Spatiotemporal Integration of Mobile, Satellite, and Public Geospatial Data for Enhanced Credit Scoring

Abstract

Credit scoring of financially excluded persons is challenging for financial institutions because of a lack of financial data and long physical distances, which hamper data collection. The remote collection of alternative data has the potential to overcome these challenges, enabling credit access for such individuals. Whereas alternative data sources such as mobile phones have been investigated by previous researchers, this research proposes the integration of mobile-phone, satellite, and public geospatial data to improve credit evaluations where financial data are lacking. An approach to integrating these disparate data sources involving both spatial and temporal analysis methods such as spatial aggregation was employed, resulting in various data combinations. The resulting data sets were used to train classifiers of varying complexity, from logistic regression to ensemble learning. Comparisons were based on various performance metrics, including accuracy and the area under the receiver operating-characteristic curve. The combination of all three data sources performed significantly better than mobile-phone data, with the mean classifier accuracy and F1 score improving by 18% and 0.149, respectively. It is shown how these improvements can translate to cost savings for financial institutions through a reduction in misclassification errors. Alternative data combined in this manner could enhance credit provision to financially excluded persons while managing associated risks, leading to greater financial inclusion.

Keywords

• credit scoring
• satellite data
• machine learning
• ensemble learning
• financial inclusion