Explainable Machine Learning for Fallout Prediction in the Mortgage Pipeline

Preetam Purohit () and Amit Verma
Additional contact information
Preetam Purohit: Embrace Home Loans, Inc., Middletown, RI 02852, USA
Amit Verma: Craig School of Business, Missouri Western State University, St. Joseph, MO 64507, USA

JRFM, 2024, vol. 17, issue 10, 1-20

Abstract: This study examines mortgage loan fallout using data provided by a leading financial institution. By accurately predicting mortgage loan fallout, lenders can protect their bottom line, maintain financial stability, and contribute to a healthier economy. The paper employs various machine learning models to predict mortgage fallout based on loan, market, property, and borrower characteristics. A large dataset of locked mortgage applications from a major U.S. lender was analyzed. The random forest model demonstrated superior predictive efficiency and stability. To understand the factors influencing mortgage fallout, the SHAP method, along with empirical analysis with logistic regression, was utilized to identify key determinants. The paper discusses the implications of these findings for mortgage lenders and future research.

Keywords: mortgage; fallout; machine learning; pull through; lenders (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1911-8074/17/10/431/pdf (application/pdf)
https://www.mdpi.com/1911-8074/17/10/431/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:17:y:2024:i:10:p:431-:d:1487363

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().