Machine learning application in online lending risk prediction [article]

Xiaojiao Yu
<span title="2017-07-16">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Online leading has disrupted the traditional consumer banking sector with more effective loan processing. Risk prediction and monitoring is critical for the success of the business model. Traditional credit score models fall short in applying big data technology in building risk model. In this manuscript, data with various format and size were collected from public website, third-parties and assembled with client's loan application information data. Ensemble machine learning models, random
more &raquo; ... t model and XGBoost model, were built and trained with the historical transaction data and subsequently tested with separate data. XGBoost model shows higher K-S value, suggesting better classification capability in this task. Top 10 important features from the two models suggest external data such as zhimaScore, multi-platform stacking loans information, and social network information are important factors in predicting loan default probability.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1707.04831v1">arXiv:1707.04831v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lrg54o7dfvcnffjniws5xxpj7a">fatcat:lrg54o7dfvcnffjniws5xxpj7a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191015222318/https://arxiv.org/pdf/1707.04831v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/31/bd/31bdc9f11640cbf7c374ced65f8290031effae8f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1707.04831v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>