Richard Golian

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

Castellano Français Slovenčina

Manage subscription Choose a plan

RSS
Newsletter
New articles to your inbox

Article

Open Source Intelligence and AI Crime Detection

How AI supercharges OSINT, digital forensics, and anti-money laundering investigations
Richard Golian
Richard Golian · 2 734 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’s both terrifying and fascinating how much can be reconstructed from publicly available data.

Blockchain Forensics and Anti-Money Laundering

It’s 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.

Continue

Join the Library

Full access to my thoughts, personal stories, findings, and what I learn from the people I meet.

Join the Library · €29.99 per year
Read only this one · €2,99

Get the full article by email and feel free to reply if you want to discuss it further.

Visa Mastercard Apple Pay Google Pay

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. Processing ownership structures that take human analysts 40 hours can be completed by AI in under 60 seconds. 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

If you have any thoughts, questions, or feedback, feel free to drop me a message at mail@richardgolian.com.

NEWSLETTER
What I write about, what I am working on, what I learned.
Sent the first Sunday of the month. Unsubscribe anytime.

Related articles

Autonomous Weapons and Military AI: How AI Warfare Threatens Our Security

You might say I am being too pessimistic, that I am fearmongering. Fear is useful.

15 March 2025·2 960 reads
AI, Wealth Inequality, and the Singularity We Cannot Predict

No matter how I look at the future, I see very few answers and far too many questions and problems.

25 February 2025·3 151 reads
AI, Algorithmic Trading, and Systemic Risk to Financial Stability

It is real, growing, and potentially devastating.

21 February 2025·2 340 reads

More articles

I Ran Object Detection on My Laptop, and Saw Everything Is Possible

A few weeks ago I installed a small local AI model on my laptop that watches a live camera feed. I turned the webcam on in the dark, and in near total darkness it recognised me and the objects in the room. That such things exist, I have known for a long time. What opened my eyes was the accessibility. I installed it in one prompt, free, and it runs entirely on my machine, sending data nowhere.

15 July 2026·62 reads
Dependent on AI: Are We Still Masters, or Slaves?

I have Heidegger and my notebook beside me. I am asking where all of this is heading, where artificial intelligence is taking us.

21 June 2026·542 reads
Which Work Will AI Not Replace?

Seventy per cent. That is where the first AI output begins, even when you give it the full company context and the best examples from the past. We are talking about the kind of output that cannot be defined programmatically. It is more complex. Often it is creative work. On one repeated type of output I reached eighty per cent within a week. Every further percentage point is harder than the one before.

10 June 2026·516 reads
What is the dead internet theory? Will we return offline?

For a long time we treated the internet as the main road. The place where work and relationships happen. Yet most of what we see on it today is, or soon will be, AI-generated: text, images, profiles and comments. The internet is turning into an online game full of bots, where you cannot be sure that a human is on the other side of anything. So I ask: was the online world the main road, or only a temporary detour that part of us will return from, back offline?

7 June 2026·657 reads
The Gap Between Professionals in the AI Era

A few days ago I interviewed a senior marketer. An experienced man, years of practice. I asked him about AI. He said he barely uses it. He had one bad experience with the output and decided he was too senior for it to add value when it is not perfect. I know the other side too: professionals who automate everything that can be automated.

6 June 2026·616 reads
Europe Is Not Ready for Drone Warfare

Europe does not have the capacity to face a full-scale, mass drone war of the kind we see in Ukraine. Three dependencies weaken it: China supplies the physical material for defence systems, the United States supplies capabilities Europe does not have, and twenty-seven states cannot agree how fast, or who pays. Rearmament plans exist, but they are being carried out slowly.

31 May 2026·581 reads
Can AI Replace Human Judgement?

AI produces the graphic, the newsletter and the product page faster than a person. What is left for the one who used to do it is the judgement, knowing whether the output is good. But most people have worse judgement than AI. And whoever cannot judge quality cannot delegate either. How do you tell whether yours is the judgement a company relies on, or the kind it can replace?

30 May 2026·586 reads
What Determines a Stock Price?

In April, in the first part of this series, I wrote about an AI prediction system I had started building on my own machine. At the time the software was a few hours old and the prediction record was empty. The record since then has shown one thing: the system does not yet understand the market it is being asked to forecast. It can pull macro context, book value, earnings. But it cannot put those together into something that helps it understand the price.

23 May 2026·620 reads
Where the Money Goes When AI Takes the Work: Mapping the AI Economy

Prague, 13 May 2026. On my way to work I started thinking about something that stayed with me for days. If most routine work on a computer disappears in the next ten years, and a large share of repetitive manual work disappears with it, what happens to the flow of money? Who pays whom for what? Which economic layers will exist, how large will they be, and what relationships will run between them? This is the six-layer map I sketched as an answer.

15 May 2026·1 250 reads
Can AI Predict the Stock Market? Building a Calibrated System

I am building an AI system to predict the S&P 500. It runs on my own machine, uses free public data (yfinance, FRED, the Shiller dataset), and grades every forecast against reality. This series documents the build itself: the decisions, the methodology, the mistakes. What I will eventually share from the running system is a separate question, and an honest one.

26 April 2026·1 994 reads
All in on AI agents, or an analogue life.

Four days in Catalonia. No computer, no AI, almost no social media. I bought this notebook so that I could write down what I would think about, and what I would come across and learn on the trip.

10.5.2026·1 091 reads
NEWSLETTER
What I write about, what I am working on, what I learned.
Sent the first Sunday of the month. Unsubscribe anytime.