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Good practice Imported

BETTO — Colombia’s AI system for targeting early childhood care to vulnerable children

Colombia · Bogotá · See the Colombia profile · See the Bogotá profile

Evidence: Observational / pre–post Top 36% 67/100 · Ask Evidence Copilot about this practice

ICBF's BETTO uses AI to geo-locate Colombia’s most vulnerable 0–5-year-olds and match them with qualified childcare operators. By mid-2021: 217,000 previously unserved children reached, 1,138 contracts (COP 2.2 trillion), 743,741 care slots across 32 departments.

217,000 children
Previously unserved children reached (by 30 June 2021)
69,600 children
Children reached in rural areas (by 30 June 2021)
14,600 children
Children reached from ethnic minority communities (by 30 June 2021)
1,138 contracts
Contracts awarded (by 30 June 2021)
2.2 trillion COP
Contract value (by 30 June 2021)
743,741 slots
National early-childhood care slots
12,738 bids
Operator bids processed automatically
24 to 9 months
Adoption processing time reduction (ADA)
3,480+ adoptions
Adoptions facilitated (ADA)
BETTO — Colombia’s AI system for targeting early childhood care to vulnerable children

Details

Maturity
Established
Promoter
Instituto Colombiano de Bienestar Familiar (ICBF)
Period
2020-present
Keywords
early childhood, social targeting, procurement optimization, machine learning, poverty, welfare

Context

BETTO (Bienestar, Eficiencia, Transparencia, Tecnología y Oportunidad) is an AI-powered targeting and procurement platform developed by Colombia's Instituto Colombiano de Bienestar Familiar (ICBF), with technical and financial support from the World Bank, the Inter-American Development Bank (IDB), UNICEF, Microsoft Colombia and Colombia Compra Eficiente. It launched in 2020 and operates nationally across all 32 Colombian departments.

Objectives

BETTO addresses two linked challenges: identifying which children aged 0-5 lack access to ICBF early-childhood services, and selecting the best-qualified operators to deliver those services.

Activities

The system combines three AI methodologies: geo-referenced optimisation to target potential beneficiaries using 11 poverty and vulnerability variables, knowledge-structure reasoning to evaluate operator qualifications, and predictive modelling to forecast operator performance. Operators are ranked on 28 quality indicators covering territorial experience, operational and financial capacity, historical performance and sanctions history. A companion tool, ADA (Asistente de Adopciones), applies AI to streamline adoption administration.

Results

By 30 June 2021, BETTO had reached 217,000 previously unserved vulnerable children, including 69,600 in rural areas and 14,600 from ethnic minority communities, across 1,138 contracts valued at COP 2.2 trillion. It processed 12,738 operator bids automatically, optimised 69,000 existing service units and created 32 new units in high-vulnerability areas, for a national total of 743,741 early-childhood care slots. ADA cut adoption-processing time from 24 months to 9 months and has facilitated over 3,480 adoptions.

Conclusions

Multi-stakeholder co-design involving government, multilateral institutions and the private sector appears to have accelerated quality and trust in the system. IDB's fAIrLAC network flags BETTO as a replicable Latin American model, though no confirmed cross-country deployment is documented, and no post-deployment algorithmic audit or formal redress mechanism is publicly documented.

Implementation

Implementation detail (cost, timeline, staffing, conditions for success) is not yet available for this practice.

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Data sources

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