Data-Driven Underwriting: A study of Lending with Precision

Author(s)	Balaji Ethirajulu
Country	United States
Abstract	Traditional underwriting processes depend on insufficient data and personal judgment which results in operational inefficiencies and heightened risk. The utilization of advanced analytics together with diverse data sources enables data-driven underwriting to achieve both precision and efficiency in lending operations. Our study analyzes underwriting evolution while evaluating data-driven methods advantages and obstacles alongside technologies that drive this shift and upcoming trends defining precise lending approaches. This analysis evaluates multiple data sources and analytical techniques along with risk assessment models to demonstrate their influence on loan approval rates as well as risk mitigation while improvingcustomer experience. This study examines the ethicalimplications and regulatory frameworks that governdata usage inlending practices.
Keywords	Data-Driven Underwriting, Lending, Risk Assessment, Machine Learning, Alternative Data, Credit Scoring, Fintech, Loan Approval, Risk Management
Field	Engineering
Published In	Volume 2, Issue 5, May 2021
Published On	2021-05-08
DOI	https://doi.org/10.5281/zenodo.15112169
Short DOI	https://doi.org/g9bcjm

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