Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/298830 
Erscheinungsjahr: 
2023
Quellenangabe: 
[Journal:] Credit and Capital Markets – Kredit und Kapital [ISSN:] 2199-1235 [Volume:] 56 [Issue:] 1 [Year:] 2023 [Pages:] 27-62
Verlag: 
Duncker & Humblot, Berlin
Zusammenfassung: 
Marketplace lending has fundamentally changed the relationship between borrowers and lenders in financial markets. As with many other financial products that have emerged in recent years, internet-based investors may be inexperienced in marketplace lending, highlighting the importance of forecasting default rates and evaluating default features such as the loan amount, interest rates, and FICO score. Potential borrowers on marketplace lending platforms may already have been rejected by banks as too risky to lend to, which amplifies the problem of asymmetric information. This paper proposes a holistic data processing flow for the loan status classification of marketplace lending multivariate time series data by using the Bidirectional Long Short-Term Memory model (BiLSTM) to predict "non-default," "distressed," and "default" loan status, which outperforms conventional techniques. We adopt the SHapely Additive exPlanations (SHAP) and a four-step ahead model, allowing us to extract the most significant features for default risk assessment. Using our approach, lenders and regulators can identify the most relevant features to enhance the default risk assessment method over time in addition to early risk prediction.
Schlagwörter: 
Default loan prediction
marketplace lending
time series classification
LSTM/BiLSTM
feature importance
SHAP
JEL: 
D81
G32
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.