“OSINT has emerged as both a goldmine for launderers and a battleground for regulators.”
It has also burdened financial institutions to invest more resources in AML compliance programs, and it could be understood through the UNODS findings that the banking sectors in Great British spend nearly 5 billion pounds every year to combat money laundering and other financial crimes.
Are the results despite investing billions of pounds every year satisfactory? The numbers are not acceptable and are still at less than 1%.
What’s the solution, then? OSINT industry’s digital footprint has emerged as a significant source of information that reveals the connection between entities and criminals.
Therefore, using the OSINT techniques, financial institutions can reduce the chances of money laundering and increase the number of detection processes.
The following writing will discuss why Open source intelligence is a hot topic in the fight against money laundering.
Understanding the OSINT Industries Footprint
Is it possible for a commoner to be unknown to the world?
Since the OSINT has evolved as the primary source of information gathering, fortunately or unfortunately, even a teenager can’t hide his identity and personal information.
Why is it? The emergence of social media as a part of our lives, sharing pictures with friends, making connections, and showing the world our achievements and activities aren’t just limited to your timeline. The data is available to many agencies, regulatory bodies, and companies.
The publicly available data of any person over the internet is widely known as the OSINT industry’s Footprint. This also includes publications, news about a person, sanctioned lists of people, etc.
Why is so much debate on the OSINT framework in fighting against money laundering?
- Social Network Analysis for Relationship Mapping
The financial crimes are not committed by individuals in most of the cases. Therefore, finding the connections and relationships between the individuals is necessary.
Implementing an advanced social network analysis tool integrated with OSINT can map this relationship and its connections.
By analyzing the data from different social media platforms publicly available records, the OSINT can successfully identify the network that could indicate money laundering activities.
This approach can uncover hidden connections between individuals and organizations involved in illicit activities.
How could this be beneficial in AML compliance efforts? Well, the deeper insights and facilitaQuicklyEasily identify the intricate web of relationships often used to obscure their activities.
2. Geospatial Intelligence for Transaction Analysis
What could be more beneficial than tracking the suspicious transaction using individual imagery movements? You can do this using geospatial intelligence, which helps analyze transaction patterns and identify anomalies.
Companies must integrate the OSINT data with Geospatial intelligence, such as clients’ social media posts and publicly available data.
This method could be very beneficial in revealing geographic hotspots of money laundering activity, tracking the movement of illicit funds across different regions, and highlighting areas where regulatory scrutiny should be increased.
3. Dark Web Monitoring for Illicit Activity Detection
Another way OSINT could be beneficial in fighting against money laundering is by developing a unique OSINT tool to monitor the dark web marketplaces and forums where money laundering services are often advertised and discussed.
OSINT is powerful in analyzing transactions, communications, and user interactions on the dark web, which will ultimately help gain insights into emerging money laundering tactics and trends.
Do you know what financial institutions could do with this approach? They could stay ahead of criminals and adapt their AML strategies accordingly.
It provides early warning signs of new methods and schemes being developed by money launderers.
4. Predictive Behavioral Analytics
How does it seem if an organization successfully finds out what the people will do next? Does it seem filmy? It does?
However, it is possible to forecast potential money laundering activities with OSINT predictive behavioral analytics.
This works by Analyzing publicly available data on individual behaviors then checking people’s spending patterns, social interactions, and online activities to predict and prevent money laundering before it occurs.
5. Crowdsourced Intelligence Platforms
This is quite a complicated and long-term plan. Still, it is very beneficial to create an online platform for crowdsourced intelligence where all AML professionals, industry experts, and even the general public can contribute to sharing knowledge and insights on money laundering activities.
This collective intelligence can be analyzed to identify patterns and suspicious activities.
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