A deep learning approach to risk management modeling for Islamic microfinance

Abstract

Islamic Microfinance rides two recent growing trends: conventional microfinance and Islamic banking. It offers financial flexibility to the poorest strata of the population in different Muslim countries by borrowing and mixing techniques from these two sources. In particular, risk management and loan qualifications tend to be similar to those operating inside conventional and Islamic financial institutions. The loan approval process heavily relies on scoring applicants mostly on their financial criteria. This paper aims to demonstrate that an alternative framework based on artificial intelligence improves traditional financial techniques. This framework also resonates more with the fundamental and specific values of Islamic Microfinance as it captures some non-financial attributes of the applicant that are informationally rich. We first present the critical components of this novel approach. Then, we apply it to a business case (approximately 30,000 applications to a microfinancing institution in the Central African Republic) to demonstrate its usefulness.