Top 5 Jobs in Financial Services That Are Most at Risk from AI in Kazakhstan - And How to Adapt

Kazakhstan's financial services sector is moving from talk to scale: banks are the most active adopters and the National Bank's survey finds more than 30% of market participants already using AI, while reporting shows cashless payments and mobile banking have surged - unlocking instant account opening, sub‑two‑minute loan decisions and a digital tenge pilot that together make routine branch and back‑office tasks prime targets for automation (National Bank of Kazakhstan AI adoption survey, Astana Times report on Kazakhstan fintech growth).

That speed is good for consumers and startups, but it raises urgent questions for workers: the path forward in KZ is less about resisting AI than learning to work with it - practical courses like Nucamp's Nucamp AI Essentials for Work bootcamp teach promptcraft and job‑based AI skills that help employees turn disruption into opportunity.

“Advanced technologies such as artificial intelligence, blockchain, biometrics, mobile payments, and QR codes are being widely adopted in the country.” - Danilina, Astana Times

Rankings were derived by combining global evidence on which tasks automation actually replaces with Kazakhstan's market signals: vendor case studies and best practices (PwC's work on automating analytics and breaking data bottlenecks, Ushur's taxonomy of IDA, conversational AI and agent ecosystems) were used to define the risk dimensions, and local indicators - digital payment growth and National Bank adoption signals cited earlier - checked where Kazakh roles land on that spectrum.

Four practical dimensions were scored for each occupation: task routineness and rule‑based work; document intensity (how much OCR/IDA would remove manual entry); customer‑facing automation exposure (likelihood of conversational AI or virtual agents handling the interaction); and compliance/regulatory complexity (how much human judgement remains).

Benchmarks from industry pilots and vendor reports (for example, vendor examples where month‑end close fell from weeks to days) guided thresholds for “high,” “medium,” and “low” risk, and Nucamp's local guides were used to map realistic upskilling paths for Kazakh workers and employers (PwC report: automating analytics in financial services, Ushur guide to financial services automation and IDA, Nucamp AI Essentials for Work syllabus).

“…over time, banks could have hundreds of AI agents at their disposal, each trained to complete a particular task and ready to be called on by other agents or humans.” - McKinsey (cited in Ushur)

Bank tellers and retail‑branch customer service staff in Kazakhstan sit squarely in the crosshairs of automation because most of their day is predictable, transaction‑heavy work that ATMs, mobile channels and conversational bots can already handle; the country's labor report warns that nearly 52% of future jobs are at high risk of automation, with “predictable and routine” banking roles singled out (Kazakhstan jobs automation report: 52% at high risk of automation).

Local practice shows how quickly this plays out: Otbasy Bank's RPA rollout automated account creation, daily reconciliations and even OCR‑driven document processing - robots can chew through 2,000 documents a day, a volume that once required about 13 people - freeing branch staff from manual entry but pushing them toward exceptions handling and digital customer education (Otbasy Bank RPA automation case study).

Research on bank workforces in Russia and Kazakhstan also finds that AI implementation, when managed well, can boost employee motivation and that younger workers tend to view these changes more positively (study on AI implementation and bank employee motivation (Russia & Kazakhstan)), so the practical imperative is clear: branches must train staff for advisory, complex problem‑solving and digital onboarding before routine tasks disappear.

“We are not reducing staff, but redistributing the workload, and employees have free time for other, more complex tasks.”

Back‑office functions - payments processing, reconciliations and trade settlement - are the plumbing of Kazakhstan's financial system and therefore prime targets for AI because they are high‑volume, rules‑driven and paper‑heavy; combining RPA, OCR/NLP and machine learning can lift straight‑through processing, cut errors and free teams for exception handling and risk analysis.

Practical pilots show the scale: a payments modernization project achieved roughly a 50% reduction in effort for cards and payments operations, illustrating how Zero‑Ops thinking turns manual workflows into mostly automated pipelines (payments back-office modernization and Zero Ops).

Platforms that blend RPA with AI accelerate invoice and reconciliation throughput, improve anomaly detection and build audit trails, while no‑code automation lowers the bar for finance teams to own change (back-office finance automation).

At the same time, AI improves fraud and dispute handling by surfacing suspicious patterns in real time and routing only true exceptions to analysts, shortening cycle times and protecting customers (AI for fraud, disputes and faster decisions), so the practical implication for Kazakh banks is clear: modernize the back‑office first, or risk bottlenecks that undercut front‑end innovation.

