EMBRACING ARTIFICIAL INTELLIGENCE IN FINLAND’S FINANCIAL SECTOR

The Finnish financial services landscape is undergoing a transformative shift through the deployment of artificial intelligence (AI) technologies. The authors have noticed a considerable uptick in the use of artificial intelligence in within the financial sector starting last year. This article reflects the authors’ current view of the market situation combined with a recent thematic assessment by the Finnish Financial Supervisory Authority (FIN-FSA) of how banks, insurers, capital markets operators, payment service providers, and consumer credit firms in Finland are adopting AI. It summarises key aspects of patterns of AI adoption, examples of AI applications, the associated investments and resource allocations, and how Finnish financial institutions anticipate and address governing regulations.

1. Background and Regulatory Context

Given FIN-FSA’s mandate to oversee and manage risks in Finland’s financial sector, the authority conducted a survey to assess current AI usage. This study, published in June 2025, provides a timely snapshot of how AI-powered innovations are becoming central to various financial services, while also gauging the industry’s capacity to comply with incoming obligations for high-risk AI systems.

To capture a representative data set, FIN-FSA engaged a risk-based sample of 83 financial sector operators. The sample included banks, insurance companies, payment service providers, consumer credit issuers, and investment firms active in capital markets. Collectively, these participants provided a cross-sectional perspective of AI adoption across the financial services industry.

2. Patterns of AI Adoption

One of the most striking findings is the near-universal commitment to incorporating AI: Nearly 90% of organisations either already used AI solutions or planned to implement them within two years. This pattern was especially pronounced among large entities, all of which had at least one AI project underway or on the horizon. Medium-sized institutions likewise displayed robust interest, while smaller organisations showed varied levels of engagement, sometimes hindered by limited resources or specialised AI expertise.

Across the banking sector, bank reported using or intending to use AI for diverse applications including data search, translation services, text generation, customer segmentation, and support functions like chatbots. The insurance industry likewise showed a pronounced emphasis on AI uptake, with 20 out of 22 surveyed insurers having existing solutions or highly developed near-term roadmaps. Their adoption spanned from augmenting marketing and sales to more specific tasks such as underwriting, risk assessment, and claims handling. These patterns were broadly comparable in payment services and capital markets, though differences emerged when comparing the scope of AI applications.

Generative and general-purpose AI models were especially widespread, employed by 74% of financial institutions responding to the survey. Machine learning was also prominent, featuring in 64% of respondents’ systems. Meanwhile, rule-based models, historically the easiest to explain in regulated contexts, maintained a strong foothold.

Survey results suggested that, while larger and more resource-rich entities had integrated AI deeply into both operations and strategic planning, smaller organisations often concentrated on simpler AI use cases or outsourced more complex tasks. However, all groups anticipated increased AI usage over the coming two to three years, a trend unimpeded by sector or size.

3. Strategic Objectives and Realised Benefits

For most respondents in the survey, AI adoption served as a direct route to operational efficiency and cost reduction. This goal manifested in automating administrative tasks, generating real-time analyses for fraud detection, and streamlining compliance workflows. Banks, for instance, reported that AI chatbots relieved contact centres by handling repetitive queries and providing immediate support. Payment providers leveraged AI to isolate suspicious transactions in near real-time, allowing compliance teams to focus on investigations rather than manual assessments.

Enhancing the customer experience also featured prominently among survey participants’ objectives. Insurers used AI for personalised policy quotes, drawing on advanced data analytics to craft more accurate assessments and potentially lower premiums. Similarly, banks and consumer credit issuers aimed to refine creditworthiness evaluations, ensuring decisions were not only prompt but also fair and transparent.

These findings are in line with the authors’ understanding of the market’s goals and objectives when it comes to adoption of AI. Majority of clients indicate that leveraging artificial intelligence provides clear benefits both in minimising operational costs and in meeting customer needs. At the same time, organisations are reallocating their human resources to ensure new technologies most effectively support their business objectives.

4. Investment Trends and Human Resources

Roughly 47% of surveyed companies dedicated specific line items in their budgets for AI development. Broadly, the data pointed to consistent investment patterns across financial segments; neither industry type nor organisation size emerged as a major differentiator in terms of budget scope.

52% of respondents had dedicated AI staff. Small companies typically had one AI specialist, while medium-sized ones employed one to five. Large financial institutions managed teams of up to 20 AI professionals. Overall, over 320 professionals across Finland’s financial sector were working on AI initiatives, a number expected to grow in tandem with expanding adoption.

