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

Finland's financial sector is racing from pilots to production in 2025, with a FIN‑FSA snapshot showing nearly 90% of firms already using or planning AI and 74% testing generative models alongside classic machine‑learning tools - yet many have set strict limits, even banning tools like ChatGPT over data‑leak concerns (FIN‑FSA 2025 AI adoption survey and market overview).

Institutions cite clear wins in fraud detection, back‑office automation and personalised pricing, while regulators and industry guides stress alignment with the EU AI Act and national oversight as the next milestone (Finland legal framework and EU AI Act alignment (Chambers Practice Guides)).

The picture is pragmatic: bigger banks staff dozens of AI specialists and dedicate budgets, smaller firms prioritise simple, explainable models, and the pressing “so what?” is this - governance, data quality and fairness now determine who captures AI value without tripping compliance risks.

“Many financial institutions are interested in AI but hesitant to commit to extensive transformation projects with uncertain returns,” says Päivi Karesjoki, Services SVP at Evitec Solutions.

Market momentum in Finland's financial services has shifted from cautious experiments to broad, pragmatic rollout: a FIN‑FSA snapshot shows nearly 90% of firms using or planning AI and three‑quarters already testing generative or general‑purpose models, but adoption is uneven - large banks commonly run dozens of AI projects and may staff up to 20 specialists while smaller lenders often rely on a single expert or outsource complex work, steering them toward simpler, explainable models that satisfy regulators and customers alike (FIN-FSA AI adoption snapshot - Finland financial sector 2025).

Use cases concentrate on fraud detection, back‑office automation and personalised pricing, mirroring wider Nordic patterns where generative AI spending and productivity goals compete with talent and data‑readiness concerns (Nordic generative AI adoption and momentum).

Investment lines are becoming permanent - roughly half of firms now budget for AI and more than half report dedicated AI staff - and the “so what?” is clear: firms that pair practical use cases with robust governance and data quality will convert efficiency wins into durable competitive advantage, while those that don't risk getting outpaced or tripped up by compliance and fairness issues.

“The Evident AI Index shines a light on the growing impact artificial intelligence is having on the financial services industry” - Teresa Heitsenrether, Chief Data & Analytics Officer, JPMorganChase

Across Finland the AI story is practical and varied: customer-facing conversational AI and chatbots dominate front‑line wins, while machine‑learning and generative models power fraud detection, underwriting and pricing engines - the FIN‑FSA snapshot shows 74% of firms using generative/general‑purpose AI and 64% using machine learning, and insurers are especially active with 20 out of 22 reporting live solutions or near‑term roadmaps (FIN‑FSA AI adoption snapshot 2025).

Nordic banks are already routing huge volumes to bots - Nordea's Nova handles hundreds of thousands of conversations across the region with the Finnish bot achieving a 95% in‑scope resolution rate (Nordea Nova virtual agent conversational AI case study) - and chatbot penetration in the Nordics sits among the highest globally, reflecting fast returns on efficiency and 24/7 service (Nordic banking chatbot adoption and performance statistics).

Behind these wins sit back‑office automation, document parsing for faster lending decisions, AML/fraud monitoring and personalised customer offers; the “so what” is clear - scale the right use cases, keep humans in the loop and pair models with strong data governance to turn pilots into durable, compliant value.

“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.”

For Finnish firms the bottom-line benefits of AI are already concrete:

leaders expect a GenAI “productivity windfall” (77% in EY's pan‑European survey) that shows up as faster software delivery, smarter customer service and steadier cost reductions, with industry analyses citing productivity uplifts of roughly 30–50% and operational cost savings near 22–25% as firms automate document processing, fraud detection and routine back‑office work (EY survey on GenAI adoption; market data on AI productivity and cost savings).

The strategic objectives that follow are clear for Finland: convert quick efficiency wins into durable capability by funding GenAI roadmaps, doubling down on training so that many roles become “AI‑augmented” rather than redundant (EY finds significant upskilling needs and rising capital plans), and shifting pilots from back‑office proof‑of‑value into customer‑facing, revenue‑generating flows while keeping governance and explainability at the centre.

