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National Bank of Kazakhstan launches in-house AI platform and GPU cluster for supervision and analytics

The central bank deployed proprietary AI infrastructure to monitor the financial system.

In brief
  1. The regulator moved data processing for AI models to its own GPU servers.
  2. Over the past year, the bank launched 47 digitalization projects, including a "Data Factory" that sped up analytics by 80%.
  3. The local infrastructure supports independent risk monitoring and the digital tenge ecosystem.
National Bank of Kazakhstan launches in-house AI platform and GPU cluster for supervision and analytics

Фото: Brett Sayles / Pexels

The National Bank of Kazakhstan deployed a local GPU cluster and an in-house AI platform for supervision.

Kazakhstan and digital sovereignty

For the Kazakh financial market, the regulator’s move marks a shift toward hardware digital sovereignty. Processing transactions, monitoring the banking sector, and detecting anomalies require massive computing power. Hosting such sensitive data in public foreign clouds carries risks. A local cluster solves this problem.

It also provides the technological foundation for future phases of the national digital currency rollout. Finteqstan previously reported that the digital tenge will gain legal tender status in Kazakhstan. To manage its circulation and programmable payments, the regulator needs independent and scalable capacity.

Why it matters

Central banks are gradually adopting the infrastructure approaches of major tech companies. The National Bank of Kazakhstan shows a willingness to invest in hardware, not just in drafting regulations. Having its own GPUs allows the bank to train anti-money laundering (AML/KYC) models faster and implement suptech tools—technologies that automate commercial bank supervision.

The regulator’s shift to local AI models sets a technical security standard for the entire Central Asian banking sector.

What comes next

In the next phase, the National Bank will likely use the AI platform for direct, real-time transaction supervision, rather than just internal routine tasks. Commercial banks will have to adapt as the regulator uses machine learning to analyze their reporting and detect discrepancies much faster than before.

Sources