Machine Learning in Mortgage Scoring: A Comparative Analysis with Traditional Statistical Methods
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
Recommended Citation
Hoxha, Visar; Demjaha, Blerta; Lecaj, Veli; Dana, Hazer; and Pallaska, Fuat, "Machine Learning in Mortgage Scoring: A Comparative Analysis with Traditional Statistical Methods" (2023). UBT International Conference. 4.
https://knowledgecenter.ubt-uni.net/conference/IC/LAW/4
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.