Page 25 - FIGI - Use of telecommunications data for digital financial inclusion

Endnotes

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1 https:// www cignifi com/
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2 Cignifi and the IFC to Grow Usage of Mobile Money in Uganda. Cambridge, MA: Cignifi. https:// www .prnewswire com/
news -releases/ ifc -partners -with -cignifi -to -grow -usage -of -mobile -money -in -uganda -300030886 .html
3 Luciano Diettrich, Fábio de Souza, and André Guerreiro; Claro Brazil, Paper 4831 - 2020, Development of credit scores
with telco data using Machine Learning and agile methodology in Brazil, available at https:// www .sas com/ content/
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dam/ SAS/ support/ en/ sas -global -forum -proceedings/ 2020/ 4831 -2020 .pdf..
4 Turner M, Lee A, Schnare A, et al. (2006) Give Credit Where Credit Is Due: Increasing Access to Affordable Mainstream
Credit Using Alternative Data. Washington, DC: Political and Economic Research Council/The Brookings Institution.
5 Blumenstock, J., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science,
350(6264), 1073–1076.
6 Mobile money accounts also show financial transactions, and for many users can provide the same picture of finances
as a traditional deposit account, including size and frequency of deposits and transaction history.
7 Bjorkergren D and Grissen D (2015) Behaviour Revealed in Mobile Phone Usage Predicts Loan Repayment, available at
https:// papers .ssrn com/ sol3/ papers cfm ?abstract _id = 2611775..
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8 Pedro, J. S., Proserpio, D., & Oliver, N. (2015). MobiScore: Towards Universal Credit Scoring from Mobile Phone Data. In
User Modeling, Adaptation and Personalization (pp. 195–207). Springer, Cham. https:// doi org/ 10 1007/ 978 -3 -319 -20267
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9 Bjorkergren D and Grissen D (2015) Behaviour Revealed in Mobile Phone Usage Predicts Loan Repayment, available at
https:// papers .ssrn com/ sol3/ papers cfm ?abstract _id = 2611775..
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10 Bjorkergren D and Grissen D (2015) Behaviour Revealed in Mobile Phone Usage Predicts Loan Repayment, available at
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https:// papers .ssrn com/ sol3/ papers cfm ?abstract _id = 2611775..
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11 The Future of FinTech: A Paradigm Shift in Small Business Finance 19 (World Economic Forum 2015)
12 Innovation in Electronic Payment Adoption: The case of small retailers 25 (World Bank Group June 2016)
13 Gonzalez, M. C., Hidalgo, C. A., & Barabasi, A.-L. (2008). Understanding individual human mobility patterns. Nature,
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453(7196), 779–782. https:// doi org/ 10 1038/ nature06958
14 Lu, X., Wetter, E., Bharti, N., Tatem, A. J., & Bengtsson, L. (2013). Approaching the Limit of Predictability in Human
Mobility. Scientific Reports, 3. https:// doi org/ 10 1038/ srep02923
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15 Safaricom entered into partnership agreements with the Commercial Bank of Africa (CBA) and the Kenya Commercial
Bank (KCB) to offer M-PESA customers access to banking services, including savings products and loans. The CBA
agreement was entered into in 2013. The KCB agreement was entered into in 2015.
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16 Tencent’s WeBank applying “federated learning” in A.I. (DigFin 29 July, 2019) available at https:// www digfingroup com/
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webank -clustar/ .
17 For example, Section 222(h)(1) of the US Communications Act defines customer network proprietary information
(CNPI) is: “(A) information that relates to the quantity, technical configuration, type, destination, location, and amount
of use of a telecommunications service subscribed to by any customer of a telecommunications carrier, and that is
made available to the carrier by the customer solely by virtue of the carrier-customer relationship; and (B) information
contained in the bills pertaining to telephone exchange service or telephone toll service received by a customer of a
carrier; except that such term does not include subscriber list information.”
18 E.g., Monetary Authority of Singapore, Principles to Promote Fairness, Ethics, Accountability and Transparency (FEAT)
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in the Use of Artificial Intelligence and Data Analytics in Singapore’s Financial Sector, available at https:// www .mas gov
.sg/ ~/ media/ MAS/ News %20and %20Publications/ Monographs %20and %20Information %20Papers/ FEAT %20Principles
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%20Final .pdf and Smart Campaign Digital Credit Standards, available at http:// www .smartcampaign org/ .
19 A wide range of risks associated with artificial intelligence have been identified and data regulators internationally
are taking steps to address them. These include the limited “explainability” of decisions resulting from machine
learning, the potential for bias in datasets (including bias with respect to gender or ethnicity) to result in biased
decision-making, the problems of securing informed consent, and application of cyber security requirements. Use
of telecommunications data for fintech presents certain heightened risks. For example, individuals benefiting from
financial inclusion may be illiterate or at the very least unfamiliar with fair practice with respect to financial services. Use
of machine learning, especially federated learning, increases the absence of explainability in credit decision-making.
20 G20 High-Level Policy Guidelines on Digital Financial Inclusion for Youth, Women and SMEs 26 (G20, 2020)

Use of telecommunications data for digital financial inclusion 23