Revisiting microfinance

We are working with a group of experts to improve our performance cycles for the microfinance space. The aim is to understand credit risk. Microfinance lenders were criticised for profiteering from unfairly high rates of interest. This made it imperative to review practices, understand the universe of microfinance variables and see if an objective system could be arrived at to assist the sector, players.
Though microfinance has a moralistic role of creating social impact, it’s hard to draw the line between profit and a social cause. What happened in Andhra Pradesh with SKS has created a flutter among players. The ‘for profit’ players attempt to walk the tight ‘social cause’ rope. Also, we have an economic cycle that connects consumption patterns, basic demand and economic growth to microfinance. And, a downward economic cycle hurts all finance players, whether micro- or conventional finance.
This is why we took up the challenge to build an industry-wide model for the sector. Microfinance is a bit different compared to conventional finance. On one side are social impact variables like poverty alleviation indicators and, on the other, are financial variables and microfinance institutions that are a part of the listed exchange market. The approach that identifies outliers in a group of stocks could also do the same for social variables. The idea of seasonality is not only about a sector’s good or bad behaviour over a period of time, but also about its quarterly performance in an overall bad year. Our performance cycle approach could also look at social variables like poverty, infant mortality and trends in the health services.
Only by looking at the big picture, could we understand and explain how credit worthiness on Monday could be different from that tomorrow. The method would also assist in understanding and assigning a risk profile to microfinance variables, leading finally to comprehend the credit risk at a certain point of time. Our first sample coverage, thus, included microfinance institutions and a few social indices. We added them to a composite group of global assets and ranked them according to performance.These were: Network Microfinance (Pakistan), SKS (India), Grameen (Bangladesh), Alrafah (Palestine), Social Service Index (China), BRAC (Bangladesh), Jantzi Social Index (Canada), US Employment Index, Social Awareness Index, Compartamos (Mexico).
Compartamos was the topper and SKS and Grameen finished as laggards. According to our studies done on outliers ranked among a group of assets, there is a 65 per cent higher chance that an outlier would reverse in performance. This means toppers have high odds against further growth and vice versa.
The percentile ranking linked with assets or parameters is never static but dynamic. This change in ranking creates seasonalities, tendencies and Jiseki cycles that allow for interpretation and signal generation. Here, we have illustrated how the performance cycle grew from June 2010 to August 2011 by 37 per cent. This was accompanied by a growth in the Grameen price by 115 per cent. For SKS, the Jiseki cycle suggested a fall by 20 per cent from October 2010 to August 2011. The cycles anticipated a fall in the stock, which dropped in value by 81 per cent during the period. Currently, the cycles for the sector majors Grameen and BRAC point to a reversal, suggesting the worst for microfinance is behind us. Let’s see.