Work Smarter, Not Harder: Top 5 AI Prompts Every Finance Professional in Netherlands Should Use in 2025

Finance teams in the Netherlands should treat AI prompting as a core skill: well-crafted prompts speed month‑end close, surface anomalies for AFM/DNB/DPA review, and help demonstrate EU AI Act readiness while preserving auditability.

Practical guides show that starting small - ask an LLM to produce outputs such as:

Summarize key financial highlights

Draft disclosure notes

These simple requests produce reliable lifts in productivity (and always review for accuracy).

For example, DFIN's guide demonstrates practical prompts and workflows: DFIN guide to AI prompts for financial reporting.

Prompt engineering is also emerging as a formal skillset that turns business questions into repeatable outputs - see Deloitte's coverage of prompt engineering for finance: Deloitte article on prompt engineering for finance.

In practice, a single, well‑specified prompt can eliminate hours of manual work and free teams to focus on scenarios and risk management - so investing in practical training like the AI Essentials for Work bootcamp pays off as a concrete pathway to safer, faster finance operations: AI Essentials for Work syllabus (Nucamp) and Register for AI Essentials for Work (Nucamp).

Selection of the Top 5 prompts started with a pragmatic, Dutch-first methodology: prioritize prompts that map to the highest-impact, repeatable tasks Dutch firms already use AI for (data analysis, administrative processes and process automation) while embedding governance and human‑in‑the‑loop controls that enable scaling.

That approach draws directly on Conclusion AI 360's playbook - workshops, Responsible AI Assessments and an AI Readiness model - to ensure each prompt is actionable, auditable and stakeholder‑tested (Conclusion AI 360 responsible AI assessments and AI Readiness model), and was cross‑checked against national statistics showing common uses and adoption barriers in the Netherlands (CBS AI Monitor 2024 Dutch AI adoption statistics).

Prompts were also filtered to reduce the fear‑of‑error risks highlighted in sector reporting - so choices favour small, verifiable outputs that accelerate month‑end tasks or flag anomalous items rather than automating end‑to‑end decisions, a measured strategy meant to turn pilot wins into organisation‑wide adoption.

“As soon as risks are involved, the threshold for implementation is high. But AI doesn't have to be an all-or-nothing story. With a step-by-step approach, clear frameworks and a grip on data, companies can take the next step safely and responsibly.”

Compliance monitoring in Dutch banks is narrowing from broad “unusual” flags to sharper, risk‑based detection: under the Wwft institutions must report unusual transactions to FIU‑NL “immediately (but in any case, within two weeks)”, and objective indicators such as cash transactions ≥€10,000 or transfers ≥€2,000 still trigger scrutiny, so monitoring rules should be tuned to those thresholds while preserving audit trails for DNB/AFM review.

Recent Dutch policy shifts aim to ease burdens on legitimate customers while raising barriers for criminals - measures include pilots for cross‑border data sharing, strengthened FIU‑NL powers to request freezes and closer coordination with law enforcement - and they sit alongside the incoming European AML package that will reshape national practice (see the Dutch government's letter to Parliament and the AFM's CASP annex for specific supervisory expectations).

Practically, this means transaction‑monitoring systems must combine clear, documented rules with the capacity to escalate high‑risk cases and to produce concise, auditable summaries for regulators - one small, well‑recorded alert can be the difference between a routinized follow‑up and a costly enforcement action.

For legal context and enforcement trends, consult the ICLG Netherlands AML chapter and the AFM guidance linked below.

Stress testing is the forward‑looking backbone of portfolio risk assessment under Basel III/CRR: run the severe-but-plausible scenarios that reveal whether capital and liquidity hold up when markets turn, and make sure every assumption, data feed and model step is auditable for supervisors like DNB and AFM. The BIS summary reminds practitioners that stress tests complement day‑to‑day risk metrics by exposing vulnerabilities at both the portfolio (microprudential) and system (macroprudential) levels, while Basel guidance and codes of practice demand clear governance, documented methodology and proportionality in scenario choice (BIS FSI executive summary on stress testing under Basel III/CRR).

Practical implementations should marry bottom‑up loan‑level shocks with top‑down macro scenarios, include reverse stress tests to find breaking points, and prioritise a single, reconciled data source - exactly the centralised, documented approach experts argue is needed to meet Basel expectations and turn stress testing from a compliance chore into actionable risk management (SAS guide to business-driven stress testing and data consolidation for risk management).

