The Complete Guide to Using AI in the Financial Services Industry in Kazakhstan in 2025

Kazakhstan's 2024–2029 AI roadmap and a flurry of national projects make 2025 a turning point for financial services: with a national AI strategy underway and local models like AlemLLM plus a new supercomputer cluster, banks and fintechs can deploy fraud detection, eKYC and real‑time credit decisions at scale.

High digital uptake - cashless payments at 86% and mobile banking used by over 80% of Kazakhs - means AI-powered workflows can speed onboarding (opening an account now takes under three minutes) and cut operating costs across payments and AML. Leading institutions are already moving: Halyk Bank uses AI for KYC, AML and RPA efficiencies, while Open Banking and Open API pilots are creating secure data channels for innovation.

For practical context on the national push and local AI labs, see the Kazakhstan national AI coverage on Astana Times coverage of Kazakhstan national AI roadmap and Halyk Bank's AI use cases in Global CIO article on Halyk Bank AI use cases.

“Advanced technologies such as artificial intelligence, blockchain, biometrics, mobile payments, and QR codes are being widely adopted in the country. Traditional banks are working closely with fintech startups to build ecosystems that integrate financial and non-financial services,” said Danilina.

The AI industry outlook for Kazakhstan in 2025 is bullish but pragmatic: national strategy and heavy infrastructure investments are turning policy into capacity, with the International Center for Artificial Intelligence (Alem.AI), the release of AlemLLM and a new supercomputer cluster creating the compute and language backbone local fintechs need to move from pilots to production.

Policy targets - including a government plan to increase the number of AI-based products fivefold by 2029 and ambitious skills targets that aim to train millions - are paired with concrete tools such as a national AI platform, expanded data centres and Open API workstreams that make integration in banking, payments and AML systems technically feasible.

The supercomputer's regional leadership (peaking at about two exaflops) means local teams can train large language models and run heavy fraud‑detection and simulation jobs without sending sensitive data abroad, while ecosystem moves - from Astana Hub startups to export and venture-fund initiatives - signal growing market depth and potential for cross-border fintech services.

For a full rundown of the national build-out and priorities see Astana Times' coverage of Alem.AI and AlemLLM, the government Concept for AI development, and the Global CIO analysis of Kazakhstan's 2025 IT trends.

“Kazakhstan's new national supercomputer can perform in just one second as many calculations as the entire world's population – all eight billion people – could do in over four days if each person solved one equation every second,” said Minister Zhaslan Madiyev.

Global leaders set the baseline for where AI is used most: the United States still leads by a wide margin in model production and private investment, while China sits firmly in second as it narrows the performance gap - facts underscored in the Stanford HAI 2025 AI Index report (Stanford HAI 2025 AI Index report) and country rankings - so Kazakhstan's ambition should be judged against that concentrated scale rather than discouraged by it.

Investment patterns matter: U.S. private AI funding in 2024 dwarfed others (about $109.1 billion versus roughly $9.3 billion for China in the same period), and that funding gap helps explain why many flagship models and cloud services originate outside Central Asia.

Still, the global picture also offers opportunity: several middle‑income countries are rapidly closing readiness gaps through targeted compute, governance and talent programs, and visual analyses of national AI investment show where capital and infrastructure cluster - helpful context for Kazakhstan's strategy (VisualCapitalist visualization of global AI investment by country).

For Kazakhstan's financial sector the

so what?

is practical: competing directly on model counts isn't necessary if local strengths - AlemLLM, a national supercomputer and fast mobile adoption - are used to build compliant, sovereign LLM-enabled fraud detection, eKYC and credit decisioning that keep sensitive data in-country while leveraging global best practices on governance and talent development.

For beginners, AI in Kazakhstan's financial services is best understood as a set of practical tools that already touch daily banking: conversational chatbots and voice assistants for 24/7 customer engagement, eKYC and automated onboarding that shrink account opening to minutes, real‑time fraud detection that feeds the NBK's shared Anti‑Fraud Center, and personalization engines that tailor product offers and cross‑sells in mobile apps.

The national push toward Open APIs and a “data factory” makes supervised AI (SupTech) and predictive monitoring realistic for regulators and banks alike, while commercial projects show multilingual, omnichannel chatbots handling millions of queries and tying into core banking systems.

