A Comparison of Data Mining Methods in Microfinance
Microfinance provides financial services to low income or poor credit record clients.The credit crunch has led to mainstream lenders tightening their lending policies,resulting in increased financial exclusion.Loan sharks then become an alternative and easy way of borrowing money.However,extremely high interest rates from loan sharks put low income people into worse poverty.Subprime lenders play an important role in providing affordable loans to fill the gap between loan sharks and mainstream lenders.All the mainstream lenders have their own loan risk assessment systems,but these systems are either in house or not applicable for giving loans to this marginal group of client.Due to the varying characteristics of this marginal group of clients,sub-prime lenders need to develop their own loan risk assessment system.Although data mining methods have the potential for developing such a risk assessment system,the relative performance of the different data mining methods on such data is not known.Hence,this paper focuses on comparing different data mining methods when applied to loan data for sub-prime lenders.
Microfinance Data mining Exemplar based model Bayesian network Decision tree Clustering
Jia Wu Sunil Vadera Karl Dayson Diane Burridge lan Clough
University of Salford 43 Crescent,Salford,United Kingdom East Lans Money line (IPS) Ltd Lancashire United Kingdom
国际会议
重庆
英文
499-502
2010-09-17（万方平台首次上网日期，不代表论文的发表时间）