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Practical experience of using AI to recognize fraud in the stock,
futures, and currency markets
The article describes a case study of creating an automated anti-fraud system that includes approximately 150 algorithms for detecting suspicious scenarios. Artificial intelligence was used both to fine-tune these algorithms and to tackle other project tasks. What challenges did you encounter, and in which anti-fraud tasks did artificial intelligence demonstrate the greatest effectiveness?
Internal controls in a credit institution
Automation of monitoring of suspicious transactions during brokerage services for bank clients in financial markets.
Credit institutions worldwide—including those in Russia—are increasingly using specialized systems for automated pattern recognition to detect suspicious transactions in the stock, futures, and currency markets. Which bank divisions can [...]
CIS-AUTRETECH-2021
From the generally accepted perspective, an ‘incident’ is often viewed as merely a misunderstanding, an unpleasant occurrence, or even a ‘collision.’ How do you define an incident in your terminology?
In the terminology of our system’s user manual, an incident is defined as a client’s activity—a combination of orders and transactions linked to time, market data, and news background—that can be interpreted by a court […]
CIS-AUTRETECH-2019
What prompted the need for this product?
The primary driver is the global tightening of regulatory requirements to increase the transparency of banking operations. This isn’t mere rhetoric—these rules carry staggering penalties: over the past seven years, US and European banks have paid more than USD 320 billion in fines. Moreover, this trend shows no sign of abating. In response to these colossal penalties, 2015 saw the emergence of […]
