How AI Is Helping Financial Services Companies in Kazakhstan Cut Costs and Improve Efficiency

AI matters for Kazakhstan's financial services because it links directly to a world-class, rapidly digitizing payments stack - biometric IDs, national QR and instant-pay rails - and so it drives real cost cuts in manual processing, faster fraud detection and more personalized lending.

A National Bank survey found about 31% of finance professionals already using AI, with machine learning, computer vision and LLMs at the front of deployments (National Bank of Kazakhstan AI adoption survey), while the central bank's open-banking and SupTech agenda is moving AI from chatbots into supervision and early risk-spotting (Kazakhstan open-banking and SupTech initiative).

With 2.1 billion QR transactions in eight months and dozens of NBK use cases under review, practical workplace training - like Nucamp's 15‑week AI Essentials for Work bootcamp - is the fastest way teams can turn models into measurable efficiency gains.

Bootcamp	Details
AI Essentials for Work	15 Weeks; courses: AI at Work: Foundations, Writing AI Prompts, Job Based Practical AI Skills; early bird $3,582; Register for the AI Essentials for Work bootcamp

“The huge amount of data and AI helps us to process it much better and easier; most importantly, to identify potential risks at a very early stage.”

Kazakhstan's push to digitize the economy makes AI adoption for finance less an experiment and more a practical next step: the State Program

“Digital Kazakhstan”

(launched in 2017) set the policy scaffolding - open‑API pilots, biometric digital ID and an NBK-led sandbox - that companies now lean on to deploy models at scale; strong connectivity (roughly 92% internet reach, mobile broadband covering ~89% of the population) and near‑universal eGov adoption give AI systems rich, high‑quality data to automate onboarding, scoring and fraud signals across a vast landmass; and a growing talent and startup ecosystem - Astana Hub, university programs and coding schools - feeds banks and fintechs with engineers and product teams ready to productionize ML and LLM workflows.

For financial services, that combination of policy certainty, widespread digital identity and payments infrastructure, and an expanding tech talent pipeline turns one-off pilots into measurable efficiency projects - think automated eKYC, quicker credit decisions, and fewer cashier hours - backed by the government's DPI playbook (see the Digital Kazakhstan program) and the CSIS analysis of Kazakhstan's DPI journey.

Metric	Value / Source
Digital Kazakhstan program	Digital Kazakhstan state program (2017–)
Internet penetration	~92% (CSIS)
eGov / digital ID usage	>90% of economically active population (CSIS)
Digital payments share	~89% of transactions (CSIS)

Public-sector process automation is already delivering concrete wins that financial firms in Kazakhstan can build on: the Europrotocol mobile app lets drivers formalize an accident

in up to 15 minutes

without calling traffic police and can trigger insurer notification and payouts

in up to 6 days,

cutting dozens of manual touchpoints and relying on the Mobile Citizens Database for identity verification (Europrotocol mobile app - Insurance Payment Guarantee Fund Kazakhstan); official guidance also requires insurers to organize a damage assessment by inviting an expert within 10 working days after a claim is filed, creating predictable timelines for claims workflows (Kazakhstan government guidance on road traffic accidents and claims).

Similar automation appears in platform ecosystems: ride-hailing cover in Kazakhstan automatically insures trips and expects an insurer decision within about two weeks after full documentation, smoothing passenger and driver recoveries and reducing back-and-forth with claims teams (Yandex Taxi Kazakhstan ride insurance information).

Those fast, rules-based public processes - phone-first reporting, centralized identity checks and fixed service windows - translate into lower operating costs, fewer manual inspections and much faster customer payouts for banks and insurers that integrate with them.

Intelligent automation in Kazakhstan's banks and insurers is already a hybrid playbook: Robotic Process Automation (RPA) handles repetitive, integration-challenged work, AI/ML powers smarter triage and scoring, and humans step in where judgment and compliance matter - a combination that turns pilots into production wins.

Otbasy Bank's Python RPA rollout is a local poster child: more than 40 processes robotized, OCR (Tesseract) bots processing about 2,000 documents a day (work that would have needed 13 people), operational robots that produce the day‑end balance document by 7:00 a.m., and automated account provisioning and access revocation across ABS and Active Directory - all practical examples of “digital employees” freeing human teams for complex decisions (see Otbasy Bank's Python RPA case study).

Layering in Gen AI and careful governance lets institutions automate document summarization, fraud scoring and dynamic underwriting while keeping explainability and data quality front and center, consistent with best practices for scaling Gen AI in financial services.

Crucially, human‑in‑the‑loop is not a bottleneck but a risk‑control system: humans intervene on edge cases in fraud, AML, claims and credit to prevent costly errors and regulatory fallout (read why HITL matters as risk control).

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

For Kazakhstan's banks and insurers, third‑party risk management should follow the same playbook that's reshaping global finance: centralize vendor data, automate repetitive diligence, and deploy AI for continuous monitoring and contract review so risks surface in real time rather than after a disruption; EY's 2025 TPRM research shows centralization plus AI can turn fragmented questionnaires into actionable signals and speed onboarding (EY 2025 third‑party risk management (TPRM) survey), while practical workflow design - defined roles, abnormal‑case paths and metrics - cuts the manual burden (OneTrust third‑party risk management workflow best practices webinar).

