Data Sci., vol. 1, no. 1, pp. 1736–1741, Nov. 2023, doi: 10.51219/JAIMLD/Chandra-shekhar-pareek/377.

[43] E. Owens, B. Sheehan, M. Mullins, M. Cunneen, J. Ressel, and G. Castignani, “Explainable Artificial Intelligence (XAI) in Insurance,” Risks. 2022. doi: 10.3390/risks10120230.

[44] K. M. Tekale and N. Rahu, “AI and Predictive Analytics in Underwriting, 2022 Advancements in Machine Learning for Loss Prediction and Customer Segmentation,” Int. J. Artif. Intell. Data Sci. Mach. Learn., vol. 3, no. 1, 2022, doi: 10.63282/3050-9262.IJAIDSML-V3I1P111.

Keywords:

AI-Powered Underwriting, Human–AI Collaboration, Cognitive Load, Trust and Decision Quality, Explainable AI (XAI), Financial Services, Risk Assessment.