Over half of the respondents provided general AI training to all staff, and nearly 60% reported offering risk-specific training for those tasked with AI oversight. A smaller proportion gave in-depth methodological training to developers and data analysts responsible for building or maintaining AI systems.

These results indicate that investing in AI has become a firmly established part of many financial-sector organisations’ budget planning, with no signs of a decrease. The authors see this as reflecting a common view that AI solutions support competitive advantages broadly across organisations of various sizes and in different business sectors. The same pattern extends to personnel resources and the development of expertise. The authors are particularly pleased with the fact that a relatively large portion of respondents have already created training programs for their employees.

5. Governance and Risk Management

The survey responses highlight the need for strong governance structures and clear codes of conduct regarding AI. In large organisations, AI risks have been widely integrated into IT risk management, and many respondents have designated individuals responsible for AI oversight. Key concerns include data quality, data protection, and the avoidance of discrimination – areas with significant risks in credit scoring or insurance pricing.

More than half of respondents had established explicit AI strategies, 63% had formal codes of ethics, and 82% maintained codes of conduct related to AI usage. In large organisations, such governance was comprehensive, with 81% integrating AI risk parameters into their general risk management frameworks.

Respondents pointed to data quality and data protection as their most pressing AI-related risks. Where large volumes of potentially sensitive information are processed, stringent privacy safeguards and robust data hygiene protocols become paramount. Another major concern was non-discrimination, particularly in high-stakes contexts like credit scoring or insurance pricing.

Nearly 90% of companies declared specific limits on AI usage, either entirely prohibiting certain solutions or imposing approval processes. Many had banned generative AI models such as ChatGPT due to data protection uncertainties or concerns about data leakage. Others disallowed AI for decision-making, emphasising that decisions must remain with humans.

Overall, the high percentage of companies having set rules and boundaries for AI usage corresponds with the authors’ understanding of the sector’s robust compliance structures. Finnish companies have traditionally been particularly law-abiding and risk-aversive, and the security-related actions implemented to curb the risks of AI usage underscore that presumption.

6. Non-Discrimination and High-Risk AI Applications

Non-discrimination is emerging as one of the leading regulatory challenges in the context of AI, particularly due to the EU’s AI Regulation. European regulations and Finnish anti-discrimination laws demand that AI systems apply equal treatment to all individuals.

Survey results indicated that insurance companies were particularly attentive to these issues, reflecting the segment’s heavy reliance on personal data to estimate risks. Bank and consumer credit issuers similarly expressed awareness that credit decisions risk entrenching biases if training data or algorithmic designs do not address fairness.

As usage of high-risk AI is set to expand FIN-FSA’s forthcoming supervisory powers will likely spur more consistent sector-wide approaches to fairness assurance. The authors predict that the expanded supervisory powers of the FIN-FSA will encourage the financial sector to address non-discrimination and data management even more systematically in high-risk AI applications, such as credit scoring and insurance pricing. The industry would benefit from clear guidelines regarding high-risk use cases, although the Commission’s guidelines specifying the practical implications of Article 6 of the AI Act, to be published latest February 2026, will likely provide some clarity on the matter also for the financial sector.

7. AI in Financial Crime Prevention

According to the survey, tackling money laundering, terrorism financing, fraud, and sanctions evasion ranks among financial institutions’ highest priorities. Approximately 39% of respondents indicated they use AI techniques in at least one phase of financial crime prevention.

Fraud detection is the clearest success story for AI in crime prevention, reflecting the direct financial risks to both institutions and customers if fraudulent activities go undetected. In banks, these solutions typically monitor transaction patterns in real-time, flagging anomalous or potentially fraudulent behaviours. Such AI is often combined with manual review, ensuring compliance teams retain oversight.

The study further revealed usage in anti–money laundering (AML) processes, including know-your-customer checks, real-time transaction monitoring, and risk-scoring for individuals or corporate entities. Although AML is governed by stringent regulations emphasising explainability, 13% of respondents employed AI-based identification, and 24% used AI for continuous monitoring. Payment service providers likewise leaned on AI for spotting atypical payments and controlling broader money-laundering risks.

Preventing financial crime remains a paramount priority for the financial sector, and AI has become an established part of this effort. The authors particularly emphasise the strong role of fraud prevention in AI applications because both financial institutions and their customers face significant financial and reputational harm if fraudulent activity goes undetected. AI is also widely employed in anti–money laundering efforts, such as customer due diligence and real-time transaction monitoring, which the authors see as an indication that security requirements and legislation are driving financial organisations to adopt AI technologies more extensively.

