# A comparative study on machine learning models combining with outlier detection and balanced sampling methods for credit scoring

arXiv: https://arxiv.org/abs/2112.13196 · HTML: https://mlsanity.com/paper/2112.13196/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 50 references that are in the map.
- First posted on arXiv 25 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning](https://mlsanity.com/paper/2008.09202/) (2020)
- [Wide and Deep Learning for Peer-to-Peer Lending](https://mlsanity.com/paper/1810.03466/) (2018)
- [Credit risk prediction in an imbalanced social lending environment](https://mlsanity.com/paper/1805.00801/) (2018)

## Built on by (most cited first, 1 of 1)

- [Improving Predictions on Highly Unbalanced Data Using Open Source Synthetic Data Upsampling](https://mlsanity.com/paper/2507.16419/) (2025)

## Abstract (arXiv)

Peer-to-peer (P2P) lending platforms have grown rapidly over the past decade as the network infrastructure has improved and the demand for personal lending has grown. Such platforms allow users to create peer-to-peer lending relationships without the help of traditional financial institutions. Assessing the borrowers' credit is crucial to reduce the default rate and benign development of P2P platforms. Building a personal credit scoring machine learning model can effectively predict whether users will repay loans on the P2P platform. And the handling of data outliers and sample imbalance problems can affect the final effect of machine learning models. There have been some studies on balanced sampling methods, but the effect of outlier detection methods and their combination with balanced sampling methods on the effectiveness of machine learning models has not been fully studied. In this paper, the influence of using different outlier detection methods and balanced sampling methods on commonly used machine learning models is investigated. Experiments on 44,487 Lending Club samples show that proper outlier detection can improve the effectiveness of the machine learning model, and the balanced sampling method only has a good effect on a few machine learning models, such as MLP.