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Automated mortgage origination delay detection from textual conversations
Decision Support Systems ( IF 7.5 ) Pub Date : 2020-10-31 , DOI: 10.1016/j.dss.2020.113433
Arin Brahma , David M. Goldberg , Nohel Zaman , Mariano Aloiso

For modern mortgage firms, the process of setting up and verifying a new loan, known as origination, is complex and multifaceted. The literature notes that this process is rife with delays that can stunt the firm's business opportunities, but no modern analytical techniques have been developed to address the problem. In this paper, we suggest the use of text analytic and machine learning techniques to predict likely delays. In collaboration with a large national mortgage firm, we derive a large dataset of transcripts from employees' communications pertaining to potential loans. We first use information retrieval to generate an initial list of “seed terms,” or terms most associated with loans that were delayed. We then use an array of machine learning approaches to generate predictive models based upon these seed terms. We find that these approaches are comparable in performance to less interpretable state-of-the-art approaches utilizing word embeddings. The resultant models offer interpretable and high-performing solutions to mitigate the risk of delays through early risk detection.



中文翻译:

通过文本对话自动检测抵押贷款产生的延迟

对于现代抵押贷款公司而言,设置和验证新贷款的过程(称为发起)是复杂且多方面的。文献指出,此过程充斥着延误,可能会阻碍公司的商业机会,但是尚未开发出解决问题的现代分析技术。在本文中,我们建议使用文本分析和机器学习技术来预测可能的延迟。通过与一家大型的国家抵押公司合作,我们从与潜在贷款相关的员工交流中获得了大量的笔录数据集。我们首先使用信息检索来生成“种子条款”或与延迟贷款最相关的条款的初始列表。然后,我们使用一系列机器学习方法来基于这些种子项生成预测模型。我们发现这些方法在性能上可与使用词嵌入的难以解释的最新技术相媲美。生成的模型提供了可解释的高性能解决方案,可通过早期风险检测来减轻延迟风险。

更新日期:2020-12-01
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