A comparison of data mining methods in microfinance

Microflnance 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. Sub-prime 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.