These use cases are beginner‑friendly because they map to familiar tasks - answering questions, verifying identity, flagging anomalies, and recommending products - yet they unlock outsized value: the Anti‑Fraud Center alone helped block 1.5 billion fraudulent transactions and returned nearly 300 million tenge to victims.

For a practical intro to the policy and infrastructure that enable these deployments see Astana Times' piece on Kazakhstan's open banking rollout and NBK plans, and read the local study on generative voice chatbots' effects on customer satisfaction for evidence on how users respond to conversational AI in Kazakhstani banks.

“Most financial institutions currently deploy AI for front-facing functions, such as customer engagement and communications. Expectations are high that the application will shift to more complex uses.”

Regulation in Kazakhstan in 2025 is shifting from concept to concrete rules, and that matters for every bank and fintech that wants to deploy AI at scale: the Mazhilis approved the draft Law “On Artificial Intelligence” in its first reading on May 14, 2025, signaling a move toward a standalone, human‑centred legal framework that borrows the EU's risk‑based playbook while tailoring rules for local needs.

The draft emphasises legality, fairness, transparency, explainability and accountability, proposes a tiered risk classification (high/medium/low) that would impose strict oversight on systems affecting life, health or public administration, and tightens data protection with explicit consent requirements for biometric data plus bans on unauthorised collection - measures that could even introduce criminal liability for mass automated personal‑data processing.

At a practical level this means financial players must plan for data‑sensitive model training on the National AI Platform, stronger human oversight, clearer vendor contracts and faster internal governance to meet new liability and transparency tests; the draft's 28 articles (modest compared with the EU Act's 113) are a start, but compliance teams should treat the law as a live roadmap rather than a finished blueprint.

Astana Times coverage of the draft law and Chambers overview of the proposed provisions.

“The bill reflects major global trends in AI regulation. Many countries have adopted systematic approaches to AI governance. The EU's AI Act, adopted in 2024, serves as the world's first risk‑based AI legislation and is already a model for countries like Kazakhstan,” said Shoplan Saimova.

Infrastructure in 2025 gives Kazakh fintechs a practical path from pilot to production: the national stack pairs a Central‑Asia‑leading supercomputer cluster (alem.cloud) with a National AI Platform and the QazTech sovereign platform to keep sensitive model training and data local, while the International Center Alem.AI and Astana's startup ecosystem open those resources to universities and scale‑ups - meaning fraud models, eKYC pipelines and LLM assistants can be trained domestically instead of routed abroad.

Coordination comes from a newly created Digital Transformation Group that is building a Samruk‑Kazyna roadmap to embed AI into production and public services, and ministers are fast‑tracking a single digital architecture to reduce fragmentation and expand secure access for fintechs.

Risks remain real - cybersecurity gaps and scarce specialist talent - so pragmatic deployments should prioritise on‑premise compute (alem.cloud), governed access via QazTech, and staged audits under the national AI platform to meet emerging rules.

For the government's coordination plan see Astana Times' coverage of the digital headquarters and the plan to stand up a dedicated AI ministry and state supercluster in the coming months.

"It is not only about improving the legal framework for the functioning of AI. It is necessary to address matters of data fragmentation, the lack of clear regulations for the distribution of supercomputer capacity, cybersecurity, and the complete transition to the QazTech platform. All these tasks are of strategic importance for the country's digital transformation and must be completed by December of this year with a visible economic effect," said Prime Minister Olzhas Bektenov.

Security, data protection and financial‑crime controls are now front‑and‑centre for Kazakh banks and fintechs: Kazakhstan logged roughly 30,000 information‑security incidents in early 2025 - botnet activity jumped to 17,600 and phishing rose 37.2% - a local surge that meets a global wave of automated, credential‑fuelled attacks described by Fortinet's 2025 threat review, which warns of 36,000 malicious scans per second and a shift toward industrialised exploits (Times of Central Asia: Cyberattacks in Kazakhstan - early 2025 report, Fortinet 2025 Global Threat Landscape Report).

The financial‑services lens matters: the 2025 Thales Data Threat Report - Financial Services Edition highlights that 59% of firms rank fast‑moving AI as a top concern, only 15% have encrypted most sensitive cloud data, and secrets management and API sprawl remain critical gaps, so practical controls must prioritise stronger encryption, key management, secrets hygiene and GenAI‑specific security tooling (Thales 2025 Data Threat Report - Financial Services Edition).

