SISBEN IV -- AI-Enhanced Data Analytics and Fraud Detection Layer for Social Programme Targeting

Overview

Colombia's Sistema de Identificacion de Potenciales Beneficiarios de Programas Sociales (SISBEN), administered by the Departamento Nacional de Planeacion (DNP), is an algorithmic system that individually rates the Colombian population according to socioeconomic status to determine eligibility for social programmes. SISBEN has operated since 1994 and has undergone four major revisions. The fourth version (SISBEN IV) introduced significant changes including automated cross-referencing of 34 or more administrative databases, data analytics technologies, and automated anomaly detection to identify inconsistencies and potential fraud in beneficiary data.

SISBEN produces an individual score from 0 to 100 (where 100 indicates greater prosperity) and classifies people into groups based on living conditions and income. Each social programme entity sets its own cut-off points on the SISBEN score or group to determine eligibility. The system is used for targeting at least 18 social programmes of different characteristics, and the subsidised health system using SISBEN covers more than half of Colombia's population. Plans project expansion to reach 40.5 million people, approximately 84 percent of the population.

The SISBEN IV redesign was motivated by a 2016 analysis conducted jointly by DNP, the World Bank, and ECLAC that identified critical problems with SISBEN III: the absence of an income component, the absence of an interoperable system to verify citizen-reported information, and an algorithm that concentrated 50 percent of the score on health, education, and housing variables. SISBEN IV shifted from a quality-of-life framework to a 'presumption of income' and 'income generating capacity' approach, and introduced interoperability with administrative databases covering health, pensions, education, work, real estate, taxes, financial risks, social benefits, transportation, victim registration, and public services.

The automated fraud detection and validation component is a key innovation. Decree 441 of May 2017 gave DNP charge of database validation and quality controls and enabled public entities to share information without formal agreements. Cross-referencing of pensions and health system databases with the SISBEN database identified 653,000 cases tagged 'under verification' for high income discrepancies and deceased persons. There are nine defined reasons for tagging cases as 'under verification,' ranging from unreported changes of residence to income records higher than DNP-determined thresholds. Upon tagging, territorial entities inform the affected person and decide on exclusion or reclassification; within six months, benefit-administering entities are notified to withdraw benefits if warranted.

The system has attracted significant legal and civil society scrutiny. Colombia's Constitutional Court, in Ruling T-716/17, found the system's design to be 'arbitrary or unfair' while appearing to be objective. The Karisma Foundation, a Colombian civil society organisation, filed access-to-information requests about SISBEN's algorithm, which DNP refused citing confidentiality because revealing the algorithm 'may compromise the country's macroeconomic and financial stability' and 'constitutes fraud.' Academic analysis by Joan Lopez (Tilburg University) and Juan Diego Castaneda, published through the Karisma Foundation in 2020, characterised SISBEN as an 'algorithmic assembly' that creates a surveillance-like integration of citizen data across State institutions, noting that citizens classified by the system cannot know how their data is classified and lack means to demand explanations for their rating.

Documented manipulation includes local officials modifying scores so people can receive benefits, threats to people in the SISBEN database to vote for certain candidates in exchange for keeping benefits, and local governments inflating low-scoring populations to receive bigger budgets. A 1997 assessment had already found increasing fraud in how SISBEN scores were obtained.

No specific algorithm type, model architecture, or technology vendor has been publicly disclosed. DNP develops specialised software internally that generates individual scores and structures the population classification.

Classification

Programme Details

Algorithmic poverty classification and fraud detection system that individually rates Colombia's population by socioeconomic status using statistical models and automated cross-referencing of 34+ administrative databases, used to determine eligibility for at least 18 social programmes.

Implementation Details

Risk & Oversight

Deployment & Outcomes

Sources

1. SRC-002-COL-001 DNP (n.d.) 'Que es el SISBEN?', Departamento Nacional de Planeacion. Available at: https://www.sisben.gov.co/Paginas/que-es-sisben.html (Accessed: 30 March 2026).
   https://www.sisben.gov.co/Paginas/que-es-sisben.html
2. SRC-001-COL-001 Lopez, J. and Castaneda, J.D. (2020) 'Automation, Digital Technologies and Social Justice: The Case of SISBEN in Colombia', in Cerrillo-i-Martinez, A. and Fabra-Abat, P. (eds.) AI, Human Rights and Social Justice. Karisma Foundation / CETyS Universidad de San Andres. Available at: https://web.karisma.org.co/ (Accessed: 30 March 2026).
   https://web.karisma.org.co/
3. SRC-004-COL-001 Farhat, Y. (2021) 'Chosen by a Secret Algorithm: Colombia's top-down pandemic payments', Center for Human Rights and Global Justice, NYU School of Law. Available at: https://chrgj.org/2021-12-14-transformer-states-colombia/ (Accessed: 30 March 2026).
   https://chrgj.org/2021-12-14-transformer-states-colombia/
4. SRC-003-COL-001 fAIrLAC / IDB (n.d.) 'SISBEN', fAIrLAC, Inter-American Development Bank. Available at: https://fairlac.iadb.org/en/sisben (Accessed: 30 March 2026).
   https://fairlac.iadb.org/en/sisben