Tackling fraud in UK aid

The government’s approach to managing fraud in the aid programme is broadly relevant and effective – but more can be done to find fraud cases by improving oversight and intelligence sharing across aid-spending departments, and streamlining and enhancing whistleblowing systems.

  1. Status: Completed
  2. Published: 8 April 2021
  3. Type: Rapid review
  4. Subject: Anti-corruption, tax avoidance and fiduciary risk
  5. Lead commissioner: Tarek Rouchdy

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Review

We found that the government’s approach to managing fraud in the aid programme is broadly relevant and effective – but more can be done to find fraud cases by improving oversight and intelligence sharing across aid-spending departments, and streamlining and enhancing whistleblowing systems. We made four recommendations to the government.

Findings

Relevance: To what extent do departments have systems, processes, governance structures, resources and incentives in place to manage risks to their UK aid expenditure from fraud?

  • The Cabinet Office Counter Fraud Function has strengthened counter-fraud capability but acts only in an advisory capacity and does not have an Official Development Assistance (ODA) specialism
  • The main ODA-spending departments have adequate systems, processes and structures in place to manage ODA fraud risk albeit with differences and weaknesses
  • Some departments’ approaches to fraud risk management in ODA are less developed than others
  • All departments have a ‘zero tolerance to fraud’ but differ in their understanding of what this means in practice
  • There are multiple whistleblowing and complaints mechanisms that vary in accessibility
  • Outsourcing-related ODA fraud risks lack systematic scrutiny by third-line counter-fraud specialists
  • Some fraud risk in contracts and contract management could be better managed

Effectiveness: How effectively do departments prevent, detect and investigate fraud at portfolio, individual programme delivery and partner levels?

  • We observed measures to prevent and investigate alleged fraud to be operating as designed but detection levels are low
  • Most stakeholders believe more fraud is taking place but that people are disincentivised from reporting it
  • There is increased focus on risks down the delivery chain but poor intelligence sharing at the first-tier partner level
  • Disincentives to look for and report fraud mean even some known frauds are not being reported

Learning: How effectively do departments capture and apply learning in the development of their systems and processes for fraud risk management in their aid programmes?

  • Learning takes place through the fraud investigation process
  • There is a good spirit of learning between departments, but it is mostly informal
  • There are good examples of departments working with individual suppliers to address weaknesses but limited widespread learning efforts down the delivery chain

Recommendations

  1. Consideration should be given to establishing a centralised ODA counter-fraud function to ensure good practice and consistency of the ODA counter-fraud response and share intelligence across all ODA spend.
  2. ODA-spending departments should review and streamline external whistleblowing and complaints reporting systems and procedures, and provide more training to delivery partners down the delivery chain on how to report safely.
  3. Counter-fraud specialists should increase independent oversight of ODA outsourcing, including systematically reviewing failed or altered procurements, and advising on changes to strengthen the actual and perceived integrity of ODA procurement.
  4. To aid understanding and learning, ODA counter-fraud specialists should invest in collecting and analysing more data, including on who bears the cost of fraud, and trends in whistleblowing and procurement.

 

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Timeline

Approach

Published 9 October 2020

Evidence gathering

Complete

Review publication

Published 8 April 2021

Government response

Published 20 May 2021

Parliamentary scrutiny

IDC hearing 7 July 2021

ICAI follow-up

Published 30 June 2022

Further follow-up

Published 18 July 2023

Third follow-up

Expected summer 2024