What does your Facebook profile reveal about your creditworthiness? Using alternative data for microfinance

Abstract Microfinance has known a large increase in popularity, yet the scoring of such credit still remains a difficult challenge. Credit scoring traditionally uses socio-demographic and credit data, which we complement in an innovative manner with data from Facebook. A distinction is made between the relationships that the available data imply: (1) LALs are persons who resemble one another in some manner, (2) friends have a clearly articulated friendship relationship on Facebook, and (3) BFFs are friends that interact with one another. Our analyses show two interesting conclusions for this emerging application: the BFFs have a higher predictive value then the person’s friends and secondly, the interest-based data that define LALs, yield better results than the social network data. Moreover, the model built on interest data is not significantly worse than the model that uses all available data, hence demonstrating the potential of Facebook data in a microfinance setting.

[1]  Bart Baesens,et al.  Comprehensible Credit Scoring Models Using Rule Extraction from Support Vector Machines , 2007, Eur. J. Oper. Res..

[2]  Mark Schreiner,et al.  Scoring : the next breakthrough in microcredit ? , 2003 .

[3]  J. Morduch.  The microfinance promise , 1999 .

[4]  Jonathan Crook,et al.  Sample selection bias in credit scoring models , 2003, J. Oper. Res. Soc..

[5]  L. Getoor,et al.  Link-Based Classification , 2003, Encyclopedia of Machine Learning and Data Mining.

[6]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[7]  Chris Volinsky,et al.  Network-Based Marketing: Identifying Likely Adopters Via Consumer Networks , 2006, math/0606278.

[8]  David Martens,et al.  Pseudo-Social Network Targeting from Consumer Transaction Data , 2011 .

[9]  Ahmet Burak Emel,et al.  A credit scoring approach for the commercial banking sector , 2003 .

[10]  Manfred Zeller,et al.  Repayment performance in group-based credit programs in Bangladesh , 1997 .

[11]  Steffen Staab,et al.  Social Networks Applied , 2005, IEEE Intell. Syst..

[12]  R. Putnam.  Bowling Alone: America's Declining Social Capital , 1995, The City Reader.

[13]  David Martens,et al.  Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector , 2015, Decis. Support Syst..

[14]  Chulwoo Jeong,et al.  A binary classification method for bankruptcy prediction , 2009, Expert Syst. Appl..

[15]  N. Hermes,et al.  The Empirics of Microfinance: What Do We Know? , 2007 .

[16]  Bart Baesens,et al.  Social network analysis for customer churn prediction , 2014, Appl. Soft Comput..

[17]  Bart Baesens,et al.  Credit scoring for microfinance: is it worth it? , 2012 .

[18]  J. Copestake.  Mainstreaming Microfinance: Social Performance Management or Mission Drift? , 2007 .

[19]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[20]  Pedro M. Domingos.  Mining Social Networks for Viral Marketing , 2022 .

[21]  Foster J. Provost,et al.  Design principles of massive, robust prediction systems , 2012, KDD.

[22]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[23]  Hua Li,et al.  Demographic prediction based on user's browsing behavior , 2007, WWW '07.

[24]  Sharad Goel,et al.  Who Does What on the Web: A Large-Scale Study of Browsing Behavior , 2012, ICWSM.

[25]  Venkata Rama Kiran Garimella,et al.  Inferring audience partisanship for YouTube videos , 2013, WWW '13 Companion.

[26]  Chrysanthos Dellarocas,et al.  Credit Scoring with Social Network Data , 2014, Mark. Sci..

[27]  David J. Hand,et al.  Optimal bipartite scorecards , 2005, Expert Syst. Appl..

[28]  Mark Schreiner,et al.  Credit Scoring for Microfinance: Can It Work? , 2000 .

[29]  Manfred Zeller,et al.  Determinants of Repayment Performance in Credit Groups: The Role of Program Design, Intragroup Risk Pooling, and Social Cohesion , 1998, Economic Development and Cultural Change.

[30]  David Martens,et al.  DEPARTMENT OF ENGINEERING MANAGEMENT Classification over bipartite graphs through projection , 2015 .

[31]  Lars Backstrom,et al.  The Anatomy of the Facebook Social Graph , 2011, ArXiv.

[32]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[33]  Foster J. Provost,et al.  Explaining Data-Driven Document Classifications , 2013, MIS Q..