In particular, the United Nations, together with the International Partnership Against Corruption in Sport (IPACS) and the International Olympic Committee (IOC), prepared for the event the Guide on the Prosecution of Bribery in Sport. The guide examines specific real-life examples of corruption in the organization and conduct of sporting events, the challenges faced by law enforcement authorities in investigating such cases, and the knowledge and skills required for successful prosecution. It also recommends prosecutors handling briberyin-sport cases to adopt and, where appropriate, to use a methodology of prosecution based on the core elements of establishing this crime, following the scheme and the pneumonic OBITA:

• O (obligation) – since the primary purpose of a bribe is to influence the actions of a person who has the authority and duty to act in a certain way, establishing the source and scope of that obligation is fundamental to proving bribery. To this end, the prosecutor must: identify the role of the person vested with specific powers; determine the legal basis on which those powers may be exercised; and define the obligations attached to the exercise of those powers (which may be positive, such as the obligation to conduct tenders for awarding contracts, or negative, such as the obligation not to engage in private negotiations with tender participants). Overall, at this stage the prosecutor must understand and demonstrate how the powers should have been exercised and how they were improperly used or ignored.

• B (beneficiary) – the improper exercise of authority by an official is based on the intention of another person to obtain a benefit that would not otherwise have been available, or to secure an outcome that could not otherwise have been guaranteed. The beneficiary may be a legal or natural person, and the anticipated benefit may be direct (for example, concealing a positive doping test result) or indirect (such as ensuring the loss of a tennis player in a competition match).

• I (interaction) – once law enforcement authorities have identified the relevant official, the corresponding obligation, the beneficiary, and the benefit, the key task becomes establishing the link between the official and the beneficiary. As noted in the guide, the circumstances of the established interaction are likely to constitute an important source of evidence of an alleged or actual bribe.

• T (transfer) – where it is assumed that an offence has been committed, the prosecutor should seek to establish the transfer of some form of inducement from the beneficiary to the official; this “transfer” constitutes the bribe. Most commonly, a bribe takes the form of money (whether fiat or virtual currency). In more complex cases, however, it may take the form of options, shares, or other financial instruments, or intangible benefits such as the payment of travel expenses, goods, or other advantages.

• A (advantage) – finally, the last step consists in proving that the beneficiary obtained a direct or indirect advantage, or that there was a clear intention to obtain such an advantage.

The United Nations Office on Drugs and Crime (UNODC), together with the IOC, also presented at CoSP their interactive platform developed under an initiative examining legal approaches to combating the manipulation of sports competitions. The platform provides information on national legislation that criminalizes the manipulation of sports competition results (currently covering 48 countries), as well as UNODC publications on competition manipulation and the text of a model law that may be used by legislators in other countries considering the introduction of criminal liability for such conduct.

In addition, the non-profit organization International Centre for Sport Security (ICSS) prepared the Guide on the Use of Artificial Intelligence on Anti-Corruption in Sport: Opportunities, Risks and Emerging Practices. The guide is based on an analysis of AI applications across various sectors – including finance, law enforcement, and public governance – and proposes ways to adapt these approaches to the sport sector.

In particular, the guide notes that in the financial sector AI enables the analysis of large volumes of transactional data, the detection of anomalies, complex money-laundering schemes, and hidden links between companies and individuals, as well as a reduction in false positives in risk monitoring. An important element is not only the identification of individual suspicious transactions, but also the development of behavioral models that distinguish normal financial activity from atypical patterns. Similar approaches can be applied in sport: AI can be used to analyze player transfers, agents’ fees, club acquisitions, sponsorship agreements, and media contracts. The same methods used to detect inflated prices, payment splitting, or multi-layered financial transaction chains designed to obscure the economic substance of transactions can reveal abnormal transfer fees, hidden payments to third parties, opaque ownership structures, and indicators of money laundering through sports organizations.

In law enforcement, AI is used for crime forecasting and criminal network analysis. It enables the processing of heterogeneous data sources – from police reports and judicial information to communication metadata and open-source intelligence – in order to identify persistent patterns, links between actors, and high-risk areas. This approach facilitates a shift from reacting to crimes already committed toward a proactive, intelligence-led model. In the sports sector, this experience is proposed for detecting match-fixing, corruption networks, and organized criminal influence. Network analysis of interactions between players, referees, agents, officials, and third parties, combined with betting and financial data, makes it possible to identify collusion, recurring manipulation schemes, and criminal infiltration of clubs and federations.

In public governance, AI and open data analytics are used to enhance transparency, accountability, and the quality of decision-making. Algorithms help identify risks in public procurement, conflicts of interest, inefficient use of funds, and anomalies in resource allocation. At the same time, particular emphasis is placed on the responsible use of AI, including the protection of human rights, personal data, explainability of decisions, and democratic oversight. Applied to sport, this approach implies the use of AI and open data to analyze procurement and contracts, cross-check officials’ declarations against company and beneficial ownership registries, identify repeated tender winners, price outliers, and other indicators of corruption risks in the governance of sports organizations and events.

As a distinct cross-cutting area drawing on the experience of all three sectors, the guide addresses AI-supported whistleblower protection systems. In non-sport sectors, AI is used to create secure anonymous reporting channels, automatically remove identifying features, analyze the content of disclosures, and detect recurring patterns of misconduct. In sport, this experience is proposed as a means of increasing trust in reporting mechanisms for corruption, match-fixing, harassment, and abuse. AI can assist in prioritizing reports, identifying systemic problems and early warning signals, and monitoring potential retaliation against whistleblowers.

At the same time, the document emphasizes a common principle applicable across all sectors: AI should not replace humans, but rather enhance their analytical and managerial capabilities. Accordingly, the authors stress the need for robust AI governance mechanisms, sector-specific standards, independent algorithmic verification, transparency, and continuous monitoring of impacts. This approach is proposed to be fully transferred to sport to ensure that the use of AI does not undermine trust, but instead strengthens integrity, fairness, and accountability within the sporting system. Overall, the guide concludes that sport is neither unique nor isolated: it faces the same corruption risks as other complex, capital-intensive, and transnational sectors. For this reason, borrowing and adapting AI practices from finance, law enforcement, and public governance is seen as the most realistic and effective path for developing anti-corruption mechanisms in sport.

The issue of combating various corrupt practices in sport remains an important area of research beyond the CoSP framework.

Earlier, UNODC published the report Safeguarding Sport from Corruption: Focus on BRICS Countries, which analyzes the specific features of illicit practices and anti-corruption efforts in sport in countries such as Brazil, Russia, India, China, South Africa, the United Arab Emirates, Iran, and Ethiopia. As in the previous UNODC report focusing on countries of the Americas and the Caribbean, the document examines six key areas in which corruption most frequently occurs: manipulation of sports competitions, illegal betting, major sporting events, athlete transfers, governance of sports organizations, and corruption linked to organized crime. The report also presents UNODC recommendations for enhancing the effectiveness of anti-corruption efforts in sport in BRICS countries (largely consistent with those in the report Safeguarding Sport from Corruption: Focus on the Americas and the Caribbean), including:

• strengthening national and international cooperation and information exchange;
• developing specialized training and building targeted capacity;
• reinforcing legal frameworks and enforcement mechanisms;
• addressing gaps in policy development and risk assessment; and
• improving the collection of data on corruption in sport.