The unbanked and poverty: predicting area-level socio-economic vulnerability from M-Money transactions

Engelmann, Gregor; Smith, Gavin; Goulding, James

Abstract

Emerging economies around the world are often characterized by governments and institutions struggling to keep key demographic data streams up to date. A demographic of interest particularly linked to social vulnerability is that of poverty and socioeconomic status. The combination of mass call detail records (CDR) data with machine learning has recently been proposed as a way to obtain this data without the expense required by traditional census and household survey methods. Based on a sample of 330k mobile phone subscribers resident in Dar es Salaam, Tanzania (7.6m M-Money records, 450.2m call and SMS event logs) this paper demonstrates the improvements that can be made via an alternate data stream: M-Money transaction records. An alternative to traditional banking services, particularly utilized by citizens unable to obtain a bank account, M-Money transactions provide a currently unexplored but potentially more powerful data set held by the same telecommunication companies. Comparing directly to CDR as used in prior work the results show that M-Money provides an increase in socio-demographic classification accuracy (average F1 score) from 65.9% (0.63) to 71.3% (0.7) at much finer-grained spatial regions than previously examined. Notably, the combined use of M-Money and CDR data only increases prediction accuracy (average F1 score) from 71.3% (0.7) to 72.3% (0.71), providing evidence that M-Money is informationally subsuming CDR data. The reasons for this and the importance/contributions of individual features are subsequently investigated.

Citation

Engelmann, G., Smith, G., & Goulding, J. (2018, December). The unbanked and poverty: predicting area-level socio-economic vulnerability from M-Money transactions. Presented at 2018 IEEE international Conference on Big Data, Seattle, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2018 IEEE international Conference on Big Data
• Start Date: Dec 10, 2018
• End Date: Dec 13, 2018
• Acceptance Date: Oct 16, 2018
• Online Publication Date: Dec 10, 2018
• Publication Date: Dec 10, 2018
• Deposit Date: Dec 10, 2018
• Publicly Available Date: Dec 10, 2018
• Publisher: Institute of Electrical and Electronics Engineers
• Keywords: M-Money; M-Pesa; Poverty prediction; CDR
• Public URL: https://nottingham-repository.worktribe.com/output/1394555
• Contract Date: Dec 10, 2018