# Wide and Deep Learning for Peer-to-Peer Lending

arXiv: https://arxiv.org/abs/1810.03466 · HTML: https://mlsanity.com/paper/1810.03466/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [Bagging Supervised Autoencoder Classifier for Credit Scoring](https://mlsanity.com/paper/2108.07800/) (2021)
- [A comparative study on machine learning models combining with outlier detection and balanced sampling methods for credit scoring](https://mlsanity.com/paper/2112.13196/) (2021)
- [DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data](https://mlsanity.com/paper/2308.03704/) (2023)

## Abstract (arXiv)

This paper proposes a two-stage scoring approach to help lenders decide their fund allocations in the peer-to-peer (P2P) lending market. The existing scoring approaches focus on only either probability of default (PD) prediction, known as credit scoring, or profitability prediction, known as profit scoring, to identify the best loans for investment. Credit scoring fails to deliver the main need of lenders on how much profit they may obtain through their investment. On the other hand, profit scoring can satisfy that need by predicting the investment profitability. However, profit scoring completely ignores the class imbalance problem where most of the past loans are non-default. Consequently, ignorance of the class imbalance problem significantly affects the accuracy of profitability prediction. Our proposed two-stage scoring approach is an integration of credit scoring and profit scoring to address the above challenges. More specifically, stage 1 is designed as credit scoring to identify non-default loans while the imbalanced nature of loan status is considered in PD prediction. The loans identified as non-default are then moved to stage 2 for prediction of profitability, measured by internal rate of return. Wide and deep learning is used to build the predictive models in both stages to achieve both memorization and generalization. Extensive numerical studies are conducted based on real-world data to verify the effectiveness of the proposed approach. The numerical studies indicate our two-stage scoring approach outperforms the existing credit scoring and profit scoring approaches.