The Complete Guide to Using AI as a Finance Professional in Finland in 2025

For finance professionals in Finland, AI is no longer a distant promise but a boardroom priority: a FIN‑FSA snapshot found nearly 90% of firms already using or planning AI, with clear payoffs in operational efficiency, faster fraud detection and customer-facing chatbots - but also a sharp focus on governance, data quality and non‑discrimination controls (FIN‑FSA AI adoption survey and financial sector snapshot (2025)).

National policy is aligning with the EU AI Act and new oversight rules are coming into force, so legal compliance matters as much as ROI (Finland AI regulatory outlook 2025 - EU AI Act alignment and trends).

Practical skills are now the missing link for many finance teams - training, clear procurement clauses and vendor controls turn pilots into safe scale-ups; consider a focused course like the 15‑week Nucamp AI Essentials for Work bootcamp (15‑week practical AI for the workplace) to learn prompts, tools and workplace applications without a technical background.

“AI and ML free accounting teams from manual tasks and support finance's effort to become value creators.”

Looking ahead in 2025, AI in Finnish financial services is shifting from pilot projects to business‑critical infrastructure: the FIN‑FSA snapshot found nearly 90% of firms using or planning AI, with generative models already in use at about 74% of respondents and machine learning at 64% - a mix that drives everything from personalised offers to automated underwriting but also raises clear governance and fairness questions (FIN‑FSA AI adoption survey and Finland financial sector snapshot 2025).

Expect expansion where the payoff is clearest - fraud detection, customer experience and document automation - with industry reports showing AI can cut costs and dramatically speed fraud detection (up to a 95% faster response in some cases), while early adopters report measurable revenue and efficiency gains (Databricks Data + AI Summit findings on AI in financial services 2025).

At the same time, thought leaders warn that AI is becoming foundational, not ornamental, so Finnish finance teams must pair ambitious pilots with stronger data quality, explicit limits on high‑risk systems and targeted upskilling if the sector is to scale safely and fairly (Deloitte Tech Trends 2025 report on AI as infrastructure in Finland).

The near future will look less like isolated experiments and more like an orchestration of models, controls and human oversight - think agentic and multimodal tools deployed under tight non‑discrimination and data‑protection guardrails, so the upside of faster decisions and lower costs doesn't come at the price of trust.

Finland's national AI story is a pragmatic mix of big-picture strategy and hands‑on programmes that matter for finance teams in 2025: the country set out a national AI strategy in 2017 and followed with targeted updates and the Artificial Intelligence 4.0 Programme to push adoption - especially among SMEs - and to harden public‑sector use via AuroraAI, MyData and ethical guidance (Finland national AI strategy report - AI Watch).

Public backing has been substantial: Business Finland's AI Business initiative (2018–2021) pumped significant funding into R&D, platforms and testbeds - running a more than EUR 200M programme with broad services for companies, research groups and public procurers - while flagship research bodies like FCAI received earmarked support to bridge lab results to production (Business Finland AI Business programme funding and overview).

Complementary moves - an AI maturity tool, innovation accelerators (FAIA), regulatory sandboxes and reskilling drives that estimate roughly one million Finns needing upskilling - mean Finland is building both talent and infrastructure; industrial incentives such as the Venturi Ecosystem programme have also leveraged hundreds of millions to grow private R&D, showing the carrot‑not‑stick approach to boost industry investment (Analysis: Venturi Ecosystem programme driving private R&D investment).

The net effect for finance: a predictable policy landscape, ready funding routes and clear national tools to help move pilots into governed, scalable AI services - think of the AI maturity tool as a compass in a dense data forest.

“The programme does deliver impact and results, and we plan to continue it.” - Kari Komulainen, director of funding services, Business Finland

Short answer: yes - Finland stacks up well for finance teams looking to adopt AI because the country blends deep public investment, world-class research and a talent pipeline that keeps growing.

National programmes and grants (notably Business Finland's AI support and the AI Business Programme) plus flagship research hubs like the Finnish Centre for Artificial Intelligence mean money and know‑how are aiming squarely at scaling lab projects into production, while infrastructure such as the LUMI supercomputer gives teams serious compute muscle for large models; the European Commission's country review lays out these flagship moves and funding commitments in detail (European Commission AI Watch: Finland AI strategy report).

