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Related Experiment Video

Updated: Aug 28, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Default risk prediction and feature extraction using a penalized deep neural network.

Cunjie Lin^1,2, Nan Qiao², Wenli Zhang²

¹Center for Applied Statistics, Renmin University of China, Beijing, 100872 China.

Statistics and Computing

|September 20, 2022

Summary

This study introduces a penalized deep learning model for predicting peer-to-peer lending default risk over time. The model accurately assesses lender risk, enhancing decision-making on online lending platforms.

Keywords:

Feature extraction Loan data Neural network Risk prediction Survival analysis

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Area of Science:

Machine Learning
Financial Technology
Data Science

Background:

Online peer-to-peer (P2P) lending bypasses traditional financial institutions, connecting lenders directly with borrowers.
Accurate assessment of borrower default risk is critical for lenders to prevent financial loss on P2P platforms.
Existing models often predict the occurrence of default rather than the probability over time.

Purpose of the Study:

To develop an advanced penalized deep learning model for predicting default risk in P2P lending.
To focus on predicting the probability of default as a function of time, offering a more nuanced risk assessment.
To integrate simultaneous feature selection and estimation within the deep learning framework.

Main Methods:

A penalized deep learning model was developed using survival data to predict default risk.
An additional one-to-one layer was incorporated into the neural network architecture.
An L1-penalty was integrated into the objective function for feature selection and estimation.
The minibatch gradient descent algorithm was employed to manage and process large datasets efficiently.

Main Results:

The penalized deep learning model demonstrated competitive practical performance on real-world loan data.
Simulations confirmed the model's effectiveness in predicting default risk.
The model successfully predicted the probability of default over time, outperforming traditional methods.

Conclusions:

The developed penalized deep learning model offers a robust solution for assessing default risk in P2P lending.
The model's ability to predict default probability over time provides valuable insights for lenders.
This approach shows significant potential for enhancing risk management and decision-making on P2P lending platforms.