本研究探討語音機器人導入保險業電訪流程之成效與風險控管意涵。在金融科技與人工智慧快速發展下，傳統人工電訪面臨效率不足、成本偏高及服務品質不易標準化等問題，且須符合高度監理要求。本文以個案公司試辦專案為研究對象，整合科技接受模型（TAM）、服務品質模型（SERVQUAL）與監理科技（RegTech），建構「效率－品質－合規」分析架構，並以電訪紀錄與質檢資料進行實證分析。

研究結果顯示，語音機器人完成率達83％、質檢正確率達99.96％，且未完成案件均能有效轉接人工，顯示其於效率與流程控制上呈現正向結果。惟本研究採關聯性分析觀點，指出成效亦可能受流程優化與人員訓練影響。進一步發現，監理模式由抽樣檢查轉向全流程監控，風險結構由人為風險轉為系統性風險，本文提出「可控制的自動化」概念，強調AI導入須兼顧效率、品質與合規之平衡。

This study examines the effectiveness and risk management implications of implementing a voice robot in insurance telephone interview operations. With the rapid advancement of financial technology (FinTech) and artificial intelligence (AI), traditional manual interview processes face challenges such as limited efficiency, high labor costs, and inconsistent service quality, while also being subject to strict regulatory requirements. Using a pilot project conducted by a case company, this study integrates the Technology Acceptance Model (TAM), the SERVQUAL model, and Regulatory Technology (RegTech) to construct a three-dimensional analytical framework of efficiency, quality, and compliance, based on interview records and quality assurance data.
The empirical results indicate that the voice robot achieved an 83％ completion rate and a 99.96％ quality accuracy rate, with all unfinished cases successfully transferred to human agents, demonstrating significant improvements in operational efficiency and process control. However, adopting an association-based perspective, this study acknowledges that the observed improvements may also be influenced by process optimization and employee training. Furthermore, the findings suggest a shift in supervisory practices from sampling-based reviews to full-process monitoring systems, alongside a transition in risk structure from human operational risk to system-oriented risk. The study proposes the concept of “Controlled Automation,” emphasizing the need to balance efficiency, service quality, and regulatory compliance in AI implementation.

中文部分
個人資料保護法。（2025 年 11 月 11 日修正）。全國法規資料庫。法務部。https://law.moj.gov.tw/LawClass/LawAll.aspx?PCode=I0050021
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金融消費者保護法。（2023 年 12 月 06 日修正）。全國法規資料庫。法務部。https://law.moj.gov.tw/LawClass/LawAll.aspx?pcode=G0380226
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英文部分
Journal articles
Alonso Robisco, A., & Carbó Martínez, J. M. (2022). Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction. Financial Innovation, 8, Article 70. https://doi.org/10.1186/s40854-022-00366-1
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Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.
Sajid, M. I. (2025). Stacy: A voice AI agent conducting risk assessment for small business insurance. Open Journal of Applied Sciences, 15, 834–853. https://doi.org/10.4236/ojapps.2025.154056
Sargeant, H. (2023). Algorithmic decision-making in financial services: Economic and normative outcomes in consumer credit. AI and Ethics, 3, 1295–1311. https://doi.org/10.1007/s43681-022-00236-7
Weber, P., Carl, K. V., & Hinz, O. (2024). Applications of explainable artificial intelligence in finance: A systematic review of finance, information systems, and computer science literature. Management Review Quarterly, 74(2), 867–907. https://doi.org/10.1007/s11301-023-00320-0

Internet
Bank for International Settlements. (2024, March 21). BIS Innovation Hub expands suptech and regtech research to include monetary policy tech. https://www.bis.org/about/bisih/topics/suptech_regtech.htm
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https://newsroom.bankofamerica.com/content/newsroom/press-releases/2025/08/a-decade-of-ai-innovation--bofa-s-virtual-assistant-erica-surpas.html
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KPMG. (2020, October). Boardroom pulse in the financial services sector: Issue 2. https://assets.kpmg.com/content/dam/kpmg/cn/pdf/en/2020/10/boardroom-pulse-in-the-financial-services-sector-issue2.pdf