The “so what?” is blunt: staying compliant and keeping customer data sovereign will require moving from reactive incident response to proactive exposure management, tighter secrets controls, and partner‑grade managed services to shore up resilience against rapid, automated attacks.

“It's encouraging that three out of five organizations are already prototyping new ciphers, but deployment timelines are tight and falling behind could leave critical data exposed.” - Todd Moore, Thales

Kazakhstan's fintech rise is less theory and more a fast-moving marketplace: Dealroom now lists over 390 Kazakh startups (with 200+ investment rounds) and Astana Hub sits at the ecosystem's center, hosting roughly 1,500–1,700 resident companies and international acceleration links that push founders into Silicon Valley and beyond.

Robust public programs and venture vehicles - from Astana Hub Ventures (aiming to seed a unicorn by 2027) to Tech Orda and AI Qyzmet - have already translated into real capital and customers: Astana Hub residents have attracted hundreds of millions in funding, generated billions in revenue and created tens of thousands of jobs, while fintech alone has quadrupled and now accounts for around 40% of venture investment.

That combination of scale, state-backed infrastructure and export ambition means fintech innovators can prototype eKYC, tokenization or LLM-enabled fraud tools locally and scale them quickly; the memorable test is simple: when a technopark's startups collectively export nearly a billion dollars, regulators and banks start treating them as strategic partners, not hobby projects.

For detail on the hub and national push see Astana Hub Dealroom roundup and Startup Genome profile of Astana Hub.

“Astana Hub's mission is to elevate Kazakhstan's tech sector to a global stage, creating real opportunities for innovation and economic growth. We're not just fostering startups; we're establishing Kazakhstan as a bridge between Central Asia and the world, empowering local talent to compete internationally,” says Magzhan Madiyev, CEO of Astana Hub.

Building AI teams in Kazakhstan is now a systems play: government-backed pipelines feed classroom talent straight into fintechs, with Tech Orda subsidies (up to 500,000 KZT per student) and a nationwide showcase of 159 accredited courses and 79 schools that have already trained thousands and report strong placement rates - an individual grant can cover a six‑month bootcamp and turn a curious junior into a deployable ML operator in months (Astana Hub Tech Orda grant program details).

Parallel tracks widen the funnel: AI‑Sana and Astana Hub initiatives scale mass training, TUMO and Tomorrow School seed practical projects for teens, and public programs like AI Qyzmet train civil servants so banks can hire locally‑tested engineers rather than only importing talent; the national push even aims to attract foreign students and form international partnerships to accelerate deep‑tech teams (Astana Times coverage of Kazakhstan free AI training programs, AI‑Sana business acceleration programme overview).

The practical hiring playbook is simple: recruit from Tech Orda/Astana Hub cohorts, pair graduates with Samaruk or bank internships, and use accelerator ties with Stanford/Imperial to plug skill gaps - so teams grow faster than resumes, and a single cohort scholarship becomes the fastest route from classroom notebook to production fraud‑model in a live banking app.

Start small, stay sovereign, and scale with controls: begin by classifying planned systems against Kazakhstan's forthcoming risk tiers and data rules (map each use case to the draft law and Personal Data Protection limits - see a clear primer on the national rules at AI regulation in Kazakhstan: national rules and guidance), then move compute-sensitive training onto local infrastructure and the National AI Platform to keep customer data in‑country and align with the 2024–2029 concept for national AI build‑out (Kazakhstan Concept for the Development of Artificial Intelligence 2024–2029).

Step 1: run a governance and risk inventory (use the Protecht/LeanIX playbook to identify high‑risk models). Step 2: pilot one high‑value, low-complexity use case (eKYC or real‑time fraud alerts) with strong human‑in‑the‑loop controls and encrypted keys.

Step 3: harden security and secrets management before production (encrypt sensitive data, lock down APIs). Step 4: build a repeatable model lifecycle - versioning, audits and monitoring - and measure impact with clear KPIs.

Step 5: train staff and scale capacity using targeted courses (fast practical upskilling is available through programs like Nucamp's AI Essentials for Work bootcamp) so teams can operate models responsibly.

Taken together, these steps offer a practical, compliant roadmap: govern first, pilot locally, secure relentlessly, then scale.