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| Title: | 學習型預測模型應用於外籍移工借貸
The learning-based credit and risk assessment models for the P2P lending of migrant workers |
| Authors: | 李昀儒
Lee, Yun-Ru |
| Contributors: | 蔡瑞煌
Tsaih, Rua-Huan
李昀儒
Lee, Yun-Ru |
| Keywords: | 學習型信用預測模型
學習型風險預測模型
外籍移工
自適應單隱藏層前饋神經網路
learning-based credit assessment model
learning-based risk assessment model
migrant workers
adaptive single hidden layer feed-forward network |
| Date: | 2022 |
| Issue Date: | 2022-08-01 17:26:19 (UTC+8) |
| Abstract: | 外籍移工是台灣不可或缺的勞動力之一，其人數從民國80年不到3000人，到民國111年已成長到66萬人，占台灣總勞動人口數約2.8%，是社會中不可忽視的一群勞動力。外籍移工每月會將大部分所得匯回母國以貼補家用，當今天在金錢上有急需時，往往會因為語言隔閡及信託資料缺乏等因素，導致他們在台灣借貸上困難重重，個人的相關權益受損。
此研究透過產學合作，建立一個專給外籍移工使用的P2P借貸平台QLend，去解決上述提到的問題。本研究將重點放在平台裡頭所使用到的學習型信用預測模型 (LCAM) 及學習型風險預測模型 (LRAM)，透過移工基本資料、工作情形、遲還機率及設計的序列模組動態調整 (SMDA) 機制進而去推斷其信用分數及違約風險。由於P2P借貸評估信用風險且借款對象為外籍移工，本研究為先行者，透過文獻回顧與領域專家討論，決定出信用分數及違約風險自變數。透過自變數及SMDA機制最終產生符合學習目標的ASLFN。此研究欲驗證模型提出之SMDA機制之有效性，選擇與KNN及DT (Decision tree) 之現有分類模型進行比較，實驗結果證實SMDA機制是有效的，準確率皆可達八成八，同時也可證實在「信用評估」及「風險評估」上均比KNN及DT來得好。
Migrant workers are one of the indispensable workforces in Taiwan. The number of people has grown from less than 3,000 in ROC 80 to 660,000 in ROC 111, accounting for about 2.8% of Taiwan`s total labor force. It’s a group of labor that cannot be ignored in society. Migrant workers remit most of their income back to their home countries each month to supply for their families. When there is an urgent need today, factors such as language barriers and lack of trust information often make them difficult to borrow in Taiwan. Personal rights are violated.
Therefore, through industry-university cooperation, this study have established a P2P lending platform QLend for migrant workers to solve the above-mentioned problems. This research focuses on the learning-based credit assessment model (LCAM) and learning-based risk assessment model (LRAM) used in the platform. Based on the information of migrant workers, work situation, the ratio of late payment, and the proposed sequentially modular dynamic adjusting (SMDA) mechanism evaluate their credit score and default risk. Since P2P lending evaluates credit risk and the borrowers are migrant workers, this study is a pioneer. Through literature review and discussion with experts in the domain, the credit score and default risk independent variables are determined. This study intends to verify the effectiveness of the SMDA mechanism proposed by the model and chooses to compare with the existing classification models of KNN and DT (Decision tree). The experimental results confirm that the SMDA mechanism is effective, and the accuracy rate can reach 88%. At the same time, it can also be proved that it is better than KNN and DT in terms of credit assessment and risk assessment. |
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| Description: | 碩士
國立政治大學
資訊管理學系
109356047 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109356047 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202200880 |
| Appears in Collections: | [資訊管理學系] 學位論文
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