On the talent side, a highly educated population, new initiatives like the AI Finland Network and vocational upskilling targets - together with a vibrant startup scene (nearly 4,000 startups and a growing string of exits) - make Finland both an incubator and a magnet for AI roles (InHuntWorld analysis: Finland as a hub for AI and tech talent).

The practical takeaway for finance: accessible funding, testbeds and compute mean pilots can move to scale here - just be ready to hire or train the specialized roles that turn models into governed, auditable services; picture a finance team feeding models that run on LUMI's racks, not just on laptops.

“The AI and data revolution must be harnessed to improve Europe's competitiveness and stability, as well as to enhance the resilience of the economy and financial system.”

For finance teams in Finland the highest‑impact AI plays are strikingly practical: conversational AI for 24/7 self‑service and agent assist (Nordea's Nova, deployed across the Nordics, now handles millions of conversations and the Finnish version tripled its topic coverage to reach ~95% in‑scope resolution and even guides logged‑in customers to actions like blocking a card or finding an IBAN - see Nordea's Nova case study), faster, more accurate credit decisions and automated underwriting (S‑Bank modernised loan processing with SAS Viya on Azure to speed approvals and make models more transparent for business users), and revenue‑side tools that drive smarter cross‑sell and pipeline conversion (commercial banking pilots show 1.5–2x higher conversion when AI synthesises prospect signals and next‑best actions).

Equally important are fraud detection, KYC/document automation, and back‑office automation - classic ML and OCR use cases that cut manual work and shorten cycle times - while generative models and scenario‑based forecasting enable richer financial reports, stress tests and synthetic data for safer model training.

The practical “so what?”: start with one customer‑facing or credit‑automation pilot that partners business owners and compliance, measure deflection, accuracy and time‑saved, then scale the playbook across fraud, treasury and sales to turn pilots into governed production services (examples and playbooks at Nordea, S‑Bank and Alexander Group show how these pieces stitch together).

“For banks, AI is going to be transformative across a wide range of applications. At Nordea, we acknowledge the importance of a scalable chatbot strategy that the boost.ai platform enables. It is a key component towards human-centered digital transformation.” - Mattias Fras, Group Head of AI Strategy & Acceleration, Nordea

Finland's regulatory landscape for finance teams using AI is now a live business constraint as well as a governance opportunity: EU rules on general‑purpose AI models and governance began applying from 2 August 2025, so banks and lenders must be ready for enhanced transparency, training‑data summaries and provider obligations while the wider, risk‑based regime (including strict rules for high‑risk uses such as credit scoring and access to essential services) phases in over 2026–2027 (EU AI Act implementation timeline and enforcement schedule).

Finland is following a decentralised national model - draft legislation proposes ten existing market surveillance authorities with Traficom as the single point of contact and the Office of the Data Protection Ombudsman tasked with oversight of prohibited practices and many high‑risk categories - so finance teams will liaise with sectoral supervisors (including the FSA for certain insurance and financial risk uses) rather than a single super‑regulator (EU AI Act national implementation plans and member state approaches, Hannes Snellman analysis of Finland's draft implementing act).

Practical takeaway: treat the AI Act as a checklist for procurement, documentation and human‑in‑the‑loop controls - expect to publish more model documentation, perform fundamental‑rights impact assessments and align vendor contracts to ensure deployers and providers meet GDPR, GPAI and high‑risk obligations before scaling models in production.

Strong governance in Finnish finance teams means turning AI ambition into repeatable controls: start with a clear, centralised oversight model (an AI ethics committee or designated owner), embed AI risk into existing model‑risk frameworks, and operationalise vendor and data‑quality checks so third‑party LLMs or ML pipelines can't quietly drift into production.

Practical playbooks are available - for example the FINOS AI Governance Framework for vendor-agnostic AI governance lays out vendor‑agnostic, industry‑focused guardrails and a catalog of risks and mitigations that teams can adapt - and national supervision expects the same discipline: the FIN‑FSA thematic review on AI use in the financial sector (2025) flags data quality, data protection, third‑party management and lack of AI expertise as top risks and shows only roughly half of firms have an AI strategy today, with 63% holding ethical AI standards and 82% operating AI user rules, so formalisation is overdue.