Practical workforce pathways matter: move talent from Excel to SQL to Python to turn manual expertise into hybrid human+AI advantage (upskilling pathway: Excel→SQL→Python).

The “so what” is stark - firms that align investment, skills and robust governance will capture AI's gains without trading away compliance or customer trust.

Investment and capability building in Finland's financial services is moving from ad hoc pilots to sustained budgeting and skills programmes: roughly 47% of firms now show dedicated AI budget lines and 52% report dedicated AI staff (small firms often have one specialist, while large institutions may staff up to 20), with more than 320 AI professionals already engaged across the sector and training becoming routine - over half of firms provide general AI training and about 60% offer risk‑specific courses for oversight (FIN‑FSA snapshot summarized in the market review).

Public and private investment reinforce each other: Business Finland's Finnish AI Landscape 2025 urges “bold investments” to showcase Finnish strengths and attract talent and capital, while national digital roadmaps earmark hundreds of millions of euros to strengthen infrastructure and skills.

The startup scene underpins capability too - Finland hosts dozens of AI companies with significant venture activity and near‑billion dollars of cumulative funding, so banks and insurers can partner locally to build customised solutions rather than re‑inventing the wheel.

The practical takeaway: coupling recurring budget lines, clear learning pathways (move talent Excel→SQL→Python) and vendor partnerships turns scattered expertise into a scalable advantage that can staff explainable, regulator‑ready models rather than fragile point solutions.

For hands‑on planners the “so what?” is simple - a single well‑funded team of 10–20 AI practitioners can flip a queue‑drained contact centre into a 24/7 automated service while training the rest of the firm to supervise, audit and improve models safely.

"The speed of artificial intelligence development is staggering... the Finnish AI Landscape Report has been conducted precisely for this need." - Timo Sorsa, Head of Business Finland's Generative AI campaign

Strong governance has moved from a nice-to-have to the business-critical layer that determines which Finnish financial firms can scale AI without tripping regulators or reputational risk: national Ethical Guidelines for AI and Finland's alignment with the EU AI Act set the ethical and legal backdrop, while FIN‑FSA's survey finds governance is already front‑of‑mind - most firms report explicit AI strategies, formal codes and strict usage limits (nearly 90% place boundaries on tools, with many banning consumer models like ChatGPT over data‑leak concerns) (Finnish ethical AI guidelines and EU AI Act alignment; FIN‑FSA AI adoption and risk snapshot for the Finnish financial sector).

Practical controls map directly to risk: transparency and explainability, human review for automated decisions, DPIAs, bias testing and robust procurement clauses about training data and audit rights; industry playbooks such as the FINOS AI Governance Framework now offer drop‑in controls and operational runbooks that financial teams can adopt to satisfy supervisors while accelerating safe deployments (FINOS AI Governance Framework v1.0 deployable guardrails).

The “so what?” is vivid: firms that bake data quality, non‑discrimination checks and traceable controls into the model lifecycle turn regulatory friction into a competitive moat, whereas those that don't will face remedy orders, stalled rollouts or hard limits on the most promising GenAI use cases.

“Putting responsible AI into practice.” - AIGA (Artificial Intelligence Governance and Auditing project)

Regulation in Finland is now defined by a fast, phased EU timetable that firms can't ignore: the EU AI Act introduces immediate bans and AI‑literacy duties from 2 February 2025, brings in obligations for general‑purpose AI (GPAI) on 2 August 2025 and rolls out the bulk of high‑risk rules on 2 August 2026 - see the official EU AI Act implementation timeline for the full schedule (EU AI Act implementation timeline).

Crucially for Finnish financial firms, the Government has confirmed that GPAI rules will apply from 2 August 2025 but that supplementary national legislation on notified bodies, supervision and sanctions is still being finalised (a legislative proposal was submitted 8 May 2025), so Finland will not impose national sanctions or start full national supervision immediately on application day (Finnish government press release on AI Act application).

The so‑what is plain and practical: Finnish banks and insurers should treat the coming months as a compliance runway - lock in AI‑literacy programmes, map which systems may be high‑risk or GPAI, prepare conformity documentation and post‑market monitoring plans, and protect against the hefty EU fines that underpin the regime - because domestic enforcement may lag slightly, but the EU rules and GPAI oversight by the European AI Office will still demand traceability, human oversight and documentation for safe, scalable deployments.

