Evaluating Machine Learning–Driven Fraud Detection: Evidence from AquaData in EPMAPS

The objective of this Technical Cooperation is to determine the causal impact of machine learning-driven inspection targeting, through the AquaData algorithm, on fraud detection effectiveness, inspector productivity, revenue recovery, and apparent losses at EPMAPS.

Project Detail

Country

Ecuador

Project Number

EC-T1647

Approval Date

Project Status

Preparation

Project Type

Technical Cooperation

Sector

WATER AND SANITATION

Subsector

WATER SUPPLY URBAN

Lending Instrument

Lending Instrument Code

Modality

Facility Type

Environmental and Social Impact Category (ESIC)

Total Cost

USD 150,000.00

Country Counterpart Financing

Original Amount Approved

USD 150,000.00

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Preparation Phase

https://www.iadb.org/document.cfm?id=EZIDB001878-1883266182-3

TC Abstract

EC-T1647 - TC Abstract.pdf

Oct. 01, 2026

English

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