SAI Peru Uses IT to Optimize Oversight During Pandemic

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The Supreme Audit Institution (SAI) of Peru is implementing initiatives to digitally transform and technologically innovate how it prevents and detects risks of corruption and functional misconduct.

Cross-referencing massive online information with various public databases, in combination with large-scale, Big Data analysis, SAI Peru has identified opportunities to improve public management and pinpoint irregularities—nationally, regionally and locally.

In 2020, online information largely focused on programs and activities carried out within the COVID-19 health emergency framework. The new measures helped reveal, for example, 12,371 civil and public servants allegedly received benefits (in the form of basic necessities and food packages) from local governments despite not being entitled to such assistance based on vulnerability criteria. Deceased beneficiaries receiving these packages were also identified.

The data also detected occurrences of beneficiaries falsely collecting economic subsidies, including government officials and their relatives, citizens earning payroll income, people residing abroad, and the deceased.

SAI Peru oversight efforts included analyzing cash transfer information to identify variables—such as socioeconomic levels, income, location and education—and cross-checking the information with the Reactiva Peru Program (bank loans possessing a State guarantee) beneficiaries.

The SAI also looked into the “Acquisition and Distribution of Computer and/or Electronic Devices” and “Internet Services Contracting for Educational Institutions Within the Framework of Preventive Actions for the Spread of COVID-19” processes to validate National Identity Document students as well as their parents or guardians.

SAI Peru’s use of Big Data represents a qualitative leap—transitioning from sample evaluation to precise assessments that consider full data analyses.

Interventions will evolve from post-event to concurrent and will advance efforts toward timely pattern identification to more effectively predict irregular behaviors, enhance audit work and promote sustainable development.

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