> Posted by Bailey Klinger, Asim Ijaz Khwaja, and DJ DiDonna, Co-Founders and COO, Entrepreneurial Finance Lab

The recent Financial Inclusion 2020 conference took a very private-sector focus to financial inclusion, with headliners such as Citigroup and MasterCard CEOs, demonstrating the shared importance of financial inclusion across all types of institutions. It also illustrated the enormity of the problem at hand, and subsequently the proximity of the year 2020 by which to address it. Participants agreed that an integral aspect of this gap is solving the puzzle of how financial institutions can evaluate risk for information-scarce micro, small, and medium-sized enterprises (MSMEs), and do so while keeping transaction costs low.

Industrial and organizational psychology has been working for decades on a problem with similar characteristics: how to screen people applying for jobs. Firms must decide which individuals to hire, often based on little available information. Moreover, firms must evaluate a large number of applicants in a low-cost way, particularly for entry-level positions. In response to this challenge, industrial psychology has developed a series of assessments of individuals that predict a person’s future success in a job in an objective, quantitative, scalable way. This field, which assesses skills, abilities, personalities, and intelligence, is known as psychometrics.

Our innovation at the Entrepreneurial Finance Lab (EFL) is to use psychometrics to assess an assortment of features of potential entrepreneurs and to bring this testing method to developing countries. Starting as a research project at Harvard in 2010, we have developed a 30-60 minute automated psychometric application that incorporates the potential borrower’s attitude and outlook, ability, business acumen, and character. It can measure risk without relying on business plans, credit history, collateral, or group liability.

We recently tested a psychometrically-enhanced loan application with Financiera Confianza, a lender serving MSMEs in Peru. We compared applicant responses to self-reported sales, subsequent loan repayment performance, and credit bureau data. We then created a scorecard based on that information using data from other countries and evaluated its effectiveness on a sample of business owners seeking loans. The psychometrically-enhanced application performed extremely well in predicting default—those MSMEs rejected by our scorecard had a probability of defaulting up to four times greater than those accepted. (Click here for our full results.)

Other tests have showed that our psychometrically-enhanced application is able to meet and exceed the predictive power of credit scoring models in developed countries. It has now been used in over 50,000 loan applications in 20 countries, expanding access to credit for promising entrepreneurs who may lack the collateral, personal networks, and formal business training otherwise needed to secure a loan. Non-traditional data such as psychometrics should certainly be counted among the new tools available to financial providers, in addition to those discussed at FI2020.

Bailey Klinger and Asim Ijaz Khwaja are co-founders of the Entrepreneurial Finance Lab, and DJ DiDonna is Chief Operating Officer. Khwaja, a Professor of International Finance and Development at the Harvard Kennedy School, will be offering a week-long executive course together with Rohini Pande (who blogged for us earlier this year) this February on “Rethinking Financial Inclusion: Smart Design for Policy and Practice”

This post was modified from its original version on November 25, 2013. The original version incorrectly indicated in the fourth paragraph that those rejected by the EFL scorecard had a probability of defaulting up to four times greater than those rejected.

Video credit: FOMIN Member of the IDB Group

Have you read?

Four Ways Big Data Will Impact Financial Inclusion

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