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Predicting financial trouble using call data—On social capital, phone logs, and financial trouble

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  • Rishav Raj Agarwal
  • Chia-Ching Lin
  • Kuan-Ta Chen
  • Vivek Kumar Singh

Abstract

An ability to understand and predict financial wellbeing for individuals is of interest to economists, policy designers, financial institutions, and the individuals themselves. According to the Nilson reports, there were more than 3 billion credit cards in use in 2013, accounting for purchases exceeding US$ 2.2 trillion, and according to the Federal Reserve report, 39% of American households were carrying credit card debt from month to month. Prior literature has connected individual financial wellbeing with social capital. However, as yet, there is limited empirical evidence connecting social interaction behavior with financial outcomes. This work reports results from one of the largest known studies connecting financial outcomes and phone-based social behavior (180,000 individuals; 2 years’ time frame; 82.2 million monthly bills, and 350 million call logs). Our methodology tackles highly imbalanced dataset, which is a pertinent problem with modelling credit risk behavior, and offers a novel hybrid method that yields improvements over, both, a traditional transaction data only approach, and an approach that uses only call data. The results pave way for better financial modelling of billions of unbanked and underbanked customers using non-traditional metrics like phone-based credit scoring.

Suggested Citation

  • Rishav Raj Agarwal & Chia-Ching Lin & Kuan-Ta Chen & Vivek Kumar Singh, 2018. "Predicting financial trouble using call data—On social capital, phone logs, and financial trouble," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-17, February.
    • Handle: RePEc:plo:pone00:0191863
    DOI: 10.1371/journal.pone.0191863
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