Richard Golian

1995-born. Charles University alum. Head of Performance at Mixit. 10+ years in marketing and data.

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Richard Golian

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Open Source Intelligence and AI Crime Detection

How AI supercharges OSINT, digital forensics, and anti-money laundering investigations
Richard Golian
Richard Golian · 3 092 reads
Hi, I am Richard. On this blog, I share thoughts, personal stories, findings and what I am working on. I hope this article brings you some value.

Today, I realised how dramatically AI can help in investigating criminal activities through the analysis of publicly available information. When I think about it, investigators, journalists, and state authorities have always relied on their ability to connect information, recognise patterns, and uncover hidden links. But until recently, this was purely human work, slow, exhausting, and dependent on a person’s ability to notice details and combine facts. AI can speed up this process and take it to an entirely new level.

OSINT: Turning Public Data Into Investigative Intelligence

Beneficial Ownership and Corporate Network Analysis

Take business registries, for example. Many countries now have them publicly accessible, and we can search them manually. But what if AI could automatically scan these registries, compare ownership structures, and detect suspicious patterns? What if it could identify that a certain company has an unusually high volume of transactions with another, which is linked to a politically exposed person? Or track how company ownership changes shortly before securing a major government contract? This is already possible today, yet it is not being fully utilised.

Detecting Public Procurement Fraud

The same applies to public procurement. If the same companies repeatedly bid for tenders, if suspiciously similar pricing offers appear, or if an unknown company with minimal history unexpectedly wins, AI could quickly flag these irregularities. People might notice such patterns by chance, but a system that is continuously running and analysing all available data could detect them instantly.

Asset Monitoring and Politically Exposed Persons

Another fascinating use case is AI's ability to monitor the wealth of politicians and business figures. Today, asset declarations exist, but few systematically compare them with reality. What if AI could automatically pull data from land registries, corporate financial statements, and asset disclosures to identify discrepancies? For example, when someone with an officially low income owns luxury real estate or when expensive properties are purchased through a network of obscure companies.

Social Media OSINT and Link Analysis

Then there are social networks. Public posts, photos, location tags. All these are small puzzle pieces that can be assembled into a picture that someone never intended to reveal. AI could identify that two individuals who claim not to know each other actually spent a vacation together. Or that a politician who insists they have never met a particular business owner was in fact present at their private celebration. It is both terrifying and fascinating how much can be reconstructed from publicly available data.

Blockchain Forensics and Anti-Money Laundering

It is not just about politics and corruption. AI can be extremely useful in investigating tax fraud as well. Large-scale money laundering schemes often involve funds moving through dozens of companies in different countries. A human analyst might spend months mapping out these transactions, but AI could accomplish this within minutes. It could track money flows, analyse blockchain transactions, and detect patterns indicative of money laundering.

Mass Surveillance, Deepfakes, and the Collapse of Proof

I also reflect on what impact this could have on society. On one hand, there is enormous potential for uncovering fraud and criminal activity. On the other, there is the issue of privacy and the misuse of power.

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Summary

AI can scan corporate registries, monitor public procurement, cross-reference asset declarations, and track money flows, in minutes, not months. The potential for exposing corruption is tremendous. So is the risk of misuse.

Common questions on this article's topic

How can AI help investigate corruption and financial crime?
AI can automatically scan corporate registries, compare ownership structures, and detect suspicious patterns, such as unusually high transaction volumes between related companies or ownership changes shortly before government contracts. Ownership-mapping work that can take human analysts many hours can be reduced to minutes or seconds by AI, dramatically compressing analysis time (exact ratios vary with the complexity of the structure). In the article, this capability is described as already possible today but not yet being fully utilised.
Can AI detect irregularities in public procurement?
Yes. Machine learning algorithms can monitor bidding patterns in real time, flagging situations where the same companies repeatedly win tenders, where suspiciously similar pricing appears, or where unknown companies with minimal history unexpectedly win. In the article, this is contrasted with human detection: people might notice such patterns by chance, but a continuously running AI system could detect them instantly across all available data.
What is OSINT and how does AI enhance it?
OSINT, Open Source Intelligence, is the practice of gathering actionable information from publicly available sources. AI dramatically enhances OSINT by automating the analysis of social media posts, photos, location tags, corporate filings, and financial records at a scale impossible for human analysts. In the article, the example is given of AI identifying that two individuals who claim not to know each other actually spent a vacation together, reconstructed entirely from public data.
Can AI track money laundering across multiple countries?
AI-powered anti-money laundering systems can trace funds moving through dozens of companies in different countries, analyse blockchain transactions, and detect patterns indicative of laundering, dramatically faster than human analysts. In the article, this is presented as one of the most powerful applications: schemes that would take a human investigator months to map could be processed by AI in a fraction of the time.
What are the risks of AI-powered investigation tools?
In the article, the dual nature of these tools is a central concern. The same technology that can expose corruption could be used for surveillance, intimidation, or blackmail if it falls into the wrong hands. The ACLU has warned that AI is super-charging machine surveillance. Anyone with the right knowledge could potentially build their own intelligence agency. Combined with generative AI that can fabricate convincing evidence, this creates a world where the very concept of proof may start to collapse.
Could AI-generated fake evidence undermine investigations?
Yes. In the article, this is identified as a fundamental problem. Generative AI can produce convincing text, images, videos, and even fabricated documents. When anything can be convincingly created, distinguishing real evidence from fabrication becomes increasingly difficult. This does not just threaten individual investigations. It threatens the entire framework of trust on which legal and democratic systems depend.
What is digital forensics and how does AI use it?
Digital forensics is the practice of recovering, analysing, and interpreting digital evidence, from financial records and blockchain transactions to files and metadata, in a way that can support an investigation. AI accelerates digital forensics by tracing money flows across dozens of companies and countries, analysing blockchain transactions, and surfacing patterns that a human analyst might take months to map. In the article, this is described as one of the most powerful applications of AI in exposing financial crime.
What is AI crime detection?
AI crime detection is the use of machine learning to identify criminal or fraudulent activity by analysing large volumes of public and transactional data, corporate registries, procurement records, asset declarations, social media, and financial flows, and flagging suspicious patterns automatically. In the article, the argument is that a system running continuously can notice irregularities that a human would only catch by chance, detecting them instantly across all available data.
Who uses AI-powered open source intelligence in real investigations?
Open source intelligence is used by journalists, state authorities, and independent investigators. The best known practitioner is Bellingcat, the investigative organisation founded by Eliot Higgins in 2014, which pioneered the use of publicly available data, satellite imagery, social media posts, and geolocation, to expose war crimes and corruption. Investigative teams of this kind are increasingly adopting AI to collect and analyse data at a scale no human team could manage, which is exactly the shift this article explores.
Richard Golian

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