Compliance and procurement in Finland now sit at the heart of operationalising AI: contracts must do the heavy lifting by spelling out strict data‑protection and security measures, clear ownership of training datasets, audit and bias‑mitigation rights, explainability expectations and remedies for degraded model performance - in short, procurement is the place to bake in GDPR, DPIAs and operational resilience rather than shoehorning them in later (Finland AI procurement and data-protection guidance (Chambers Practice)).

Practical clauses to demand from suppliers include breach notification timetables, access controls, model‑training transparency, regular fairness audits and the right to independent testing; the EU's new model contractual clauses for AI procurement are a pragmatic checklist for public and private buyers aiming for traceability and accountability (EU model contractual clauses for AI procurement (Cooley)).

Operationally, treat procurement as a compliance gateway: map high‑risk AI, require PIAs/monitoring plans, appoint DPOs where needed, and lock consumer‑facing terms into customer contracts - Finland's Ombudsman case about parking enforcement shows the cost of omissions when a processing purpose isn't written into service terms, which rendered the data processing unlawful and painfully obvious to regulators.

The so what? is sharp and simple: well‑crafted contracts plus routine audits turn vendors into compliance partners; weak procurement leaves institutions exposed to fines, stalled rollouts and reputational damage, so procurement teams should act like gatekeepers, not paper pushers.

Generative AI in Finland collides with three practical fault‑lines that any bank or insurer must manage: copyright and licensing, cybersecurity/data‑protection, and ESG‑style sustainability and fairness.

Copyright stewardship is no longer optional - the Nordic repro rights' joint statement urges Transparency, Permission and Fair Compensation for training data and stresses that creators must be engaged and paid rather than treated as invisible inputs (Nordic AI licensing principles (Kopiosto joint statement)).

Legal nuance matters too: public legal repositories such as Finlex are functionally open (Lovdata explains why database rights cannot easily block AI training unless real economic harm is shown), so not all sources carry the same clearance burden (Lovdata: Can database rights prohibit training AI on Finnish laws (Finlex)).

Cybersecurity and privacy rules from Finnish institutions echo the same caution - never feed confidential or sensitive personal data into external GenAI systems, run DPIAs, and require provable information‑security guarantees from suppliers to avoid leaks and downstream liability (University of Helsinki guidance on GenAI use).

ESG considerations thread through these risks: energy and social sustainability, fair remuneration for creators and bias testing should be part of any vendor scorecard, and simple measures - for example treating a creator's “NoAi” metadata tag as a red traffic light - make compliance operational.

The bottom line: combine clear licensing, ironclad data controls and visible sustainability checks to turn generative AI from a compliance headache into a trusted, scalable capability (Aalto University guidance on AI and copyright).

Wrap up the roadmap for Finnish finance: treat the FIN‑FSA thematic review as the starting whistle - nearly every large and medium firm is already on board with AI and many have high‑risk systems in play, so the practical checklist is short and urgent: lock in clear governance and documented policies, map which models are high‑risk or general‑purpose and prepare conformity and post‑market monitoring, harden data quality and privacy controls, and run wide AI literacy and role‑based training so teams can safely supervise automation (the FIN‑FSA thematic review of the use of AI in the financial sector: FIN‑FSA thematic review of the use of AI in the financial sector).

Treat the coming EU deadlines as a compliance runway - start documenting traceability and human‑oversight now per the EU AI Act timeline (EU AI Act implementation timeline and compliance deadlines) - because national supervision will ramp up and firms with missing paperwork will see rollouts stall.

Finally, make upskilling a funded priority: practical programmes that teach promptcraft, risk-aware prompts and human+AI workflows (for example, the AI Essentials for Work bootcamp) turn compliance-readiness into competitive capability rather than a cost centre (AI Essentials for Work bootcamp registration).

A vivid rule of thumb: tightening governance around a few mission‑critical models and training one well‑resourced team will often unlock more value - and less regulatory heat - than scattering small pilots across the organisation.