Session

LAW

Description

This paper delves into the comparative advantages of machine learning over traditional statistical methods in real estate mortgage scoring. By examining the efficiency, robustness, and productivity gains of machine learning, the study underscores its potential to transform the financial industry, particularly in mortgage application processing. The findings highlight the reduced need for extensive data preprocessing with machine learning and its implications for faster and more accurate mortgage decision-making.

Keywords:

Machine Learning, Real Estate Mortgage, Financial Industry, Data Preprocessing, Traditional Statistical Methods.

Proceedings Editor

Edmond Hajrizi

ISBN

978-9951-550-95-6

Location

UBT Lipjan, Kosovo

Start Date

28-10-2023 8:00 AM

End Date

29-10-2023 6:00 PM

DOI

10.33107/ubt-ic.2023.126

Included in

Law Commons

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Oct 28th, 8:00 AM Oct 29th, 6:00 PM

Machine Learning in Mortgage Scoring: A Comparative Analysis with Traditional Statistical Methods

UBT Lipjan, Kosovo

This paper delves into the comparative advantages of machine learning over traditional statistical methods in real estate mortgage scoring. By examining the efficiency, robustness, and productivity gains of machine learning, the study underscores its potential to transform the financial industry, particularly in mortgage application processing. The findings highlight the reduced need for extensive data preprocessing with machine learning and its implications for faster and more accurate mortgage decision-making.