This article examines the effectiveness of machine learning algorithms in predicting loan default probability using data from Renrendai, an online credit platform. Three models—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF)—are compared against logistic regression.
1. Objective
- To assess the effectiveness of machine learning algorithms in predicting loan default probability in the online credit market.
2. Data & Methodology
- Data Source: Renrendai, an online lending platform.
- Models Used:
- Machine Learning: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF).
- Benchmark: Logistic Regression for comparison.
3. Key Findings
- Performance: Machine learning models outperform logistic regression.
- Evaluation Metrics: AUC, accuracy rate, and Brier score show superior results for ML models.
- IDI Test Results: Confirms higher predictive accuracy of ML models.
4. Investor Impact
- ML models reduce misclassification costs.
- Improve profitability and risk assessment for investors.
5. Conclusion
Machine learning enhances credit risk prediction and financial decision-making in online lending markets.Math Scientist Awards
Visit our page : https://mathscientists.com/
Nominations page : https://mathscientists.com/award-nomination/?ecategory=Awards&rcategory=Awardee
Get Connects Here:
========================
Youtube: https://youtube.com/@maths-groot?si=QgHvOhb5caP1yDMy
Instagram : https://www.instagram.com/
Blogger : https://mathsgroot03.blogspot.com/
Twitter :https://x.com/mathsgroot03
Tumblr: https://www.tumblr.com/mathscientists
What'sApp: https://whatsapp.com/channel/0029Vaz6Eic6rsQz7uKHSf02
No comments:
Post a Comment