FALCON ¦ Policy Brief No. 2 Leveraging AI in the Fight Against Corruption

AI Tools Against Corruption – Practical Steps for EU Policymakers

Artificial intelligence (AI) is no silver bullet, but it can materially strengthen the European Union’s capacity to prevent, detect and investigate corruption. Traditional methods struggle with scale: sprawling public procurement records, cross-border asset routes, complex beneficial ownership chains and encrypted communications generate data volumes and patterns that outstrip human processing. AI and machine learning (ML) can sift these data, surface anomalies in near real time, and point investigators to the highest-risk leads. At the same time, unregulated or poorly governed deployments risk privacy violations, unfair bias, opaque decision-making and mission creep. For EU policymakers, the immediate priority is to adopt a balanced approach that captures AI’s operational benefits while embedding robust legal, ethical and oversight safeguards.

A policy-first agenda to incorporate AI into anti-corruption work

EU Member States should craft national strategies that explicitly position AI and ML as core tools in anti-corruption efforts. Those strategies need to do more than endorse technology: they must define lawful use-cases, set resource allocations, plan training and identify data integration paths. Cross-disciplinary collaboration – bringing together technical experts, ethicists, legal advisors, public procurement officials and investigative practitioners – will produce strategies that are both innovative and safe. Equally important is the use of standardised formats and registries to reduce friction when AI models consume multiple data sources; without harmonised data, analytical gains are limited and costly to achieve.

Design governance frameworks tailored to corruption risks

Deploying AI in law enforcement and oversight settings demands tailored governance. Generic AI rules are useful as a foundation, but anti-corruption applications raise particular issues: the need for explainability when AI flags procurement irregularities, safeguards against wrongful targeting when models infer conflicts of interest from imperfect registries, and strict conditions for analysing sensitive personal data. A governance framework should require risk assessments, human oversight, continuous bias testing, transparent documentation of model design and provenance, and alignment with existing EU data protection and criminal procedure safeguards. Internationally recognised principles, such as accountability-focused approaches, should be integrated into any framework to strengthen legitimacy and interoperability.

Bastian Schwind-Wagner _Follow

"AI can significantly strengthen efforts to prevent and detect corruption by analysing large, diverse datasets and highlighting anomalies that would be impractical for humans to spot at scale. When combined with clear legal safeguards and human oversight, these tools improve investigative efficiency without replacing essential procedural protections.

To realise this potential, policymakers must invest in targeted training, data harmonisation and ethical governance frameworks that address bias, privacy and accountability. Independent oversight and sustained funding will ensure AI deployments remain effective, transparent and respectful of citizens’ rights."

Invest in capacity building and iterative training for investigators

AI tools only deliver value when practitioners know how to use and question them. Law enforcement officers, procurement auditors and regulatory staff require iterative, scenario-based training that covers technical operation, legal constraints, ethical risks and the limits of algorithmic inference. Capacity building should include joint exercises with data scientists, forensic accountants and domain experts so that investigative teams can interpret model outputs – distinguishing between genuine leads and artefacts due to data gaps or model bias. Policymakers must budget for ongoing training cycles, not one-off sessions, to keep pace with evolving techniques used by corrupt actors and the countermeasures AI provides.

Enable data harmonisation and responsible sharing

AI performs best on consistent, well-structured data. The EU should encourage national and cross-border efforts to harmonise registries relevant to corruption investigations: public procurement records, contract databases, beneficial ownership registers, property and vehicle registers, and asset declarations. Standardised formats or centralised indexing mechanisms will make it feasible to build ML pipelines that detect suspicious patterns. Data sharing protocols must balance analytical needs with privacy and legal safeguards: controlled access, purpose limitation, audit trails, and robust anonymisation where appropriate. Removing legal and technical barriers to lawful data exchange between competent authorities across Member States will materially improve cross-border detection of sanctions evasion, shell company networks and other multi-jurisdictional schemes.

Establish independent oversight and evaluation

Independent oversight mechanisms are essential to maintain public trust and to ensure AI systems do not function as unchecked surveillance or litigation shortcuts. Oversight bodies should review system design, approve high-risk uses, audit outcomes, and require redress mechanisms for individuals adversely affected by AI-driven decisions. Periodic external evaluations of effectiveness and fairness should be mandated – assessing both investigative yield and the incidence of false positives or discriminatory impacts. The European AI Office and national equivalents can develop tailored protocols for anti-corruption deployments, ensuring technology use remains proportionate, necessary and transparent.

Practical use-cases where AI adds value

AI enhances several concrete anti-corruption activities. At border control, anomaly detection models can flag unusual vehicle or personnel movement patterns, correlating sensor feeds, crossing timestamps and staffing rosters to highlight potential collusion. For conflicts of interest, algorithms can cross-link ownership registries, tender records and asset declarations to reveal unexplained matches between decision-makers and contract beneficiaries. In public procurement, predictive analytics can surface procurement cycles and vendor behaviours consistent with bid rigging or favouritism. AI-driven web crawlers and natural language processing help extract relevant evidence from unstructured online sources. In sanctions evasion, graph analytics analytics combined with entity resolution can unravel shell company webs across jurisdictions and languages – an impossible task at scale without automated tools.

Addressing barriers: resources, trust and ethics

Several practical barriers slow adoption. Budget and staffing constraints limit procurement and maintenance of AI systems. Resistance to change and low digital literacy among some enforcement bodies reduce uptake and can lead to misuse. Data fragmentation and legal differences between Member States complicate cross-border investigations. Above all, ethical concerns – privacy, bias and lack of transparency – must be handled proactively. Policymakers should allocate stable funding lines for AI capabilities, include change management measures in rollout plans, harmonise data governance across jurisdictions where possible, and mandate ethics-by-design practices for procurement and development.

Recommendations summary for policymakers

1. Adopt national AI-in-anti-corruption strategies that set clear objectives, funding and multi-stakeholder governance.
2. Develop sector-specific AI governance frameworks requiring risk assessments, human oversight and ongoing bias monitoring.
3. Prioritise capacity building with hands-on training for law enforcement and oversight bodies.
4. Promote data harmonisation and lawful data sharing infrastructures to feed AI models reliably.
5. Create or empower independent oversight bodies to audit, evaluate and ensure accountability in AI use.

These steps will enable the EU to harness the analytical power of AI while limiting the risk to rights and procedural fairness.

Concluding note

AI can materially increase the efficiency and reach of anti-corruption efforts – improving early detection, supporting investigative triage and making sense of cross-border, multi-source data. Success requires deliberate policy choices: clear strategies, tailored governance, resourced capacity building, harmonised data practices and independent oversight. With those building blocks, EU institutions and Member States can use AI to strengthen transparency, protect public funds and reduce the systemic harms of corruption while safeguarding civil liberties and due process.

Dive deeper

• FALCON ¦ Shiji A. N., Gibson H., Guiver C. (2025). Recommendations for leveraging Artificial Intelligence (AI) in the fight against corruption. Policy Brief of Project FALCON. Centre of Excellence in Terrorism, Resilience and Organised Crime Research (CENTRIC) ¦ Link