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

Czech financial firms are moving fast from experimentation to practical AI: the government's approved Czech AI Implementation Plan and National AI Strategy 2030 set the stage for harmonising national rules with the EU AI Act and supporting tools, sandboxes and funding for businesses.

On the analytical front the Czech National Bank has shown how LLMs and embeddings can nowcast and forecast inflation by turning millions of web‑scraped product names into vectors - so “Dr. Halíř butter” clusters with olive oil - helping risk teams and pricing models react faster (Czech National Bank AI inflation forecasting analysis).

For firms ready to capture those efficiency gains, practical upskilling matters: Nucamp's AI Essentials for Work teaches the promptcraft and tool‑use finance teams need to deploy explainable credit scoring, fraud detection and process automation quickly (Nucamp AI Essentials for Work bootcamp registration).

Model	Overall RMSE	Stable	High inflation	Return to 2% target
OpenAI o1	5.28	1.09	7.94	1.81
Grok 2	6.19	0.50	9.05	3.45
Financial market analysts	6.63	0.96	9.99	2.31
CNB core model	5.48	0.79	8.41	0.50

“Artificial intelligence represents a huge potential for our economy and society and can significantly improve our quality of life.”

Key AI use cases in Czech financial services cluster around smarter credit scoring, risk‑scoring for non‑clients, and prescriptive optimisation of collections: academic work from the Prague University of Economics shows machine‑learning credit models can improve default prediction for Czech retail portfolios but must be balanced with interpretability and regulatory constraints using tools like SHAP and stakeholder interviews (Prague University of Economics study comparing machine‑learning and traditional credit scoring models); commercial implementations combine transaction histories, NLP on payment descriptions, geo‑location and graph ML to pre‑score people without loan histories and boost accuracy across client segments (Datasentics case study: risk scoring with machine learning using transaction, NLP, and graph signals); and Czech banks have applied prescriptive analytics to cut operational effort - for example, Česká spořitelna reduced collector call minutes by 25% while keeping portfolio performance stable - showing AI can free capacity for complex cases and improve customer outcomes (FICO report: Česká spořitelna uses prescriptive analytics to improve collections effectiveness).

The takeaway: combine transparent ML, behaviour and graph signals, and decision optimisation to win both efficiency and explainability - so banks can pre‑score a cold applicant from web and device signals instead of waiting months for a repayment history.

“The key challenge with the collections project was how to best balance operational efforts with portfolio performance and customer experience,” said Olšák.

Risk analysis and credit scoring in the Czech Republic is moving from textbook models to practical, explainable machine learning: a recent Prague University thesis shows ML models can boost default‑prediction accuracy for Czech retail portfolios but must be carefully integrated with traditional methods to preserve interpretability and regulatory compliance, using SHAP to surface which features drive decisions and nine interviews to map ethical and legal concerns (Prague University thesis on ML vs traditional credit scoring).

Lenders should treat data plumbing as a strategic asset - case studies stress centralized data platforms and seamless integration of disparate sources so models see a fuller borrower picture - and expand scoring beyond credit bureaus by responsibly using non‑traditional signals like utility payments to reach thin‑file customers (ML credit‑risk techniques case study; Creditinfo white paper on AI in credit scoring).

The practical takeaway for Czech teams: marry improved predictive power with explainability, robust data integration and clear governance so models cut losses without surprising regulators or customers.

Thesis author	Elizaveta Novikova
Submission date	16. 12. 2024
Defense date	28. 1. 2025
Language / Institution	English / Vysoká škola ekonomická v Praze

Czech banks and fintechs are increasingly defending digital journeys with layered AI: home‑grown players such as Resistant AI - founded in 2019 and expanding after a $27.6M Series A - apply anomaly detection to flag forged documents and emerging money‑laundering patterns, advising firms to overlay intelligence on legacy systems rather than rip‑and‑replace (Resistant AI anomaly detection for fraud prevention); graph‑driven approaches detect hidden rings by linking emails, phones and device signals so three apparently unrelated accounts reveal a coordinated fraud ring (graph-driven fraud detection using fraud graphs); and behavioural‑intelligence vendors with Czech presences (for example ThreatMark's platform in Brno) combine keystroke dynamics, device fingerprints and real‑time transaction risk scoring to block account takeover and authorised‑push‑payment scams while keeping false positives low (ThreatMark behavioral intelligence platform for transaction risk scoring).

The practical lesson for Czech teams: blend unsupervised anomaly detectors with network analysis and UX‑aware controls so suspicious flows are quarantined before reputational damage - because in today's market fraud is industrialised and “crime‑as‑a‑service” tools can turn a single weak onboarding check into a six‑figure loss overnight.