8. Sector-Specific Insights

Domestic Finnish banks and foreign branches show widespread adoption of generative and machine learning solutions for everything from back-office automation to the complex calculation of capital requirements. Customer-facing chatbots were nearly universal among large banks, reflecting a push to improve user experience. Financial crime analytics, particularly for fraud detection, was equally pervasive.

Insurers embraced AI for process automation, risk/pricing models, marketing, and claims processing. These companies prioritised ethical frameworks and codes of conduct, showcasing a growing awareness of how easily algorithms could inadvertently penalise certain demographic groups. By systematically integrating AI risk considerations into broader IT risk management, the insurance segment has established stronger non-discrimination defence measures compared to some other areas.

Capital markets participants, such as exchanges, investment management firms, and securities service providers, demonstrated moderate to high AI usage, focusing heavily on data analysis, security, and process automation. Algorithmic trading was not explicitly detailed in these survey findings, but references to advanced data modelling hint at widespread behind-the-scenes use of AI for insight generation and risk analysis.

Among payment service providers, AI solutions ranged from chatbots and text content creation to more complex fraud and AML checks. This embraces the demands of a fast-evolving payments ecosystem, where innovation is critical to meet customer expectations for instant transactions.

Consumer credit issuers displayed comparatively limited AI adoption, yet the majority planned to implement AI within two years. Credit scoring and creditworthiness assessment sat high on their priority list, underscoring how automated processes can accelerate lending decisions. Nevertheless, smaller issuers struggled with data governance, highlighting the tension between personalisation and regulated data usage.

9. Challenges and Opportunities Ahead

The authors emphasise that AI plays a genuinely transformative role in industry processes. It can boost efficiency in credit decisions, insurance pricing, risk assessment, and fraud detection, allowing employees freed from manual routines to focus on strategic and customer experience–enhancing tasks. In this sense, AI is viewed as a fundamental reinvention of operational methods, rather than merely another IT tool. As EU AI regulation tightens, it becomes increasingly important for organisations to have strong foundations, such as codes of conduct, strategic AI guidelines, and risk management structures, since inadequate systems could raise the risk of non-compliance.

Finland’s financial sector displays an encouraging maturity in AI governance. Yet, consistent implementation remains a challenge, especially for smaller players without the resources to hire dedicated AI executives or maintain extensive oversight processes. As the EU AI Regulation elevates standards, those lacking robust frameworks risk potential non-compliance.

Ensuring data quality and minimising discriminatory outcomes are defining concerns for the sector. Despite ongoing improvements in algorithmic design and data oversight, organisations still grapple with balancing data minimisation requirements against the need for more granular information that helps surface bias.

Staffing, budgets, and technological infrastructure require continuous enhancement. While, according to the study, many financial institutions forecasted stable or increased AI spending, a significant minority of smaller firms remain cautious, particularly those unsure how AI integrates with their business models. As the technology becomes cheaper and more accessible, these barriers could subside, potentially democratising AI for smaller or specialised financial players.

10. Conclusion

The Finnish financial sector stands at the forefront of European AI adoption, with most participants either already using or planning to deploy advanced AI solutions, including generative models and machine learning. The sector’s enthusiasm is propelled by clear benefits: streamlined internal processes, cost reductions, improved customer experiences, more effective financial crime prevention, and stronger risk management capabilities. Larger organisations lead this transformation, but smaller entities are also catching up, albeit sometimes with more limited resources and governance structures.

Despite the positive, there are critical areas demanding greater attention. Ensuring high data quality and protecting personal information remain top concerns. Non-discrimination efforts highlight the need for stricter oversight and advanced technical solutions to detect and mitigate biases embedded in AI-driven processes. Robust governance frameworks – ranging from AI-specific codes of conduct and ethics to integrated risk management – are essential for consistent, sector-wide compliance with the EU AI Regulation and other relevant laws.

As AI continues to reshape industry strategies, institutions that demonstrably align innovation with principled governance will stand at a competitive advantage. By balancing innovation with prudent management and oversight, Finland’s financial industry offers an example of how technology can transform critical services while respecting fundamental regulatory and societal expectations.

The authors feel that the rapid adoption of artificial intelligence in Finland’s financial sector reflects both a desire to increase competitive advantage and pressure to meet the industry’s increasingly stringent regulatory requirements. The most evident benefits involve cost savings, more efficient internal processes, and enhanced customer experiences. At the same time, the authors emphasise that the responsible use of AI applications demands a clear governance structure, high-quality data, and active oversight to prevent discrimination. It is essential to combine innovation with sustainable, ethical risk management in a way that supports both business objectives and broader societal expectations.