AN ENHANCED RURAL COMMUNITY LOAN ELIGIBILITY MATCHMAKING MODEL USING A HYBRIDIZED RECOMMENDER SYSTEM

Abstract

The involvement of modern technology is necessary for lenders to find a borrower with whom they can build confidence and reliable relationships leading to a good financial  community development system in Nigeria. Grameen model is used as a standard to offer unsecured small loans to the poor in Africa and beyond but the model has some limitation like control of groupings by individual is centered on human behaviour factor, Punishment meant for others are suffered by different people. Hence to solve these problems stated we develop a an enhanced rural community loan eligibility matchmaking model using a hybridized recommender system. In this project, we developed a Lenders-to-Borrowers Recommender system using a hybridized algorithm of Content-based filtering and Success Credibility Score model to ensure credible recommendations of Borrowers to Lenders. Object-Oriented Analysis and Design Methodology was used in the analysis and was implemented with PHP programming language with the Apache web server to manage the Database developed using MySql.  Our hybridized system was able to produce accurate and credible recommendations of Eligible Borrower and the result shows that Lenders can effectively be guided on the choice of borrowers for loans.