基於外部風險事件預測中小企業信用風險之研究

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姓名

羅智超(Zhi-Chao Luo) 查詢紙本館藏

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企業管理學系

論文名稱

基於外部風險事件預測中小企業信用風險之研究
(SMEs Credit Risk Prediction Using External Risk Data)

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摘要(中)

互聯網銀行業務發展迅猛，並且主要利潤來源於中小企業（medium-sized enterprises , SMEs）。然而中小企業違約風險較高，因此需要構建風險識別模型來識別企業信貸違約。該模型應具備：提前預測能力使銀行對不良貸款行為有快速回應能力；使用公開信用數據而不是傳統的財務數據；保證在樣本不平衡率較高水準下仍能保持較高的精確率（Recall）。本研究通過使用公開可獲得的外部風險事件時序數據和橫截面數據構建了一個兩階段模型來預測中小企業的違約風險。第一階段設計了RS-Ripper演算法，該演算法改進了Prefix-SPAN演算法提取風險事件的頻繁項，並構建了基於規則的分類器。第二階段通過使用橫截面數據構建LightGBM提升模型精確度（Recall）。該模型在違約預測方面平均提前預測天數達350天，在違約樣本和非違約樣本比例為1:1情況下查全率（Recall），查準率（Precision）,準確率（Accuracy）和 AUC分別為0.92, 0.911, 0.915, 0.956, 在違約樣本和非違約樣本比例為1:16情況下查全率（Recall），查準率（Precision）,準確率（Accuracy）和 AUC分別為0.751, 0.618, 0.958, 0.962。

摘要(英)

Online banks receive much publicity, and they profit by loaning to small and medium-sized enterprises (SMEs). However, a risk detection model is required to reduce the risk involved in nonperforming loans. This task involves three requirements: predicting the future to enable banks to react to bad loans, considering publicly available credit data instead of financial reports or managers’ personal records, and ensuring that the model has a large area under the receiver operating curve (AUC) and high recall and precision when the data are highly skewed. This study proposes a two-stage model to predict the risk of SME default by using sequences of risk events available on public websites. In the first stage, 1) revised prefix-projected sequential pattern mining and repeated incremental pruning to reduce error are combined and 2) sequences of events are used as input to generate a rule-based classifier with consistent performance as imbalanced increases. The method is combined with LightGBM to increase recall. On average, the proposed method can provide banks with 350 days of early warning. In an ideal scenario, where the number of defects and normal profiles are the same, the recall, precision, accuracy, and AUC of the method can reach 0.92, 0.911, 0.915, 0.956, respectively. In a near-worst-case scenario, with a 1:16 imbalance ratio, the recall, precision, accuracy, and AUC can reach 0.751, 0.618, 0.958, and 0.962, respectively.

關鍵字(中)

★ 中小企業
★ 企業違約
★ 外部風險數據
★ 信用風險
★ 時間序列挖掘

關鍵字(英)

★ SMEs
★ Default Prediction
★ External Risk Data
★ Credit Risk
★ Time Seires Data Mining

論文目次

摘要 I
ABSTRACT II
致謝辭 III
目錄 IV
圖目錄 VII
表目錄 VIII
一、緒論 1
1-1 研究背景 1
1-2 研究現狀 3
1-2-1 從數據維度看 3
1-2-2 從數據類型看 4
1-2-3 從研究方法論看 4
1-2-4 違約數據樣本不平衡 5
1-2-5 當前研究的空白點 5
1-3 研究目標 6
1-4 研究貢獻 7
1-5 研究思路和方法 7
二、文獻回顧 9
2-1 信用風險方法研究 9
2-1-1 信用風險理論研究 9
2-1-2專家分析法 11
2-1-3 專業機構信用模型 12
2-1-4 信用評分卡 13
2-1-5統計機器學習方法 14
2-2 企業違約過程研究 21
2-3 時間序列模式挖掘研究 22
2-3-1 Apriori 23
2-3-2 GSP 23
2-3-4 SPADE 24
2-3-5 PrefixSpan 24
2-4 違約變數選擇研究 25
2-5 違約樣本不平衡研究 25
2-6 文獻回顧小結 26
三、研究方法 28
3-1 信用風險定義 28
3-2 中小企業的界定 30
3-3 研究框架 31
3-3-1 第一階段模型構造 33
3-3-2 第二階段模型構造 39
3-4 數據收集 42
四、數據和變數 44
4-1 樣本資訊 44
4-2時序數據 44
4-3橫截面數據 46
五、實證結果與分析 48
5-1 超參數設置 48
5-2 基於1:1數據集的分類結果 49
5-2-1 序列和規則結果 49
5-2-2 ARSR規則預測時間提前量 52
5-2-3 橫截面實證結果 53
5-3 基於不同樣本平衡率下的預測結果 58
5-3-1 1:2條件下的混淆矩陣與ROC圖 60
5-3-2 1:4條件下的混淆矩陣與ROC圖 60
5-3-3 1:8條件下的混淆矩陣與ROC圖 61
5-3-4 1:16條件下的混淆矩陣與ROC圖 62
5-4 模型對比實證研究結果 62
5-4-1 模型演算法改進比較結果 62
5-4-2 不同模型比較結果 63
5-4-3 不同行業模型比較結果 64
5-5討論 64
六、結論及未來研究方向 67
6-1 結論 67
6-2 不足及未來研究方向 69
參考文獻 70
附件一：ARSR規則列表 84

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指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2022-1-22

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