Digital Shield: How AI Protects the Financial Sector from New Forms of Cyber Fraud

Since March 13, amendments to the Administrative Code of Kazakhstan have come into force, tightening penalties for violations in the field of personal data protection and the use of electronic digital signatures (EDS). Fines for the illegal collection and processing of data have tripled, and both parties are now held accountable for the use of someone else’s EDS—the one who transferred the key and the one who used it. However, legislative measures are only part of the solution for enhancing personal data protection and combating fraud. Modern cybercriminal schemes require equally sophisticated technological defense methods. Leading cybersecurity experts are working in this direction, and their solutions are already being implemented in the country’s largest financial institutions. Abilaykhan Azamatov, a chief expert in language models, has developed a range of protective solutions for the financial sector. The anti-fraud system he developed continues to ensure the security of corporate operations in one of the country’s major banks, while the protection system for one of the largest crypto exchanges, Latoken, repels attacks from automated bots. How exactly AI counters new types of fraud, which threats are considered the most dangerous today, and what the financial sector can expect in the future—we explore these details with Abilaykhan Azamatov.

What threats exist today

Modern scammers constantly refine their methods, exploiting vulnerabilities in digital systems and human factors. Financial organizations in Kazakhstan face new types of cybercrimes every year, which require fundamentally different approaches to protection, says Abilaykhan Azamatov. Among the most dangerous attack vectors for banks and clients, he highlights three:

1. Credit fraud

Criminals mass-use forged documents or leaked data to obtain loans. Hacked databases and deepfake technologies make such attacks particularly sophisticated—traditional verification methods often fail to cope.

2. Transaction attacks

From suspicious operations using stolen bank cards or internet banking logins to complex schemes involving session hijacking. A new trend is the manipulation of transfer details in real time, where the victim themselves confirms the operation with altered data.

3. Arbitrage bot attacks

Automated systems artificially create imbalances on exchanges, profiting from price differences. This is not just an unfair practice—such attacks can paralyze trading platforms, as happened with several crypto exchanges in 2023.

Abilaykhan Azamatov, machine learning expert and developer of fraud prevention systems

These threats cannot be neutralized through manual checks. The speed and scale of attacks require equally technological solutions.

He develops exactly such systems, combining machine learning methods with behavioral analysis.

How artificial intelligence protects our money

Modern fraudulent schemes have become so complex that detecting them by human effort alone is no longer possible. Intelligent systems that can analyze millions of operations in real time and identify even the most cunning attacks come to the rescue. It is precisely such solutions that Abilaykhan Azamatov creates.

In a bank, an anti-fraud system for legal entities, created under his leadership based on an ensemble of machine learning algorithms, reduced the level of fraud by 25%.

Abilaykhan Azamatov, machine learning expert and developer of fraud prevention systems

This is like hundreds of experienced analysts working simultaneously. The system evaluates each client’s operation against multiple parameters: from data entry speed to device geolocation. Operations deviating from the client’s usual behavior are flagged as suspicious.

Artificial intelligence in such solutions allows for the immediate detection of suspicious operations within the entire flow of transactions and “alerts” about potential fraud. Without this tool, financial organizations can no longer ensure the security of their clients, says Azamatov. Thanks to his developments and approach to creating intelligent models, the bank can not only record instances of fraud after the fact but prevent them at the very moment the transaction is made.

How trading bots are detected on exchanges

Trading exchanges have also faced a new threat in recent years—armies of automated bots distort market conditions. These programs can execute hundreds of operations per minute, creating false market activity, manipulating exchange rates, and overloading exchange systems. The damage from their interference is significant for both the system and users: the platform operates unstably, private traders lose money due to volatility and manipulation, and trust in the trading platform declines. Traditional protection systems often fail to cope, so Azamatov proposed a different approach. Instead of pattern matching, it analyzes behavioral anomalies: request speed, repetition of actions, suspicious IP addresses, and technical markers. Azamatov’s solution is used by the crypto exchange Latoken. The result—a 50% reduction in the number of active bots and a 30% increase in the overall security and stability of the exchange.

Abilaykhan Azamatov, machine learning expert and developer of fraud prevention systems

Modern bots have learned to disguise themselves as humans in individual trades, but they reveal themselves through algorithmic repetition of actions. The task of protective systems is to detect these anomalies.

Prospects for using AI in the financial industry

Abilaykhan Azamatov has previously consulted development teams and audited existing AI solutions, helping companies build more reliable processes at the intersection of machine learning, computer vision, and cybersecurity. Among the areas he has worked on are document recognition systems capable of identifying not only textual information but also signs of forgery or interference with the document structure.

He is convinced that the future of data protection in the financial sector lies in comprehensive platforms that combine behavioral analysis, verification of digital footprints, and automatic data matching from various sources.

Abilaykhan Azamatov, machine learning expert and developer of fraud prevention systems

Modern protection should work like an immune system—anticipating threats rather than fighting their consequences. This should become the security standard in the banking and financial industry.

Experience in Kazakhstan, where the implementation of intelligent anti-fraud solutions is already yielding tangible results, shows that the future of financial security lies in adaptive systems capable of learning from new threats. And the key role in this is played not only by technological development but also by the training of experts capable of applying them effectively.

Author: Ekateria Buchnikova