Grandata Lab

We partner with leading academic institutions around the world to advance research in Artificial Intelligence, Machine Learning, and Data Privacy to tackle a range of global issues, from financial inclusion to disease contagion.
55+
Published Scientific Papers
14
Scientific Papers published in top journals, including Nature
4
Patents
9
Scientific collaborations with research teams around the world

Working with Leaders in Machine Learning

Aline Viana, PhD
Research scientist INRIA Paris
Dr. Cristián Bravo
Associate Professor of Business Analytics Southampton Business School at the University of Southampton
Marta Gonzalez, PhD
Associate Professor of City & Regional Planning UC Berkeley, Lawrence Berkeley National Laboratory
Márton Karsai, PhD
Assistant Prof. Computer Science Ecole Normale Supérieure de Lyon Laboratoire de l’Informatique du Parallélisme
The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics
Published January 2019
Credit scoring is without a doubt one of the oldest applications of analytics. In recent years, a multitude of sophisticated classification techniques have been developed to improve the statistical performance of credit scoring models. Instead of focusing on the techniques themselves, this paper leverages alternative data sources to enhance both statistical and economic model performance. The study demonstrates how including call networks, in the context of positive credit information, as a new Big Data source has added value in terms of profit by applying a profit measure and profit-based feature selection. A unique combination of datasets, including call-detail records, credit and debit account information of customers is used to create scorecards for credit card applicants. Call-detail records are used to build call networks and advanced social network analytics techniques are applied to propagate influence from prior defaulters throughout the network to produce influence scores. The results show that combining call-detail records with traditional data in credit scoring models significantly increases their performance when measured in AUC. In terms of profit, the best model is the one built with only calling behavior features. In addition, the calling behavior features are the most predictive in other models, both in terms of statistical and economic performance. The results have an impact in terms of ethical use of call-detail records, regulatory implications, financial inclusion, as well as data sharing and privacy.
 

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