“The great value of machine learning is the sheer volume of data you can analyse, but selecting the correct data and approach is critical. Supervised learning, which incorporates prior knowledge of fraud tactics to guide pattern identification because it's easy to teach the machine once there's a clear target for it to learn.”

Customer service automation in the Czech Republic is already moving beyond scripted FAQs to human‑like, secure assistants that cut costs while lifting loyalty: a Czech bank that deployed a private GPT‑4 instance in Azure and fed it sanitized internal FAQs and product rules saw escalations fall from 37% to 2%, First‑Contact Resolution jump 60% and NPS rise 34% - clients got answers about loans, cards and claims roughly four minutes faster and the bank reported a 7% bump in 30‑day retention (Instinctools GPT-4 chatbot case study for a Czech bank).

Large incumbents are taking voice seriously too: Česká spořitelna's neural‑voice virtual agent handles routing with about 90% accuracy and gets customers to the right help in ~22 seconds, even using the recognisable tone of a Czech actor to make 24/7 service feel familiar (Born Digital case study: Česká spořitelna custom neural voice virtual agent).

Best practice in the Czech market pairs private models and tight data governance with seamless hand‑offs to humans and personalization engines so routine tasks are automated, complex cases get expert attention, and insurers and banks realise measurable savings without sacrificing compliance or trust (Banking chatbot implementation best practices for banks and insurers).

“The strong brand reputation comes with the trust of our customers. We are pretty fast, yet careful with the innovations we implement. We even have our very own team of conversational specialists developing our virtual assistants. However, we were looking for partners who will help us with things we cannot do on our own and also someone experienced enough to guide us on our way of digital transformation. And Born Digital turned out to be an excellent option.” - Pavlína Kacrová, Call Center Routing Lead, Česká spořitelna

Regulatory reporting, KYC and compliance automation in the Czech Republic sits at the intersection of clear rules and big efficiency upside: the Czech National Bank (CNB) and the Financial Analytical Department (FAU) set the supervision and SAR‑filing routes, AML law dates back to 1996, and a transaction threshold of EUR 15,000 remains a hard reference for when routine checks escalate into formal due diligence; yet there's no legal mandate to deploy automated suspicious‑transaction monitoring, so firms can choose phased automation that preserves human review for high‑risk cases.

Practical implementations pair fast e‑KYC and continuous monitoring with automated filing and audit trails - platforms like Eastnets promote real‑time decisioning and dynamic risk scoring to shrink false positives, while reporting tools such as Alessa claim automation of the bulk of regulatory submissions and templated SAR narratives to cut investigator hours.

Best practice in CZ is simple: adopt a risk‑based, data‑centralised approach, apply EDD for PEPs and remote onboarding (first remote payment must come from an EU/EEA account), and combine automation vendors with strong governance so compliance becomes a competitive advantage rather than a cost sink (Czech KYC rules and SAR process, Eastnets SafeWatch KYC, Alessa regulatory reporting).

Item	Details
Primary regulator	Czech National Bank (CNB)
SARs filed to	Financial Analytical Department (Ministry of Finance)
Minimum threshold	EUR 15,000
Automated STM requirement	No legal requirement
E‑signatures	Accepted (electronic signatures recognized)

“I have worked with Alessa for years because of how useful it is to thoroughly analyze transactions and identify suspicious operations.”

For Czech market insight, the Czech National Bank's experiments show how embeddings can turn millions of web‑scraped product names into numeric vectors so “Dr. Halíř butter” clusters with olive oil, improving automatic assignment to consumer‑basket categories and sharpening inflation nowcasts - read the Czech National Bank first use of AI in inflation forecasting (Czech National Bank first use of AI in inflation forecasting).

This practical, item‑level approach sits alongside continental efforts such as the BIS Innovation Hub's Project Spectrum, which is using LLMs and embeddings to convert raw big data into COICOP categories and thereby scale nowcasting across languages and retail chains (BIS Innovation Hub Project Spectrum on LLMs and embeddings).

For Czech banks and insurers, the payoff is concrete: faster, more granular snapshots of current inflation that complement CNB models and give pricing, risk and ALM teams earlier signals when inflation dynamics shift.

