| dc.contributor.advisor | 蔡子傑 | zh_TW |
| dc.contributor.advisor | Tsai, Tzu-Chieh | en_US |
| dc.contributor.author (Authors) | 翁世育 | zh_TW |
| dc.contributor.author (Authors) | Weng, Shih-Yu | en_US |
| dc.creator (作者) | 翁世育 | zh_TW |
| dc.creator (作者) | Weng, Shih-Yu | en_US |
| dc.date (日期) | 2024 | en_US |
| dc.date.accessioned | 5-Aug-2024 13:55:59 (UTC+8) | - |
| dc.date.available | 5-Aug-2024 13:55:59 (UTC+8) | - |
| dc.date.issued (上傳時間) | 5-Aug-2024 13:55:59 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0110971030 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/152768 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊科學系碩士在職專班 | zh_TW |
| dc.description (描述) | 110971030 | zh_TW |
| dc.description.abstract (摘要) | 鑑於商業保險市場迅速發展，許多人擁有多份保單，這些保單隨著時間更新，條款也不斷調整。由於保險條款的專業性，保戶在理解上常需依賴保險專家或業務員的建議。然而，業務員也可能無法記住所有條款，需要諮詢公司專業人員，這會耗費時間且缺乏即時性，或因利益衝突或不當得利，導致理賠糾紛。為減少糾紛，保戶需詳細了解保單條款、保障範圍、除外條款及賠償限制等內容，並尋求專業建議。
為解決這些問題，運用大語言模型和RAG技術，構建交談式保險理賠暨推薦系統。該系統能解析保單條款，分析保戶的理賠問題，通過關鍵字提取並計算條款匹配的信心分數，將結果回傳並以易懂的方式回答保戶，協助他們輕鬆獲得相應答覆。系統將複雜保險條款轉換為易於理解的語言，降低理賠過程的困難和不確定性。
系統設計需考慮許多因素，包括數據正確性、演算法效率、友善地使用者介面及隱私安全問題。系統需持續更新，以適應保險市場變化、新法規、新產品和技術標準等，最終，這種交談式保險
理賠暨推薦系統能提高理賠效率和準確性，減少理賠糾紛，保護保戶權益。同時，也能幫助保險公司提升客戶服務質量和滿意度，提高業務效率並降低成本，為保戶和保險公司帶來多重益處。 | zh_TW |
| dc.description.abstract (摘要) | Due to the rapid development of the commercial insurance market, many people hold multiple policies that are updated over time, with terms that are constantly adjusted. Because of the specialized nature of insurance terms, policyholders often need to rely on the advice of insurance experts or agents for understanding. However, agents may not remember all the terms and might need to consult company specialists, which can be time-consuming and lack immediacy. Additionally, conflicts of interest or improper gains can lead to claims disputes. To reduce such disputes, policyholders need to thoroughly understand the policy terms, coverage scope, exclusions, and compensation limits, and seek professional advice.
To address these issues, a conversational insurance claims and recommendation system using large language models and Retrieval-Augmented Generation (RAG) technology can be developed. This system can interpret policy terms, analyze policyholder claims issues, extract keywords, and calculate the confidence scores of term matches. The results are then returned and presented in an understandable manner, assisting policyholders in obtaining relevant answers easily. The system translates complex insurance terms into easy-to-understand language, reducing the difficulty and uncertainty of the claims process.
The system design needs to consider many factors, including data accuracy, algorithm efficiency, user interface friendliness, and privacy and security issues. The system must be continuously updated to adapt to changes in the insurance market, new regulations, new products, and technical standards. Ultimately, this conversational insurance claims and recommendation system can improve claims efficiency and accuracy, reduce claims disputes, and protect policyholders' rights. At the same time, it can help insurance companies enhance customer service quality and satisfaction, improve business efficiency, and reduce costs, bringing multiple benefits to both policyholders and insurance companies. | en_US |
| dc.description.tableofcontents | 第一章緒論 1
第一節研究動機 1
第二節 問題陳述 3
第三節 目標和貢獻 5
第二章 文獻探討 6
第一節 保險理賠流程 6
第二節 自動化技術在保險領域的應用 8
第三節 推薦系統在保險領域的應用 10
第四節 BERT模型 11
第五節 開源大型語言模型 13
第六節 醫療保險手術/處置項目 14
第三章 研究方法 16
第一節 研究設計 16
第二節 數據收集與處理 18
1.保單條款數據： 18
2.醫療保險爭議案例: 20
3.數據清理: 21
第三節 Llama3 / Gemma / Phi3_Mini 模型 24
第四節 超參數說明 24
第五節 Retrieval-Augmented Generation (RAG) 25
第六節 實驗執行 27
第七節 系統介面設計 29
第四章 實驗結果 31
第一節 開源模型效能評估 31
1.模型的微調 31
2.本地端運行的正確率與反應時間 33
第二節 使用者體驗評估 34
1.實際操作 34
2. 問卷調查 35
第五章 結論 37
第六章 討論與未來研究方向 38
第七章 參考文獻 39
第八章 附錄 41 | zh_TW |
| dc.format.extent | 3685050 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0110971030 | en_US |
| dc.subject (關鍵詞) | 人工智慧 | zh_TW |
| dc.subject (關鍵詞) | 大型語言模型 | zh_TW |
| dc.subject (關鍵詞) | 檢索增強生成 | zh_TW |
| dc.subject (關鍵詞) | 保險賠償 | zh_TW |
| dc.subject (關鍵詞) | 語意辨識 | zh_TW |
| dc.subject (關鍵詞) | 資訊安全 | zh_TW |
| dc.subject (關鍵詞) | Artificial Intelligence | en_US |
| dc.subject (關鍵詞) | Large language model | en_US |
| dc.subject (關鍵詞) | RAG | en_US |
| dc.subject (關鍵詞) | Insurance compensation | en_US |
| dc.subject (關鍵詞) | Semantic recognition | en_US |
| dc.subject (關鍵詞) | Information security | en_US |
| dc.title (題名) | 智慧保險理賠評估暨推薦系統設計以醫療險為例 | zh_TW |
| dc.title (題名) | Design of an Intelligent Insurance Claim Assessment and Recommendation System: Case Study on Health Insurance | en_US |
| dc.type (資料類型) | thesis | en_US |
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