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題名 企業建構新興信用評分模型所面臨之個資保護法規範與因應－以借貸領域之應用為中心
Personal Data Protection Under Alternative Credit Scoring: A Focus on the Applications of Lending Market
作者 王詩函
Wang, Shih-Han
貢獻者 鄭菀瓊
王詩函
Wang, Shih-Han
關鍵詞 新興信用評分模型
信用評分
個人資料保護法
一般資料保護規範
隱私權
人工智慧
大數據
Alternative credit scoring model
Credit scoring
General data protection regulation
Personal data protection law
Privacy
Artificial intelligence
Big data
日期 2022
上傳時間 1-Aug-2022 18:52:05 (UTC+8)
摘要 信用評分之高低將決定個人取得金融服務之便利程度及成本。在過往，缺少信用紀錄的族群因信用分數較低，難以透過傳統金融機構獲取所需之貸款服務，在需求尚未被滿足及技術發展的推波助瀾下，借貸市場之業者逐漸發展出有別於傳統徵信機構之評分機制，透過更成熟的建模技術與大數據之運用，試圖建立能更準確描繪申貸者輪廓之信用評分模型（又稱為新興信用評分模型），提升其貸款服務觸及之範圍，並藉此發展新型態之商業模式，然而，企業透過新興信用評分模型拓展新商機的同時，亦會因為模型建立的過程中涉及大量資料之蒐集與利用，而需先釐清其中是否有涵蓋個人資料，以避免資料主體隱私權之侵害而需承擔相關法律責任。

近年來，個人資料被非法外洩或盜用之情事屢見不鮮，因而提升各國對於個人資料保護之意識，紛紛設立或修訂法規以落實更完善之個人資料與隱私保障，而在本文研究之我國、歐盟及美國的個人資料保護規範中，皆可發現其射程範圍不僅止於境內之企業，因此我國企業在建立新興信用評分模型時，恐將同時受多國法規範之拘束。

本論文將先比較我國、歐盟及美國對於個人資料之定義及判斷，並接續探討企業在建立新興信用評分模型的過程中，其所處理之資料在各國法下是否落入個人資料之範疇，而使其需履行相關義務，最後從個人資料之流程管理面及技術處理面出發，嘗試提供企業相關之法律遵循措施，以降低其在建立新興信用評分模型時所面臨之法律風險。
The credit rating is an important factor that would influence the cost and possibility when obtaining the financial services. In the past, people who lacked credit histories would have a lower credit score thus fail to apply loans through traditional financial institutions. With the unmet needs of the borrowers and the development of the technology, companies of the lending market tend to develop a new credit scoring mechanism by using more complicated algorithms and big data. By applying the new credit scoring model (so called alternative credit scoring model), companies are able to target their consumers more precisely hence exploit a new market and build different business models. However, the process of building alternative credit scoring models would involve the use of big data. Companies should clarify whether there is any personal data in the model in order to avoid the invasion of the data subject’s privacy.

In recent years, the leaking and misusing of personal data has raised the awareness of personal data protection in various countries, pushing the authorities to establish or revise personal data protection law. Based on the findings of this paper, personal data protection laws tend to have extraterritorial effect, which means companies may be subject to multiple regulations of different countries at the same time.

This paper compares the definition of personal data in three countries including R.O.C, EU and the U.S. Then discussed whether the data used in the alternative credit scoring model fall into the category of personal data under the laws of the three countries. And last, this paper will provide some compliance suggestions from the data management perspective and the technical processing aspect, trying to lower the legal risk of the companies when building the alternative credit scoring model.
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描述 碩士
國立政治大學
科技管理與智慧財產研究所
108364213
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108364213
資料類型 thesis
dc.contributor.advisor 鄭菀瓊zh_TW
dc.contributor.author (Authors) 王詩函zh_TW
dc.contributor.author (Authors) Wang, Shih-Hanen_US
dc.creator (作者) 王詩函zh_TW
dc.creator (作者) Wang, Shih-Hanen_US
dc.date (日期) 2022en_US
dc.date.accessioned 1-Aug-2022 18:52:05 (UTC+8)-
dc.date.available 1-Aug-2022 18:52:05 (UTC+8)-
dc.date.issued (上傳時間) 1-Aug-2022 18:52:05 (UTC+8)-
dc.identifier (Other Identifiers) G0108364213en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141359-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 科技管理與智慧財產研究所zh_TW
dc.description (描述) 108364213zh_TW
dc.description.abstract (摘要) 信用評分之高低將決定個人取得金融服務之便利程度及成本。在過往，缺少信用紀錄的族群因信用分數較低，難以透過傳統金融機構獲取所需之貸款服務，在需求尚未被滿足及技術發展的推波助瀾下，借貸市場之業者逐漸發展出有別於傳統徵信機構之評分機制，透過更成熟的建模技術與大數據之運用，試圖建立能更準確描繪申貸者輪廓之信用評分模型（又稱為新興信用評分模型），提升其貸款服務觸及之範圍，並藉此發展新型態之商業模式，然而，企業透過新興信用評分模型拓展新商機的同時，亦會因為模型建立的過程中涉及大量資料之蒐集與利用，而需先釐清其中是否有涵蓋個人資料，以避免資料主體隱私權之侵害而需承擔相關法律責任。