Consumer basket level	Agreement with ex‑ante method
Level 1 (broadest)	99.7%
Level 2	98.5%
Level 3 (e.g., oils and fats)	89.2%
Level 4 (detailed)	80.1%

“AI can identify patterns in data more effectively than traditional methods.” - Piero Cipollone, ECB

Czech finance teams already mix on‑premise muscle, specialised cloud services and emerging LLMs to power scoring, chatbots and real‑time risk pipelines: national plans for an AI Gigafactory (the Prague Gateway DC expansion) promise the sort of capacity - more than 100,000 specialised AI chips - for large‑scale model training and secure local hosting that banks prefer for sensitive workloads (Czech AI Gigafactory press release - Ministry of Industry and Trade); at the model layer, finance teams evaluate global LLM alternatives (Gemini, Claude, Grok) and hardware platforms such as Nvidia's DGX when balancing latency, cost and explainability (Large language models and Nvidia DGX market overview - TechMonitor).

Local projects - like the Czech‑led multilingual model covering 32 European languages - give firms a pathway to culturally aware NLP for KYC, customer support and document parsing without exposing data offshore (Czech-led 32-language AI model project - Expats.cz).

The practical takeaway: combine secured local infrastructure, GPU‑backed training, and carefully chosen LLM providers to run private inference for scoring and virtual assistants while keeping audit trails and model interpretability intact.

Tool / Project	Use for Czech finance teams	Source
AI Gigafactory CZ (Prague Gateway DC)	On‑prem / sovereign training & secure hosting for sensitive models	Czech AI Gigafactory press release - Ministry of Industry and Trade
Grok, Gemini, Claude (LLM alternatives)	Provider choices for chatbots, retrieval‑augmented generation, analytics	Large language models and Nvidia DGX market overview - TechMonitor
Czech‑led multilingual model (32 languages)	Localized NLP for KYC, support and document classification	Czech-led 32-language AI model project - Expats.cz
Nvidia DGX / cloud GPUs	Training and fine‑tuning large models at scale	Large language models and Nvidia DGX market overview - TechMonitor

“Artificial intelligence is already entering the everyday lives of many of us and will be a key driver of the economy in the coming years. That is why we want the Czech Republic to be an active creator, not just a user, of these technologies. The AI Gigafactory will enable us to become part of the European elite and bring a cutting-edge research, investment, and highly skilled jobs to the country,” said Minister of Industry and Trade Lukáš Vlček.

Adoption in Czech financial firms works best when strategy meets pragmatism: start small with focused, deployable pilots (Adastra recommends “AI Days” workshops to surface quick wins), prove value fast and then scale governance and tooling around those successes, not before (Adastra Czech AI adoption analysis).

Anchor programmes to the National AI Strategy 2030 and its Action Plan - which funds capacity building and sandboxes - so projects sit inside a clear legal and funding pathway rather than as isolated experiments (Czech National AI Strategy 2030 official page).

Practical best practices for Czech banks and insurers: centralise data plumbing, pick use cases with measurable ROI (Adastra cites examples reaching payback in 3–6 months), combine bottom‑up teams with visible top‑down sponsorship, run a regulatory sandbox for high‑risk flows, and invest in reskilling so change sticks - data readiness and clear KPIs matter more than chasing the fanciest model.

Expect a cadence of staged wins (fraud, KYC, chatbots, collections) that build trust; Wolters Kluwer's survey shows finance leaders are already planning rapid next‑step adoption of agentic AI, so align pilot timing with talent and compliance readiness (Wolters Kluwer agentic AI adoption survey).

The rule of thumb: prove impact visibly, govern responsibly, and treat AI as a production change - move from sprawling Excel fixes to a humming, auditable pipeline that scales.

Metric	Value / Trend
Czech companies using AI	~11% (cautious estimate / Adastra & Eurostat)
EU average using AI	14% (Eurostat)
Agentic AI (Wolters Kluwer)	6% current; 38% intend to adopt; 44% using by 2026

“Artificial intelligence represents a huge potential for our economy and society and can significantly improve our quality of life.”

Adoption stalls in Czech financial firms are less about shiny models and more about people: over 30% of companies plan to hire in 2025, yet nearly two‑thirds struggle to find qualified candidates - a gap that hits finance, insurance and IT particularly hard and slows rollout of scoring, fraud and automation projects (CzechTrade 2025 report on shortage of qualified candidates).

The industrial and tech talent squeeze is acute for automation engineers, PLC programmers and other specialists, pushing wages up and nudging firms to look abroad; success requires active integration, language support and retention programs rather than one‑off hires (Spenglerfox analysis of demand for automation and technical specialists in the Czech industrial sector).