Practical use cases for Kazakhstan include AI triage of security questionnaires, automated contract clause extraction for enforceable SLAs, real‑time cyber posture scoring of vendors and even scenario stress‑testing of critical ICT providers; picture an AI agent surfacing a subcontractor breach risk before the morning governance meeting - an operational lifeline on a country with wide geographies and concentrated vendor dependencies.

Metric	Value (source)
Centralized TPRM adoption	57% in 2025 (EY)
Reported benefits: better user experience	56% (EY)
Reported benefits: increased understanding of risks	52% (EY)

“Many companies have repeatedly focused on solving the last problem - the COVID pandemic, supply chain resilience, and so on - rather than approaching TPRM strategically and cohesively.”

Kazakhstan's local AI infrastructure is shifting from promise to practice: the Alem.AI international center in Astana now anchors research, training and industry outreach (Alem.AI international center in Astana), while homegrown assets - AlemLLM (the largest Kazakh‑language model) and the newly launched alem.cloud - put language models and cloud services inside the country's borders (Astana Times coverage of AlemLLM and alem.cloud).

Crucially, Central Asia's first national supercomputer - a Presight partnership powered by NVIDIA H200 GPU clusters delivering up to two exaflops (FP8) - sits alongside Alem.AI, giving universities, startups and enterprises the raw compute to train large models and run secure, on‑shore workloads (Middle East AI News: Kazakhstan national supercomputer announcement).

For banks and insurers this local stack is more than infrastructure: it enables on‑country model training, faster inference for fraud and credit scoring, and stronger data‑sovereignty and security controls - imagine morning risk reports that reflect yesterday's transactions because models were retrained overnight on a two‑exaflop cluster.

Component	What it provides	Source
Alem.AI international center	Research, training, industry hub in Astana	Alem.AI international center official site
AlemLLM	Largest Kazakh‑language AI model	Astana Times: AlemLLM and alem.cloud coverage
alem.cloud	Local cloud platform for AI workloads	Astana Times: alem.cloud local cloud platform coverage
National supercomputer	Up to two exaflops (NVIDIA H200, FP8) for large‑scale model training	Middle East AI News: Kazakhstan unveils national supercomputer

Data governance and sensible centralization are the plumbing that lets Kazakhstan turn AI pilots into repeatable savings: a draft 2025 AI law is introducing risk tiers, explicit limits on biometric and personal data, and liability rules that make human oversight non‑negotiable (see the draft 2025 Kazakhstan draft AI regulation), while eGov 3.0 and Smart Bridge APIs already knit government datasets into a single workflow that reduces duplicate records and speeds secure model training (Kazakhstan eGov 3.0 and Smart Bridge APIs integration for public services).

Practical governance means naming data owners, building a centralized data catalog, enforcing role‑ or attribute‑based access, and measuring data‑quality KPIs so analytics drive one “single source of truth” rather than three competing dashboards (a common governance failure Sigma highlights in its data‑democratization guidance - Sigma Computing data democratization governance best practices).

For banks and insurers, that combination - clear legal guardrails, vetted on‑shore platforms, and strong stewardship - lowers compliance risk, improves model explainability, and shifts effort from firefighting bad data to delivering faster, trustworthy decisions that customers and regulators can rely on.

Focus	Why it matters (source)
Risk‑based AI rules	Defines oversight for high/medium/low systems (Nemko)
Centralized eGov / National AI Platform	Integrates government data and APIs for secure on‑shore workloads (development.asia; Nemko)
Data catalog & roles	Creates a single source of truth and assigns stewardship (Teradata / Proofpoint / Sigma)

“A modern governance framework goes beyond just restricting access; it creates a dynamic, adaptable system for securing data at every layer for each user across the organization. By combining the correct access control models with intelligent monitoring, businesses can ensure that data is utilized safely by users collaborating across teams and organizations while meeting the evolving security demands”

Measuring AI's real cost and efficiency wins in Kazakhstan means tracking straightforward KPIs - days-to-decision, payout latency, number of reengineered processes, service‑delivery speed and aggregate tenge saved - and the country already has headline numbers to prove the point: Kazakhstan's digital reforms produced a reported economic effect of 51.3 billion tenge while public services are being delivered roughly 20× faster, and a DGSC program has reengineered 1,340 business processes since 2021 (Astana Times report on Kazakhstan digital transformation economic impact (51.3 billion tenge)).

Concrete operational metrics are even more vivid - an accident‑reporting app cut insurer payouts from 40 days to about 5 days and school transfers now take 1 day instead of 5 - making the “so what” unmistakable for banks and insurers that integrate with these systems: faster payouts and fewer manual hours directly lower operating expense (Astana Times report on DGSC process reengineering and public service speedups).

