How Osint supports financial criminal investigations

In this help of the net security interview, Stuart Clarke, CEO of Blackdot Solutions, discusses the strategic use of open source intelligence (Osing) in combating financial crime. He outlines his application in areas such as fraud, sanction ID and money laundering and deals with the legal, ethical and operational challenges. Clarke also provides case studies that illustrate how Osint was used to uncover criminal networks.

Are there specific typologies for financial crime such as fraud, money laundering or failure of sanctions in which Osint has proven to be particularly valuable? Can you give examples?

There are numerous typologies for financial crime in which open source intelligence agencies have shown a significant value. However, it is important to emphasize that we can identify certain core typologies – such as fraud, sanction circumference and money laundering – must remain fluid. The financial crime itself is naturally adaptive, and so the techniques used for examination must also be.

For example, take sanctions. A particularly effective application of Osint in this domain includes the combination of sanction lists with maritime ship enamel data. This merger enables investigators to identify ships that may operate suspicious routing behavior, e.g. This practice can help uncover networks, deliberately lead the programs through jurisdiction of third -party providers in order to avoid sanctions and to avoid discovery.

Criminal actors often set up front companies to mask the actual property and control of companies that are used to move illegal goods or means. Corporate registers, which are publicly accessible in many jurisdiction, can uncover layers of ownership and reveal shell structures that are intended to cover the connections to criminals or sanctioned units.

Then there are top-class data leaks such as the Panama papers and paradise paper-only two examples of the leaks that are documented and shared by the ICIJ. These were not originally leaked through OSint per se, but they received the investigation value if they were enriched with open source data. The investigators overlaid through names and company data with public registers, sanction databases and corporate network analysis, with global networks of tax evasion and illegal closures being uncovered by individuals and companies. It is an example of how Osint itself cannot improve and contextualize public data in order to create a comprehensive image of misconduct.

What are the ethical and legal limits that investigators have to navigate if you use OSINT to recognize financial crime?

Legal and ethical considerations are of central importance for responsibility, especially in the event of an investigation into financial crime. While legal framework conditions vary between the jurisdiction, there are certain joint threads. The main data protection is data protection: collecting and storing personally identifiable information in masses is largely unacceptable and often illegal, especially according to regulations such as the GDPR in Europe.

From an ethical point of view, we work under a clear sentence of principles and encourage our customers to do the same. First, we only work with public data. This can include freely accessible information or data sets available in stores, but must be legally and ethically related. Second, all examinations should be conducted. We advise you to rely exclusively on automated bots or AI agents in order to carry out examinations, since the human judgment is of crucial importance for avoiding distortions and misinterpretations.

Transparency is another basic principle. Investigators must maintain a complete examination track of their work so that their results are justifiable and reproducible. This is of essential importance for both the legal examination and the ethical accountability obligation.

After all, proportionality is important. Just because large amounts of data are available does not mean that it is appropriate to collect everything. The examinations should be narrow and targeted. Our goal is always to ensure that OSINT is used forever.

What are the biggest challenges that you integrate in the integration of OSINT in investigations into financial crime: technologically, legal or human?

Technologically speaking, one of the main barriers can be accessed in a safe and defensive way in open source data. Investigators have to avoid tipping suspects or accidentally to potential evidence. However, open source data is often fragmented and via company register, publicly available social media, news archives, the dark web and much more. It is not stored centrally and is largely unstructured. The investigators need platforms that consolidate this data in a cohesive, searchable format and at the same time maintain operational security.

Human factors are both a current and a future problem. There is still a limited understanding in many areas of the true value of Osint. In addition, with increasing volume and complexity of the data, researchers are increasingly associated with the right mix of analytical and technical skills.
With regard to the future, the technology will be of essential importance to bridge this gap. However, it has to add human insights, not replace.

The problem is not necessarily the existence of barriers, but the lack of clear, consistent leadership. In some regions, such as in the USA, we have seen progress in the introduction of official strategies for the state -owned Osing strategies. It is important that similar instructions are introduced in other nations.

At a more detailed level, the supervisory authorities indicate when using OSINT in financial institutions that Osint is an important instrument, but have not yet prescribed them. However, Osint is of essential importance for the support of financial institutions in order to make a complete risk, especially in more complex studies. We hope to see regulatory shift and specific guidance in the near future.

Can you lead us through a reduced example or a case study in which Osint played a role in uncovering or proof of a financial crime?

An outstanding example included what turned out to be a Ponzi program, which focused on an allegedly revolutionary financial technology. It quickly gained traction and attracted considerable investments, which were based on magnificent promises.

However, skeptics looked more closely. They noticed red flags through basic OSINT techniques – including the examination of the domain registration information on the website and historical snapshots. The website infrastructure did not meet the company's technological requirements, and the website had frequent changes in content.

The investigators dug deeper, examined company register and identified the people involved. What turned out to be a network of offshore -shell companies in which overlapping employees connected through public records and social media. Finally, it was uncovered as a fraudulent operation. The mastermind was put on an international list.

I am more personally involved in investigations for wild animal trade. In one case, a network of people with several shipping and logistics companies was connected and connected to human and wild animals via registration data and trade documents. It was a lively example of how different open source data can work together for a large discovery.

Did you come across situations in which Osint misled? Which safety precautions or best practices prevent this?

It is rarely the Osint itself that is misleading, so it is used. The data volume on the Internet makes it easier to be overwhelmed or to draw false conclusions without strict methodology.

The main protection is the investigator. An experienced investigator knows that he interviewed assumptions, checked sources and follow structured methods. You have to remain skeptical and check each information from several perspectives. Training and compliance with investigative discipline are crucial.

Interestingly, data can sometimes “mislead” in a useful direction. A good investigator can start with a hypothesis, but uncover a related or even more problem with unexpected links and patterns. That is the performance (and the challenge) of networked data.

With the advent of AI and large voice models, this challenge will only grow. AI can accelerate examinations, but also inject a distortion if it remains deactivated. Investigators have to stay in the leadership and use AI as an instrument rather than as a decision -maker. Regulation and clear guidelines will be of crucial importance here, as will the training and development of industry -wide Best Practices.

Ultimately, the success comes on the balance: the use of technology in order to increase human knowledge and at the same time maintain control, ethics and accountability during the entire investigation process.