Big Data for innovation: The case of credit evaluation using mobile data analyzed by innovation ecosystem lens

Abstract

Despite the high expectations about Big Data (BD) innovation potential, academy lacks studies exploring its implications and the process to achieve the announced benefits. This paper aims to help filling this gap, analyzing the case of an innovative credit assessment model, based on behavioral profiles generated over mobile network data. We ponder about the innovative potential of such huge data sets when applied to purposes differing from the ones they were generated for. To this case theoretical lens, we propose a framework where Innovation Ecosystem concepts are articulated by Contextualist elements. We explore this approach as an alternative to study the phenomenon different dimensions. It was useful to highlight that, despite the potential benefits of the solution as an enabling technology for financial inclusion and new business models in credit area, the ecosystem required for such innovation has critical dependencies delaying its progress. The study also revealed strategies used to break the inertia and create a minimum viable footprint (MVF), as a first step to chase its innovation full goals. It shows that the planning of an incremental path can be a good gimmick to deal with dependencies and enable radical change in complex ecosystems. © 2016 Portland International Conference on Management of Engineering and Technology, Inc.