Rigorous measurement relies on large‑scale data analysis and automated audit capabilities - an advantage highlighted by public‑audit research showing AI's ability to process vast transactional datasets for more accurate performance measurement (ASOSAI journal study on AI in public sector auditing in Kazakhstan), so teams can report savings in tenge and time with the same confidence as transaction volumes.

Metric	Value / Source
Economic effect	51.3 billion tenge (Astana Times)
Public services speedup	≈20× faster (Astana Times)
Processes reengineered	1,340 since 2021 (DGSC / Astana Times)
Insurance payout time (example)	40 → 5 days (DGSC mobile app)
School transfer processing	5 days → 1 day (Astana Times)

“Our projects bring real reductions in timelines, eliminate unnecessary procedures, and create convenient services.”

Begin with a single, high‑ROI, rules‑based workflow - think loan intake or claims document handling - and prove value quickly: Otbasy Bank's Python RPA bot processed about 2,000 scanned documents a day (work that once needed 13 people), a vivid example of starting small and measurable (Otbasy Bank Python RPA and AI case study in Kazakhstan).

Follow a practical five‑step playbook: scope the project and pick a pilot, determine baseline operating costs, map the process at mouse‑click level, standardize the workflow, then run a short proof‑of‑concept with a trusted partner before scaling and automating monitoring and optimization (Five-step RPA implementation guide for financial services).

Where documents are messy, layer in IDP and a generative AI co‑pilot to extract, validate and surface exceptions for human review - this keeps humans in the loop while speeding throughput and accuracy (OCR to IDP evolution in financial services).

Track simple KPIs (time‑to‑decision, FTE hours saved, error rates) and iterate: pilots become integrations, robots become long‑running digital employees, and overnight retraining or OCR improvements turn a proof‑of‑concept into operational scale across Kazakhstan's banks and insurers.

Step	Action
1. Scope & start small	Pick a single, high‑ROI process for a short pilot (The Lab)
2. Baseline costs	Measure employee time and operating cost before automation (The Lab)
3. Map & standardize	Document mouse‑click workflows and remove exceptions (The Lab)
4. Pilot with RPA/IDP	Use RPA for integration gaps and IDP for complex documents (Otbasy; Lightico)
5. Monitor & scale	Automate monitoring, measure KPIs, iterate and integrate APIs

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

Kazakhstan's fast-maturing AI rulebook raises concrete risks banks and insurers must manage now: unclear liability for AI-caused harm and intellectual‑property gaps, strict personal‑data rules (explicit consent for biometric data and limits on cross‑border transfers), and a coming tiered regime that will treat high‑risk systems - like automated credit decisions and surveillance - very differently from low‑risk tools.

Practical steps reduce exposure: map where models touch sensitive data, minimise data collection, embed human‑in‑the‑loop review for edge cases, appoint a data protection officer and keep processing records, and choose AIFC-style GDPR‑aligned practices where possible to win investor trust.

Prepare for permits or notifications when processing special categories, follow the draft AI law's risk classification, and align contracts and third‑party SLAs so liability is clear before a failure occurs; AIFC rules even set explicit fines for breaches that make compliance measurable.

Treat governance as operational infrastructure - risk‑tier labeling, periodic model audits, data catalogs and documented consent flows turn regulatory uncertainty into a competitive advantage as the legal framework lands.

For a concise roundup of the draft law's risk tiers and compliance expectations see the national overview of Kazakhstan draft AI law risk tiers and compliance expectations, and for AIFC‑level data rules and practical fines review the AIFC Data Protection Regulations and practical fines.

“The principle of transparency and explainability ensures that AI‑driven decisions are understandable and verifiable, especially when they affect citizens' rights.”

Kazakhstan's AI moment is closing the loop between policy, capital and day‑to‑day banking - from the National Bank's finding that over 30% of market participants already use AI to commercial stories like ForteBank's deployments in credit scoring and fraud detection, and even a national pivot where the country's oil wealth is now being steered toward AI projects (National Bank of Kazakhstan AI adoption survey; ForteBank AI initiatives in credit scoring and fraud detection; AI investment surge driven by Kazakhstan's oil wealth).

The practical next steps are straightforward: lock a few high‑ROI pilots into production, pair them with clear governance and measurement, and upskill frontline teams so models become repeatable savings rather than one‑off experiments; short, work‑focused training - such as a 15‑week AI Essentials program - helps institutions move from concept to measurable tenge savings quickly (AI Essentials for Work bootcamp (15-week program)).

In a market where regulators, banks and tech hubs are aligning, disciplined pilots, transparent contracts and a trained workforce turn Kazakhstan's policy momentum into durable operational advantage.

Program	Details
AI Essentials for Work	15 weeks; courses: AI at Work: Foundations, Writing AI Prompts, Job Based Practical AI Skills; early‑bird $3,582; Register for AI Essentials for Work (15-week)

“In Kazakhstan, access to trade and investment is crucial. As a World Trade Organization member, we cannot isolate ourselves from incoming goods, so currency fluctuations will affect us. We must continuously focus on investments and their importance.”