Traditional financial decision systems (e.g. credit) had to rely on explicit individual traits like age, gender, job type, and marital status, while being oblivious to spatio-temporal mobility or the habits of the individual involved. Emerging trends in geo-aware and mobile payment systems, and the resulting “big data,” present an opportunity to study human consumption patterns across space and time. Taking inspiration from animal behavior studies that have reported significant interconnections between animal spatio-temporal “foraging” behavior and their life outcomes, we analyzed a corpus of hundreds of thousands of human economic transactions and found that financial outcomes for individuals are intricately linked with their spatio-temporal traits like exploration, engagement, and elasticity. Such features yield models that are 30% to 49% better at predicting future financial difficulties than the comparable demographic models.
Media Lab, Massachusetts Institute of Technology, 20 Amherst St, Cambridge, Massachusetts, United States of America;School of Communication and Information, Rutgers University, New Brunswick, New Jersey, United States of America;School of Management, Sabanci University, Tuzla, Istanbul, Turkey;Media Lab, Massachusetts Institute of Technology, 20 Amherst St, Cambridge, Massachusetts, United States of America