Novissi – Machine Learning on Mobile Phone Metadata for Individual Poverty Prediction and Beneficiary Targeting

Overview

The Government of Togo used a phone-based poverty-prediction model during the rural expansion of Novissi, its COVID-19 emergency cash-transfer programme, to help prioritise potential beneficiaries within already selected cantons. The retained evidence strongly supports that this component used anonymised mobile-phone metadata and survey-based training data to estimate poverty at individual level and to rank potential recipients for programme outreach. As with the area-targeting component, the case is best framed as a government use of a research-developed targeting tool inside a broader emergency programme rather than as a fully disclosed state-built AI product.

Novissi's second phase sought to extend support into poorer rural areas during a period when household surveys and in-person registration were difficult. The strongest source base shows that the individual-targeting model relied on approximately 150 features derived from call detail records and related mobile-phone usage patterns, trained against survey-based consumption data. With financial support from the World Bank, the research team conducted a large phone survey of roughly 10,000 individuals in September 2020, immediately prior to the rural expansion, to provide ground-truth information on living conditions. The team went to considerable lengths to ensure representativeness through adaptive survey design and the use of survey sample weights, so that difficult-to-reach populations such as the extreme poor and those living in remote villages were represented in the training data. Each survey respondent provided informed consent to participate. The mobile phone metadata used as model inputs included information about the date, time, duration, and cell tower used for calls and texts, as well as data on mobile data usage volume and mobile money transaction patterns. From these raw data the research team derived aggregate statistics of each subscriber's phone-use patterns, including features correlated with wealth such as the total volume of international phone calls and average mobile money balance. The algorithms were then used to generate a consumption estimate for each of the 5.7 million mobile subscribers in the country. Public documentation is relatively strong on the research methodology and fairness trade-offs, but weaker on the internal government operating rules used when model outputs were translated into programme decisions.

This component sat downstream of a geographic pre-selection step that identified priority cantons. Within the 100 poorest cantons, where approximately 580,000 citizens lived, the government in collaboration with GiveDirectly had secured sufficient funding to provide benefits to roughly 57,000 individuals. The model generated ranked estimates and those estimated to consume less than 1.25 US dollars per day were prioritised for Novissi transfers. The government retained control over the programme parameters, and the sources describe the model as part of a broader targeting pipeline rather than as an autonomous benefit-decision engine. Preliminary evaluation results reported by IPA indicated that assuming the goal is to reach the poorest 57,000 people in the 100 poorest cantons, the satellite-plus-phone approach was significantly more accurate than the alternative approaches available to the government, providing benefits to nearly 2.5 times as many of the poorest citizens as an occupation-based targeting approach. The CEGA case study reports a 42 percent improvement in targeting precision relative to naive geographic targeting and states that 154,238 citizens received unconditional cash transfers between December 2020 and April 2021 through the scaled approach.

Data privacy was a central concern in programme design. Neither GiveDirectly nor the Government of Togo had access to any data collected by the mobile phone operators, and neither received access to the poverty scores derived from the mobile data. Instead, the research team produced a list of eligible beneficiaries based on the poverty scores, and the government received only that list. The researchers implemented strict anonymisation, encryption, and access protocols, and UC Berkeley's Committee for the Protection of Human Subjects reviewed all research procedures. The research team also designed algorithmic audits to examine whether specific vulnerable subgroups were more likely to be excluded.

The case is important because it shows a real, high-consequence use of model-based targeting in social protection under crisis conditions, with unusually strong academic validation compared with many other public-sector examples. A public replication repository was released on GitHub with notebook-level replication for survey processing, satellite poverty mapping, machine-learning modelling, targeting simulations, and fairness analysis, with synthetic data released for the non-public CDR inputs. But the case also remains partner-heavy: much of the most detailed evidence comes from academic and development-partner materials, not from direct state-authored operational disclosure. The safest production framing is therefore that Togo operationally used a phone-metadata-based poverty-prediction model in Novissi, while leaving broader claims about comparative performance and programme-wide effects carefully bounded.

Classification

Programme Details

The Novissi programme is Togo's emergency cash transfer platform launched in April 2020 in response to the COVID-19 pandemic. This case covers the individual-level phone-metadata targeting component used in the rural expansion phase, not the full Novissi delivery chain.

Implementation Details

Risk & Oversight

Deployment & Outcomes

Sources

1. SRC-005-TGO-005 Center for Effective Global Action (2020) 'Using AI and Digital Data to Target Cash Transfers in Togo'. Available at: https://cega.berkeley.edu/collection/ai-assisted-cash-transfers-togo/ (Accessed: 27 March 2026).
   https://cega.berkeley.edu/collection/ai-assisted-cash-transfers-togo/
2. SRC-001-TGO-005 Aiken, E., Bellue, S., Karlan, D., Udry, C. and Blumenstock, J. (2022) 'Machine learning and phone data can improve targeting of humanitarian aid', Nature, 603, pp. 864–870. doi:10.1038/s41586-022-04484-9.
   https://www.nature.com/articles/s41586-022-04484-9
3. SRC-004-TGO-005 Laiken, E. (2022) 'togo-targeting-replication', GitHub repository. Available at: https://github.com/emilylaiken/togo-targeting-replication (Accessed: 27 March 2026).
   https://github.com/emilylaiken/togo-targeting-replication
4. SRC-003-TGO-005 Innovations for Poverty Action (2021) 'Using Mobile Phone and Satellite Data to Target Emergency Cash Transfers in Togo', 12 January. Available at: https://poverty-action.org/using-mobile-phone-and-satellite-data-target-emergency-cash-transfers-togo (Accessed: 27 March 2026).
   https://poverty-action.org/using-mobile-phone-and-satellite-data-target-emergency-cash-transfers-togo
5. SRC-002-TGO-005 World Bank (2021) 'Prioritizing the poorest and most vulnerable in West Africa: Togo's Novissi platform for social protection uses machine learning, geospatial analytics, and mobile phone metadata for the pandemic response', Results Briefs, 13 April. Washington, DC: World Bank.
   https://www.worldbank.org/en/results/2021/04/13/prioritizing-the-poorest-and-most-vulnerable-in-west-africa-togo-s-novissi-platform-for-social-protection-uses-machine-l