Description
The year 2005 was declared by the United Nations General Assembly as the International Year of Microcredit to “raise awareness about the importance of micro-entrepreneurship and to further enhance existing programs that support sustainable, inclusive financial sectors around the world” (UN Capital Development Fund, 2005). Maes & Reed (2011) report that over 200 million clients were reached by microfinance institutions at the end of 2010. With the microfinance industry coming of age, opinions on the results are mixed. To some, microfinance is a magical method to eradicate poverty while others put forward that micro finance fell short of expectations.
The research group of professor Baesens has recently started to provide new insights into this debate by using empirical research and state-of-art modeling techniques. Currently, two research tracks are investigated:
Credit scoring for microfinance (see Credit Risk)
Due to growing competition, over-indebtedness, and economic crises, microfinance institutions have to pursue their social and financial objectives in an increasingly constrained environment. Using powerful risk management tools, therefore, becomes more than ever a key competence to survive. It is in this context that established techniques from traditional financial organizations are introduced to the microfinance industry, with the aim to improve both social impact and financial efficiency. One of these techniques is credit scoring, which analyzes historical client data and derives a model to link repayment behavior with characteristics of the loan, lender, and borrower. The developed models are assessed in terms of stability, readability, and discriminatory power.
Financial efficiency and social impact of microfinance institutions using advanced statistical techniques
In the 1980s and 1990s, microfinance institutions (MFIs) were confronted with an increased focus on financial self-sustainability, as donors argued that many subsidized credit programs were susceptible to a high degree of moral hazard. Besides this evolution, MFIs were also confronted with greater competition and increased interest from the private sector. Answering whether this new premises have resulted in better financial efficiency and/or less social impact is not straightforward. This is mainly caused by the wide differences between microfinance institutions. After all, ‘micro finance institution’ is just an umbrella term that includes many different types. Firstly, MFIs operate in almost all parts of the world, making them exposed to different social and legal systems. Secondly, some MFIs operate as non-governmental organizations or cooperatives whereas others operate as banks. Thirdly, some MFIs are relatively young in contrast to others which have been in business for decades. Other MFIs focus their business on supplying loans only while others offer a wide range of financial products. In sum, the diversity among MFIs makes it harder to report conclusive findings.
Our research tries to circumvent these difficulties by using advances statistical techniques. In one paper, we use a technique called self-organizing maps (SOM) to create graphical two-dimensional maps where similar MFIs are mapped close together and dissimilar more apart (see Marketing Analytics). This methodology is a superior method to graphically plot the heterogeneity among MFIs with regard to the different input variables. To our knowledge, SOM have not been used in the microfinance literature. In another paper, a generalized estimating equations panel analysis model investigates two types of possible improvements of pursuing profitability, efficiency gains and social impact advances.
Notable Publications
- Van Gool, J., Verbeke, W., Sercu, P., Baesens, B. (2012). Credit scoring for microfinance – Is It worth It?. International Journal of Finance & Economics, 17 (2), 102-123.
