A COMPERATIVE STUDY OF HOME LOAN PROVIDED BY CORPERATIVE BANK

Abstract

In the evolving landscape of financial services, home loans play a crucial role in empowering individuals to fulfill their aspirations of homeownership. Among the various financial institutions that offer housing finance, cooperative banks stand out due to their community-centric approach, regional accessibility, and borrower-friendly interest rates. However, despite their outreach and customer base, cooperative banks often face stiff competition from commercial banks and NBFCs in terms of digital transformation, loan processing speed, and customer experience. This study presents a comparative analysis of home loan products offered by different cooperative banks with a focus on interest rates, tenure flexibility, processing fees, documentation requirements, customer service, and digital service availability. Furthermore, this research integrates the software domain, especially machine learning (ML), to analyze large-scale customer datasets, understand loan approval patterns, and evaluate borrower creditworthiness. By using ML algorithms such as Logistic Regression, Decision Trees, and K-Means Clustering, we aim to develop models that can predict home loan approval likelihood and classify customer segments based on repayment behavior and risk profiles. This combination of financial analysis with ML-based data processing helps identify the strengths and inefficiencies within cooperative banking systems, ultimately suggesting pathways for digital advancement and smarter lending practices.