Think of scenario narratives as scripts: a missing assumption can make results uninterpretable to auditors, so keep scenarios severe, explainable and repeatable.

For Dutch insurers chasing faster, auditable operations, a pragmatic process‑optimization blueprint pairs targeted AI agents with clear, rule‑based workflows: start by automating intake, policy verification, data extraction and decisioning for low‑risk motor claims, then escalate the complex cases to human adjusters.

Real‑world results from a leading Netherlands insurer show the payoff - Beam's agentic approach automated 91% of eligible motor claims, cut average processing time by 46% and lifted NPS by 9% - a change that translated to thousands of routine decisions handled without manual touch and faster resolutions for customers (Beam case study: Dutch insurance claims processing with AI agents).

Practical design choices matter: constrain automation to clear‑coverage, two‑party claims below a threshold, keep explainability and audit trails built into every step, and use modular workflow tools so integrations stay light and scalable (Beam insurance claim AI agents use case).

Complement these technical fixes with process mapping and governance so automation becomes a reliability and compliance win, not a black box experiment - shorter cycles, fewer errors, and staff redeployed to high‑value customer work are the measurable outcomes.

Regulatory mapping is the practical first step for Dutch finance teams that want to move from experimentation to audit‑ready AI use: inventory every AI system, classify it against the Act's four risk tiers, and document the decisions so AFM, DNB and the DPA can see the rationale if asked.

The EU AI Act applies to providers and deployers inside and outside the EU when outputs are used in the EU, treats chatbots and generative tools as “limited‑risk” (transparency required), and reserves the heaviest duties for Annex‑listed high‑risk systems - think detailed technical documentation, human‑oversight, logging and, for many cases, registration in the EU database.

Use tools like the EU AI Act Compliance Checker to quickly spot whether a model is likely in scope, and follow practical legal summaries such as Lowenstein's guide to the Act to map who in the organisation must own each compliance step.

Timelines are phased, so prioritise audits of anything touching credit, underwriting, claims automation or recruitment: a small missed inventory line can turn a harmless prototype into a high‑risk obligation overnight.

“The AI arms race has accelerated rapidly in the last year. It appears this Act is trying to establish some guardrails that intend to eliminate or at least reduce the risk of harm to both businesses and private citizens,”

EU AI Act Compliance Checker - Model Scope Assessment Tool

Lowenstein Client Alert: The EU Artificial Intelligence Act of 2024 - What You Need To Know

Upskilling and knowledge transfer are the linchpins that turn promising pilots into audit‑ready practice across Dutch finance teams: Conclusion's AI 360 Academy offers a tailored, Dutch‑language e‑learning route that explicitly maps to the EU AI Act's Article 4 requirements and trains executives, risk/compliance staff and everyday users so everyone speaks the same, auditable language on models and prompts - a practical approach that helps teams produce the documentation supervisors expect and embed prompt engineering as a repeatable skill.

Complement this with L&D best practices - use AI to generate drafts and quizzes but always involve subject‑matter experts, validate outputs, and build prompt test cases as 360Learning recommends - to keep content accurate, engaging and defensible in AFM/DNB reviews.

For teams in the Netherlands, pairing a role‑based curriculum with living knowledge hubs and clear training records shortens the path from experimentation to regulated deployment and makes compliance a tangible, organisation‑wide capability (Conclusion AI 360 Academy - tailored AI training, 360Learning - best practices for AI‑generated content in L&D).

Ready-for-action finance teams in the Netherlands should close the loop between governance and practice: start by inventorying AI systems and classifying each against the EU AI Act risk tiers, fold GDPR DPIAs into your conformity checks (see practical overlap guidance in Grant Thornton's briefing on the GDPR and AI Act), and prioritise pilots with human‑in‑the‑loop controls so regulators like DNB, AFM and the AP see clear audit trails; remember that one missed inventory line can turn a harmless prototype into a high‑risk obligation overnight.

Parallel moves: upskill staff on prompt engineering and compliance workflows (consider the AI Essentials for Work syllabus), and plan supervised tests via the national regulatory sandbox launching by Aug 2026 to validate high‑risk controls in a safe environment.

For Dutch firms that sit at the intersection of tech and finance - from local banks and insurers to semiconductor leaders such as ASML - these steps turn regulatory risk into operational resilience and make AI use a documented, repeatable advantage rather than an enforcement headache.