“Think of the back‑office as the operational heart of a payments operation.” - Joyce Mehlman, iLEX Consulting Group

Credit and underwriting teams in Kazakhstan face one of the clearest near‑term AI risks: algorithmic credit scoring can turn labour‑intensive file review into an automated decision engine that approves or rejects loans in minutes rather than days, reshaping who gets access to credit and how underwriters add value.

Research shows AI and big data improve predictive accuracy and expand data sources beyond bureau history - helping lenders spot creditworthiness from cashflow, mobile signals and invoices - but also raise governance, fairness and cyber risks that regulators must manage (Research: AI and Big Data in Credit Risk Assessment, CTO Magazine analysis of AI credit scoring and machine learning in lending).

Local market moves confirm the trend: ForteBank is explicitly investing in AI‑driven credit models and fraud detection as Kazakh banks modernize underwriting pipelines (ForteBank investing in AI-driven credit models and fraud detection in Kazakhstan).

The practical mandate for credit officers is clear: learn model oversight, interpretability and exception‑management now, because the routine scoring work will migrate to models - and the human role will increasingly be to defend fairness and explain decisions to customers and regulators.

Bookkeepers and junior accountants in Kazakhstan are on the front line of change because their jobs - data entry, invoice processing, reconciliations and routine report generation - map neatly onto what Generative AI and OCR/NLP already automate: quicker, more accurate financial reports, continuous anomaly detection and real‑time dashboards that turn piles of receipts into audit‑ready narratives.

Practical guides show GenAI easing month‑end work and producing standardized disclosures, while regional upskilling (document OCR + NLP pipelines) helps local teams validate models without exposing customer PII (Generative AI in accounting use cases - SoluLab, Document OCR and NLP pipelines for Kazakhstan).

Adoption is already accelerating - Thomson Reuters found GenAI use jumped to 21% in tax and accounting firms in 2025 - so the smartest path for junior staff is to learn tooling, data‑quality controls and exception management now, shifting toward advisory, interpretation and governance where human judgment still matters (Thomson Reuters analysis: How AI will affect accounting jobs).

“Current and emerging generations of GenAI tools could be transformative,” said one U.S. director of tax.

Compliance clerks who handle AML/KYC screening in Kazakhstan are facing a swift shift from manual lists and paper files to machine‑assisted triage: the country's improved Basel AML Index standing (111th in 2025) and a sharp rise in law‑enforcement activity - 77 criminal offences tied to legalization of proceeds in the first four months of 2025 - mean banks must detect risk faster and with fewer false positives (Basel AML Index Kazakhstan 2025 ranking).

Machine learning can cut alert noise and enable real‑time sanctions and PEP screening, but adoption is uneven - only about 28% of firms use AI for compliance today while nearly half plan to implement it by 2025 - and success depends on data quality, governance and human oversight rather than blind automation (AML compliance AI trends 2025 webinar by Alessa).

For compliance clerks the practical playbook is clear: learn model‑validation checks, enhanced due diligence workflows and how to test systems safely (for example, with synthetic data for privacy‑preserving model validation), so the job shifts from sifting paperwork to adjudicating the few high‑risk, messy cases that matter most (synthetic data privacy-preserving model validation for AML testing).

Kazakhstan's rapid push to embed AI - an AI development concept approved through 2029 and national training goals that aim to scale AI skills by the millions - means the next move is practical: workers should prioritize job‑specific AI fluency (promptcraft, model oversight and data‑quality checks) while employers must pair pilots with robust governance and reskilling programs so automation lifts productivity instead of displacing people; join national momentum by skilling via targeted programs and bootcamps such as Nucamp's Nucamp AI Essentials for Work bootcamp registration, tap growing talent pipelines from Astana Hub's mass training efforts, and follow the state's playbook on infrastructure and legal frameworks to protect data and fairness (Astana Times: Kazakhstan national AI concept to 2029).

Concrete first steps: run small straight‑through processing pilots in back‑office teams, train compliance and credit staff on model validation, and make a visible commitment to upskilling so employees move into exception management, advisory and oversight roles rather than routine processing - a strategy backed by the AIFC's ongoing professional programs and the government's large‑scale AI training targets that create both supply and demand for new skills.

“Alem.ai will contribute to the growth of exports of Kazakhstan's AI solutions and launch a new wave of technological and economic development.” - Zhaslan Madiyev, Minister of Digital Development (Astana Times)