Fraud & integrity analytics in donor-funded projects
Data analytics on project expenditures
- Who we serve
- Multilateral Development Banks
- Category
- Audit, Oversight & Accountability
- Value
- Risk management
- Human role
- Human approves every decision
- Maturity
The problem
Without fraud & integrity analytics in donor-funded projects, MDB integrity units rely on sampling and after-the-fact review, while the signals in transaction records, financial statements, audit evidence, prior findings sit unread, so problems surface only after the loss, incident or complaint.
How it works
Fraud & integrity analytics in donor-funded projects - Data analytics on project expenditures. It draws on transaction records, financial statements, audit evidence, prior findings and is typically owned by MDB integrity units. Where the human comes in: MDB integrity units review and approve before any action affects a person, a payment or a case; the AI flags, ranks and drafts. It is at pilot stage, with documented pilots or single deployments; examples include World Bank INT (2023, data analytics unit, 20% increase in case identification); Global Fund OIG (2022, analytics on grant expenditures); UN OIOS (2023, analytics for investigation triage).
Where the human comes in
MDB integrity units review and approve before any action affects a person, a payment or a case; the AI flags, ranks and drafts
What it needs
transaction records, financial statements, audit evidence, prior findings
What to plan for
Data access across implementing partners
In the field
World Bank INT (2023, data analytics unit, 20% increase in case identification); Global Fund OIG (2022, analytics on grant expenditures); UN OIOS (2023, analytics for investigation triage)
World Bank INT Annual Report (2024); Global Fund OIG
Public deployments by other organizations, listed for context. Not Sphere projects.