Mitigating the Risks of Financial Exclusion: Predicting Illiteracy with Standard Mobile Phone Logs

  • Pål SundsøyEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)


The present study provides the first evidence that illiteracy can be predicted from standard mobile phone logs. By deriving a broad set of novel mobile phone indicators reflecting users’ financial, social and mobility patterns this study addresses how supervised machine learning can be used to predict individual illiteracy in an Asian developing country, externally validated against a large-scale survey. On average the model performs 10 times better than random guessing with a 70% accuracy. Further it reveals how individual illiteracy can be aggregated and mapped geographically at cell tower resolution. In underdeveloped countries such mappings are often based on out-dated household surveys with low spatial and temporal resolution. One in five people worldwide struggle with illiteracy, and it is estimated that illiteracy costs the global economy more than $1 trillion dollars each year. These results potentially enable cost-effective, questionnaire-free investigation of illiteracy-related questions on an unprecedented scale.


Mobility Pattern Minority Class Class Imbalance Underdeveloped Country Illiteracy Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Chibba, M.: Financial inclusion, poverty reduction and the millennium development goals. Eur. J. Dev. Res. 21(2), 213–230 (2009)CrossRefGoogle Scholar
  2. 2.
    IHSN: How (well) is Education Measured in Household Surveys? IHSN working paper 002 (2009)Google Scholar
  3. 3.
    Lokanathan, S., Lucas Gunaratne, R.: Behavioral insights for development from Mobile Network Big Data: enlightening policy makers on the State of the Art. (2014). SSRN 2522814Google Scholar
  4. 4.
    Blumenstock, J., Cadamuro, G., On, R.: Predicting poverty and wealth from mobile phone metadata. Science 350(6264), 1073–1076 (2015)CrossRefGoogle Scholar
  5. 5.
    Steele, J.E., Sundsøy, P., Pezzulo, C., Alegana, V., Bird, T., Blumenstock, J., Bjelland, J., Engø-Monsen, K., de Montjoye, Y.A., Iqbal, A., Hadiuzzaman, K., Lu, X., Wetter, E., Tatem, A., Bengtsson, L.: Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14(127), 20160690 (2017)CrossRefGoogle Scholar
  6. 6.
    Sundsøy, P., Bjelland, J., Reme, B.A., Iqbal, A., Jahani, E.: Deep learning applied to mobile phone data for Individual income classification. In: ICAITA (2016)Google Scholar
  7. 7.
    Toole, J.L., Lin, Y.R., Muehlegger, E., Shoag, D., González, M.C., Lazer, D.: Tracking employment shocks using mobile phone data. J. R. Soc. Interface 12(107), 20150185 (2015)CrossRefGoogle Scholar
  8. 8.
    Sundsøy, P., Bjelland, J., Reme, B.A., Jahani, E., Wetter, E., Bengtsson, L.: Estimating individual employment status using mobile phone network data. arXiv preprint arXiv:1612.03870 (2016)
  9. 9.
    Wesolowski, A., Qureshi, T., Boni, M.F., Sundsøy, P.R., Johansson, M.A., Rasheed, S.B., Engø-Monsen, K., Buckee, C.O.: Impact of human mobility on the emergence of dengue epidemics in Pakistan. Proc. Natl. Acad. Sci. 112(38), 11887–11892 (2015)CrossRefGoogle Scholar
  10. 10.
    Lu, X., Wrathall, D.J., Sundsøy, P.R., Nadiruzzaman, M., Wetter, E., Iqbal, A., Qureshi, T., Canright, G.S., Engø-Monsen, K., Bengtsson, L.: Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen. Clim. Change 138(3–4), 505–519 (2016)CrossRefGoogle Scholar
  11. 11.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat., 1189–1232 (2001)Google Scholar
  12. 12.
    Eagle, N., Macy, M., Claxton, R.: Network diversity and economic development. Science 328(5981), 1029–1031 (2010)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Telenor Group Research, Big Data AnalyticsFornebuNorway

Personalised recommendations