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A Novel Default Risk Prediction and Feature Importance Analysis Technique for Marketplace Lending using Machine Learning

  1. Journals
  2. Economics, Finance, Business & Management
  3. Credit and Capital Markets – Kredit und Kapital
  4. Vol. 56, Issue 1
  5. pp. 27–62

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Imam, S., Huhn, S., Hornuf, L., Drechsler, R. A Novel Default Risk Prediction and Feature Importance Analysis Technique for Marketplace Lending using Machine Learning. Credit and Capital Markets – Kredit und Kapital, 56(1), 27-62. https://doi.org/10.3790/ccm.56.1.27
Imam, Sana Hassan; Huhn, Sebastian; Hornuf, Lars and Drechsler, Rolf "A Novel Default Risk Prediction and Feature Importance Analysis Technique for Marketplace Lending using Machine Learning" Credit and Capital Markets – Kredit und Kapital 56.1, 2023, 27-62. https://doi.org/10.3790/ccm.56.1.27
Imam, Sana Hassan/Huhn, Sebastian/Hornuf, Lars/Drechsler, Rolf (2023): A Novel Default Risk Prediction and Feature Importance Analysis Technique for Marketplace Lending using Machine Learning, in: Credit and Capital Markets – Kredit und Kapital, vol. 56, iss. 1, 27-62, [online] https://doi.org/10.3790/ccm.56.1.27
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A Novel Default Risk Prediction and Feature Importance Analysis Technique for Marketplace Lending using Machine Learning

Imam, Sana Hassan | Huhn, Sebastian | Hornuf, Lars | Drechsler, Rolf

Credit and Capital Markets – Kredit und Kapital, Vol. 56 (2023), Iss. 1 : pp. 27–62

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Article Details

Publisher Name
Duncker & Humblot
DOI
https://doi.org/10.3790/ccm.56.1.27
Language
English
Pages
36
Keywords

Author Details

Sana Hassan Imam, Department of Informatics, University of Bremen.

Sebastian Huhn, University of Bremen/DFKI GmbH, 28359 Bremen, Germany.

Lars Hornuf, Faculty of Business and Economics, Technische Universität Dresden, 01069 Dresden, Germany.

Rolf Drechsler, University of Bremen/DFKI GmbH, 28359 Bremen, Germany.

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Abstract

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.

Table of Contents

Section Title Page Action Price
Sana Hassan Imam et al.: A Novel Default Risk Prediction and Feature Importance Analysis Technique for Marketplace Lending using Machine Learning 27
Abstract 27
I. Introduction 28
II. Institutional Background 31
III. Literature Review 32
1. Machine Learning 32
a) Recurrent Neural Network 33
b) Long Short-Term Memory Models 33
c) Deep Bidirectional LSTMs 35
d) SHAP for Feature Importance 35
e) Principal Component Analysis for Feature Extraction 36
2. Marketplace Lending 36
IV. Model 38
1. Data Collection and Pre-processing 40
2. Features Elimination 40
3. Time Series Analysis 42
4. Model Generation 43
5. Model Training and Summary 43
6. Challenges during Training 45
7. Hyper-parameters Selection 45
8. Evaluation Matrices for Model Performance 45
9. Data Division and Cross Validation 47
V. Results 48
1. Feature Importance by SHAP 50
2. The Impact of the Early Covid-19 Period 52
VI. Conclusion 55
References 55

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