Revolutionizing Real Estate Mortgage Scoring: The Superiority of Machine Learning Over Traditional Statistical Methods
Session
LAW
Description
This paper explores the advantages of machine learning over traditional statistical methods in the context of real estate mortgage scoring. While traditional methods require extensive data preprocessing, machine learning offers a more streamlined and efficient approach. The financial industry is recognizing these benefits, with machine learning enabling faster mortgage application processing and reduced modeling biases. The findings underscore the potential of machine learning to revolutionize the financial sector.
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.2023.128
Recommended Citation
Hoxha, Visar; Demjaha, Blerta; Lecaj, Veli; Dana, Hazer; and Pallaska, Fuat, "Revolutionizing Real Estate Mortgage Scoring: The Superiority of Machine Learning Over Traditional Statistical Methods" (2023). UBT International Conference. 6.
https://knowledgecenter.ubt-uni.net/conference/IC/LAW/6
Revolutionizing Real Estate Mortgage Scoring: The Superiority of Machine Learning Over Traditional Statistical Methods
UBT Lipjan, Kosovo
This paper explores the advantages of machine learning over traditional statistical methods in the context of real estate mortgage scoring. While traditional methods require extensive data preprocessing, machine learning offers a more streamlined and efficient approach. The financial industry is recognizing these benefits, with machine learning enabling faster mortgage application processing and reduced modeling biases. The findings underscore the potential of machine learning to revolutionize the financial sector.