Illustration by Celine Bejjani
Although Lebanon has introduced anti-corruption laws on access to information, whistleblowing, and public procurement, implementation has been slow and insufficient. The country continues to rank in the lowest percentile for corruption in various studies across several years, according to the World Bank, the U.N., and other independent organizations.
As governments increasingly turn to digital tools and AI to process large volumes of data, new methods are emerging to understand corruption not simply as isolated acts, but as systems of interconnected relationships. Data-driven methods can help uncover insights into those patterns and structural links that often remain invisible through conventional oversight. This makes pre-emptive intelligence — the empirical study of corruption networks before they cross legal boundaries — essential to designing anti-corruption frameworks that identify the structure at the root of the practice before attempting to legislate against it. With such AI-assisted and data-driven analysis, governments may be able to detect structural anomalies before they grow.
Data-driven tools for detection
Corruption is, in short, the abuse of entrusted power for private gain. But it can become so deep and entrenched that it becomes virtually impossible to combat, let alone eradicate, through the mere threat or application of punitive laws.
While certain laws and legal reforms could play a role, most examples of corruption come from exploiting existing laws and loopholes. Such is the case in the U.S. under President Donald Trump, where the line between politics and business is as murky as ever, and powerful lobbies can exert influence on governments across the world.
This is where pre-emptive intelligence and data-driven analysis come into play. As governments collect large volumes of digital information, AI-assisted tools can help process and visualize relationships that may otherwise remain hidden. One such method uses bipartite graphs to divide nodes — entities such as individuals and companies — into two distinct sets and connects them according to their respective, and often overlapping, relationships.
If nodes are connected to others in more than one way, they form what is known as a “multi-edge network.”
Source: Statistics How To (2023). "Bipartite Matching: Definition and Examples"
In corruption analysis, this helps separate who is involved from which nodes they are connected to, highlighting patterns of overlap that ordinary graphs or audits tend to miss.
- Are certain individuals repeatedly connected to different firms?
- Do multiple companies share the same legal representation or notaries?
- Are there clusters of people and companies that are more tightly connected than others?
These links often hide something deeper.
This method was used during Mexico’s Veracruz corruption scandal, between 2010 and 2016. Researchers Issa Luna-Pla and Jose R. Nicolas-Carlock analyzed hundreds of phantom (or fake) companies that had been falsely contracted by the government, embezzling up to $3 billion.
Corruption in the Arab World
The Arab world illustrates how corruption evolves beyond individual misconduct into full-bodied networks of political, economic, and social favor. Political access, employment, and contracting often rely on a networked system of loyalty and wasta (nepotism and personal connections) rather than merit.
A recent study delved into how Lebanon’s 2019 financial crisis was caused largely as a result of political patronage networks bleeding into financial institutions. Elites controlled credit, appointments, and oversight, using state institutions and resources as tools for political favoritism.
The same system is seen across the region, where wasta is heavily relied on as a mechanism of access while public trust in institutions declines. Mapping who benefits from whom and how resources move helps correct the failure in perception by revealing the structure behind the appearance of legality.
The country still lacks a comprehensive anti-corruption strategy and an independent oversight authority capable of dismantling patronage networks. As a result, corruption remains protected by informal practices and opaque relationships that traditional legal measures fail to detect or weaken.
The digitalization of the Arab world
As governments digitize records of contracts, signatures, and resource allocations, AI-assisted and data-driven tools can help them gain the ability to visualize and transform informal relationships into analyzable data.
These methods can also weaken the networks that sustain corruption by allowing civil society and journalists to scrutinize state activity with more transparency and increase pressure for reform.
Corruption endures because we remain unprepared to recognize its structure.
And the promise of these approaches depends on digital infrastructure. In Lebanon, where administrative systems and public records remain largely undigitized and dependent on paper-based processes, converting informal relationships into structured and analyzable data remains a challenge.
But in the wider spectrum, if corruption leaves such visible traces, why wait for a scandal to start connecting them? Most Arab governments now possess the data and technology necessary to expose anomalies and early signs of collusion.
Prevention must begin with pattern recognition and pre-emptive Intelligence, not post-crisis reform. The fight against corruption will advance only when we learn to see the system that sustains it.