Detecting insider trading involving secret codes or encrypted apps is a major challenge for regulators. Since these messages are designed to stay hidden, authorities cannot simply read them like standard emails. Instead, they focus on advanced technological methods to identify suspicious patterns.
One key method is Natural Language Processing (NLP). This AI technology analyzes large amounts of data to find unusual shifts in how people communicate. Even if they use code words, AI can spot changes in language patterns that suggest something is being hidden. Another approach is forensic data reconstruction. This involves using specialized software to recover data from mobile devices or cloud backups, which can sometimes reveal fragments of encrypted conversations.
Regulators also use network traffic analysis. This does not involve reading the content of the messages, but rather looking at metadata. They look for sudden spikes in communication between specific individuals or unusual timing of data transfers. By combining these digital footprints with traditional market surveillance, authorities can build a stronger case. While encryption makes it harder to see the exact words, these sophisticated auditing tools help reveal the suspicious behavior surrounding the illegal activity.