近年來，個人資料被非法外洩或盜用之情事屢見不鮮，因而提升各國對於個人資料保護之意識，紛紛設立或修訂法規以落實更完善之個人資料與隱私保障，而在本文研究之我國、歐盟及美國的個人資料保護規範中，皆可發現其射程範圍不僅止於境內之企業，因此我國企業在建立新興信用評分模型時，恐將同時受多國法規範之拘束。

本論文將先比較我國、歐盟及美國對於個人資料之定義及判斷，並接續探討企業在建立新興信用評分模型的過程中，其所處理之資料在各國法下是否落入個人資料之範疇，而使其需履行相關義務，最後從個人資料之流程管理面及技術處理面出發，嘗試提供企業相關之法律遵循措施，以降低其在建立新興信用評分模型時所面臨之法律風險。		zh_TW
dc.description.abstract (摘要) The credit rating is an important factor that would influence the cost and possibility when obtaining the financial services. In the past, people who lacked credit histories would have a lower credit score thus fail to apply loans through traditional financial institutions. With the unmet needs of the borrowers and the development of the technology, companies of the lending market tend to develop a new credit scoring mechanism by using more complicated algorithms and big data. By applying the new credit scoring model (so called alternative credit scoring model), companies are able to target their consumers more precisely hence exploit a new market and build different business models. However, the process of building alternative credit scoring models would involve the use of big data. Companies should clarify whether there is any personal data in the model in order to avoid the invasion of the data subject’s privacy.

In recent years, the leaking and misusing of personal data has raised the awareness of personal data protection in various countries, pushing the authorities to establish or revise personal data protection law. Based on the findings of this paper, personal data protection laws tend to have extraterritorial effect, which means companies may be subject to multiple regulations of different countries at the same time.

This paper compares the definition of personal data in three countries including R.O.C, EU and the U.S. Then discussed whether the data used in the alternative credit scoring model fall into the category of personal data under the laws of the three countries. And last, this paper will provide some compliance suggestions from the data management perspective and the technical processing aspect, trying to lower the legal risk of the companies when building the alternative credit scoring model.		en_US
dc.description.tableofcontents 第一章 緒論 1
第一節 研究動機與目的 1
第二節 研究架構 3
第三節 研究範圍 3
第二章 新興信用評分模型之介紹 4
第一節 何謂新興信用評分模型 4
第一項 新興信用評分模型之發展 5
第二項 新興信用評分模型之應用 7
第二節 新興信用評分模型所涵蓋之數據 13
第一項 傳統的徵信數據 14
第二項 另類數據 15
第三節 新興信用評分模型和傳統信用評分模型之差異 17
第一項 傳統信用評分模型 19
第二項 新興信用評分模型 20
第三項 小結 26
第三章 各國之個人資料定義與相關規範 26
第一節 個人資料與隱私權 28
第二節 我國個資法之規定 30
第一項 應以何者之識別能力為判斷依據 32
第二項 具備已知且合理之識別方法與否 36
第三項 小結 41
第三節 歐盟GDPR之規定 43
第一項 一般個資及特殊類型個資 45
第二項 假名化資料 47
第三項 個資界線之判斷 50
第四項 小結 55
第四節 美國個資之相關規定 57
第一項 美國聯邦法之規範 57
第二項 美國各州之規範 62
第三項 小結與個資定義之初步釐清 79
第五節 各國個資定義與規範對象之比較 80
第四章 企業建構模型之法律風險與因應措施 84
第一節 新興信用評分模型之個資侵害風險 85
第一項 文字資料轉換為數值型態之常見方式與影響 85
第二項 新興信用評分模型涵蓋之個資比例與風險評估 89
第二節 自動化決策模型之規範與企業應履行義務 94
第一項 現行法律規範 95
第二項 將來立法趨勢 105
第三節　企業於管理面及技術面之因應措施 110
第一項 企業於管理面之因應措施 110
第二項 企業於技術面之因應措施 122
第五章 研究結論與建議 130
第一節 研究發現 130
第一項 各國對於個資保護之態度趨於積極 130
第二項 個資判斷標準之不確定性及未來變化 133
第三項 新興信用評分模型內含大量個資 134
第四項 企業使用個資時可採取之因應法規措施 135
第二節 研究限制與建議 137
第一項 研究限制 137
第二項 研究建議 138

 		zh_TW
dc.format.extent 6770045 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108364213en_US
dc.subject (關鍵詞) 新興信用評分模型zh_TW
dc.subject (關鍵詞) 信用評分zh_TW
dc.subject (關鍵詞) 個人資料保護法zh_TW
dc.subject (關鍵詞) 一般資料保護規範zh_TW
dc.subject (關鍵詞) 隱私權zh_TW
dc.subject (關鍵詞) 人工智慧zh_TW
dc.subject (關鍵詞) 大數據zh_TW
dc.subject (關鍵詞) Alternative credit scoring modelen_US
dc.subject (關鍵詞) Credit scoringen_US
dc.subject (關鍵詞) General data protection regulationen_US
dc.subject (關鍵詞) Personal data protection lawen_US
dc.subject (關鍵詞) Privacyen_US
dc.subject (關鍵詞) Artificial intelligenceen_US
dc.subject (關鍵詞) Big dataen_US
dc.title (題名) 企業建構新興信用評分模型所面臨之個資保護法規範與因應－以借貸領域之應用為中心zh_TW
dc.title (題名) Personal Data Protection Under Alternative Credit Scoring: A Focus on the Applications of Lending Marketen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 中文文獻
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dc.identifier.doi (DOI) 10.6814/NCCU202200861en_US