Transfer Learning for Mobile Phone Credit Scoring

Mobile money platforms are gaining traction across developing markets as a convenient way of sending and receiving money over mobile phones. Recent joint collaborations between banks and mobile-network operators leverage a customer’s past mobile phone transactions in order to create a credit score for the individual. In this work, we address the problem of launching a mobile-phone based credit scoring system in a new market without the marginal distribution of features of borrowers in the new market. This challenge rules out traditional transfer learning approaches such as a direct covariate shift.  This work was recently presented at ACM COMPASS 2018.

Skyler Speakman is a Research Scientist at IBM Research -- Africa.  He is the technical lead for A.I. at the Kenya lab.  His projects use data science  and machine learning to impact the lives of millions of people on the continent. Skyler completed a Ph.D. in Information Systems at Carnegie Mellon University as well as a M.S. in Machine Learning.  He also holds masters in Mathematics, Statistics, and Public Policy.  He lives in Nairobi, Kenya with his wife and two young sons.