Benefits and Pitfalls of Statistical Credit Scoring for Microfinance
This paper discusses the benefits and pitfalls of credit scoring applied to microfinance. Scoring is the use of the knowledge of the performance and characteristics of past loans to predict the performance of future loans. For example, when a loan officer judges risk by mentally comparing a current applicant with her experience with other applicants, it is scoring, albeit implicit and subjective. Likewise, when a microlender adopts a policy not to renew loans to clients who had spells of arrears in excess of 30 days in their previous loan, it is scoring, albeit simple and one-dimensional. Thus, although the name scoring may be new to microfinance, scoring itself is old hat. Statistical scoring is the use of quantitative knowledge of the performance and characteristics of past loans recorded in an electronic data base to predict the performance of future loans.
The evaluation of the repayment risk of the self-employed poor is the central challenge of microfinance. The innovations of microfinance to date have been the use of joint-liability groups and detailed evaluations of individual applicants to judge risk; scoring promises to be the next jump in efficiency. Although scoring will not replace joint-liability groups nor loan officers, it does have enough predictive power to significantly improve the evaluation of the risk of loans applicants.
This paper discusses what scoring can and cannot do, describes the data that microlenders who plan to use scoring should start to collect from all loan applicants, and outlines the basic steps in a scoring project.