Practical remedies are immediate and familiar: double down on retraining (the same Czech report notes retraining can lift a welder's starting pay from CZK 20,000 to CZK 40,000), create clear career paths into data and RPA roles, and partner with targeted bootcamps so back‑office clerks can shift into RPA development and exception‑management careers instead of being displaced (Nucamp AI Essentials for Work bootcamp syllabus).

In short: treat talent strategy as core infrastructure - recruit smarter, reskill faster and integrate foreign hires thoughtfully - to turn a workforce shortage from a brake into a competitive advantage.

Quick wins for Czech financial firms are often small, tightly scoped pilots that deliver visible KPIs in months: a private GPT‑4 conversational agent fed sanitized FAQs and product rules can cut escalations dramatically (37% to 2%), speed answers by about four minutes and lift 30‑day retention ~7% while boosting NPS and FCR - see the Instinctools Czech bank chatbot case study for concrete results (Instinctools Czech bank chatbot case study); complementary automation pilots - RPA for credit‑application processing, KYC document ingestion and reconciliations - routinely pay back fast, with real programmes reporting multi‑month paybacks and 3–7x ROI plus tens of thousands of saved labour hours when scaled (automation case studies with 3–7x ROI).

Start with process discovery to target high‑volume, rule‑based tasks, measure time saved, error reduction and customer metrics, and lock a governance path so pilots become auditable production wins rather than one‑off experiments - one pilot that deflects simple queries can free agents to resolve the hardest cases, producing the

“so what” effect: immediate customer relief and measurable cost takeout.

Pilot	Typical measurable KPI	Practical note
Conversational AI (private GPT‑4)	30‑day retention +7%; NPS +34%; FCR +60%; escalations 37%→2%	Sanitize internal data; private hosting for compliance
RPA - credit apps / reconciliations	3–7x ROI; tens of thousands of labour hours saved (case studies)	Pick high‑volume, rule‑based workflows first
Process intelligence discovery	Identify 20% of tasks causing 80% of effort; prioritise top ROI automations	Use discovery to scope pilots and KPIs before building bots

Start with a short, Czech‑specific roadmap that ties pilots to the National AI Strategy 2030 and the recently approved implementation plan: first 6–24 months for discovery and regulatory alignment, then a 2–4 year phase to build foundational platforms and talent, and a 4–7 year horizon for an operational AI layer - these time bands follow the Bank AI Talent Roadmap and the NAIS sequencing so expectations are realistic (Czech Republic National AI Strategy 2030 - summary report).

Practically, register pilots in the CSA regulatory sandbox and plan conformity assessments with ÚNMZ early so “high‑risk” systems can be vetted without full market exposure (ÚNMZ CSA sandbox and conformity assessment framework (Czech Republic)).

Track simple, auditable KPIs: sandbox trials admitted, conformity assessments completed, roles filled (AI developer, data governance lead), annual NAIS reporting milestones, and budget alignment to the estimated national AI envelope - these measures keep projects compliant, fundable and measurable while turning governance into a competitive edge (and avoiding costly rework later).

Stage	Timeline	Top KPI(s)
Discovery	Months 0–24	Sandbox entries; AI steering group formed; key hires (data & compliance)
Foundational	Years 2–4	Platform build; conformity assessments initiated; staff upskilled
Operational	Years 4–7	AI products in production; regular NAIS reporting; governance audits

“Our goal is to create a transparent and quality environment in the Czech Republic that will allow only trustworthy and competent entities to certify AI systems according to the rules of the European Act on Artificial Intelligence.” - Jiří Kratochvíl

The path forward for Czech financial services is practical and staged: start with an AI Days workshop to surface high‑value, low‑risk pilots, prove outcomes in months (Adastra notes many AI investments pay back in 3–6 months), and pair those pilots with focused reskilling so Excel planning gives way to auditable pipelines; Adastra's guide explains how bottom‑up experiments and top‑down alignment unlock scaled adoption (Adastra guide - How Czech enterprises can overcome AI hesitation).

Keep regulation and customer trust front and centre - select transparent models, sandbox high‑risk flows, and measure simple KPIs - while piloting concrete wins such as automated policy review or document processing (Forrester‑cited savings in insurance can approach 50% of labour on some workflows) (Alltius - How AI is revolutionizing policy review in insurance).

Finally, lock learning into the organisation: combine quick pilots with permanent reskilling programs like Nucamp's AI Essentials for Work so teams can write effective prompts, run safe pilots and turn early wins into lasting operational efficiency (Nucamp AI Essentials for Work bootcamp).

“Don't be afraid. AI is here to stay, and while there is respect for new technology, fear is wasteful.”