Concrete controls that pay off: documented model lineage and DPIAs, strict procurement clauses and SLAs for providers, human‑in‑the‑loop gates for high‑risk decisions, continuous monitoring and logging, automated performance and fairness alerts, and targeted upskilling programs so business owners can challenge outputs.

Prepare contracts and documentation now - authorities' supervisory powers under the EU regime are live - and treat model documentation like ledgered transaction history: every dataset, preprocessing step and version must be auditable if AI is to scale without eroding customer trust.

“AI is a good tool in the hands of responsible operators. It provides significant benefits to its users, but it is very important to identify and manage related risks. It is imperative that companies devise AI strategies as well as ethical standards and comply with them to ensure that AI solutions are safe, fair and responsible. Only by doing so, general confidence in the operation of the financial markets can be ensured.” - Samu Kurri, Head of Department

Turn AI pilots into production-grade services by following a Finland‑focused, risk‑aware roadmap: choose one high‑impact, low‑risk process (e.g., reconciliations, customer chat deflection or credit decisioning) and run a short, measurable pilot that proves value fast - Nominal's finance roadmap shows a Foundation pilot (Weeks 1–4) can deliver 70%+ automation and ~50% time savings, then expand (Weeks 5–12) before optimization (Weeks 13–24) and month‑6+ innovation for predictive forecasting and scenario planning (Nominal four‑phase roadmap for finance teams).

In Finland, this phased approach must sit beside strict procurement, documentation and fairness checks: the FIN‑FSA snapshot and industry reviews show nearly 90% of firms are adopting AI but also flag data quality, third‑party risk and non‑discrimination as top constraints, so include DPIAs, human‑in‑the‑loop gates and vendor SLAs from day one (FIN‑FSA AI adoption and risk snapshot for Finland).

Finally, align the rollout with EU deadlines and national implementation plans - designate responsibilities, publish model documentation and lock procurement clauses now so scaling doesn't run afoul of the AI Act's enforcement timetable (AI Act national implementation plans and timelines), turning early wins into trusted, auditable services rather than one‑off experiments.

Operational readiness in Finland begins with a practical DPIA-first mindset: screen every AI procurement for high‑risk signals (biometric, genetic or location data; automated scoring; large‑scale or novel processing) using the Office of the Data Protection Ombudsman's DPIA list, involve the DPO early and treat pilots the same as production - there are no GDPR shortcuts for test runs.

Build procurement packs that require vendor disclosures (provider name, accreditation and model purpose), written data‑processing agreements, audit and access rights, SLAs for model updates and security, and explicit obligations to supply training‑data summaries and model documentation; use established templates to structure assessments and evidence (for example, the IAPP and d.pia.lab DPIA templates offer practical, stepwise forms that map to Articles 35–36).

Schedule DPIA work into the tender timeline, require prior consultation if residual risk remains, and ensure the contract binds providers to remediate fairness and data‑quality findings - think of the DPIA as the pre‑flight checklist that keeps models from taking off until both compliance and business owners sign off.

Finland's financial sector is at a clear inflection point: the FIN‑FSA's thematic review shows nearly 90% of firms already using or planning AI, with high‑risk systems in place and clear benefits in efficiency and customer service - but also persistent risks around data quality, protection and expertise that supervisors are watching closely (see the FIN‑FSA thematic review for details).

Practical next steps for finance teams: lock governance and procurement rules in now, run DPIAs and human‑in‑the‑loop gates before scaling, treat model documentation as auditable ledger entries, and prioritise targeted upskilling so business owners can challenge outputs.

With authorities' supervision and sanction powers taking effect on 2 August 2025, small measurable pilots (reconciliations, chat deflection, credit‑decision automation) should be paired immediately with robust controls; for teams wanting structured, workplace‑focused training, consider the Nucamp AI Essentials for Work 15-week bootcamp to learn prompts, tools and practical deployments without a technical background.

The smart play in 2025 is pragmatic: prove value fast, document everything, and harden the controls so AI delivers better decisions without surprising regulators or customers.

“AI is a good tool in the hands of responsible operators. It provides significant benefits to its users, but it is very important to identify and manage related risks. It is imperative that companies devise AI strategies as well as ethical standards and comply with them to ensure that AI solutions are safe, fair and responsible. Only by doing so, general confidence in the operation of the financial markets can be ensured.” - Samu Kurri, Head of Department