Top 10 AI Prompts and Use Cases and in the Financial Services Industry in Netherlands

AI is shifting from experiment to enterprise across Dutch financial services: regulators have set clear expectations (soundness, accountability, fairness, skills and transparency) and the DNB and AFM are expanding oversight as the EU AI Act arrives - see the overview of Dutch regulation and the 2019 DNB principles and joint report with AFM for context (AI and financial regulation in the Netherlands: overview of regulation).

Adoption is accelerating too: the CBS AI Monitor shows a notable rise in use of text mining and natural language generation in 2024 and that financial services are among the sectors most likely to deploy AI (Dutch AI Monitor 2024 - use of AI technology by Dutch companies).

At the same time, practical GenAI wins are emerging - Deloitte's Gen AI for Finance event highlighted NN's tailored ChatGPT saving an average of 3.5 minutes per call (an efficiency gain that scales into millions of minutes annually), illustrating why upskilling and disciplined deployment matter for risk-aware value capture (Deloitte: Generative AI for Finance insights).

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“Artificial Intelligence is reshaping how finance operates, makes decisions, communicates, and drives enterprise value.”

Selection of the Top 10 use cases was driven by practical impact in the Netherlands and the specific risks regulators and stability authorities have called out: priority went to applications that deliver measurable efficiency or risk reduction (fraud/AML, liquidity forecasting, reconciliations, contract and procurement workflows) while aligning with Dutch principles of soundness, accountability, fairness, skills and transparency.

Criteria included regulatory alignment (following the joint DNB–AFM report on the impact of AI on the financial sector and supervision), systemic-risk sensitivity (supplier concentration and operational dependence flagged by the ECB Financial Stability Review special article on supplier concentration and operational dependence), data-quality and explainability constraints, and clear ROI for front- and back-office users.

Use cases that reduce the kinds of harms regulators worry about (discrimination, data leakage, overreliance on a few vendors) were favoured, and example prompts were written to keep models in support roles - safe, auditable, and traceable - reflecting Dutch firms' current cautious approach to generative systems noted in the regulatory overview (overview of AI and financial regulation in the Netherlands).

A telling benchmark: because over a fifth of phishing activity already targets finance, cyber-resilience and prompt designs that limit sensitive data exposure were non‑negotiable.

Dynamic fraud detection and AML monitoring are increasingly urgent for Dutch financial institutions: online fraud affected 9.4% of Dutch residents in 2024, while scams cost the Netherlands an estimated €1.75 billion that year (about 0.2% of GDP), so defenses must catch social‑engineering and APP scams that target people more than systems (BioCatch report: Netherlands digital payment fraud statistics).

Modern approaches pair behavioral intelligence and machine learning with cross‑industry data sharing - ThreatMark argues that adaptive, device‑and‑behavior signals plus smarter collaboration can reveal mule networks and voice‑cloning scams (nearly 10,000 scam calls reported in Q1 2025) while reducing customer harm (ThreatMark analysis: smarter collaboration to fight scams in the Netherlands).

Home‑grown ML vendors are already showing better detection with fewer false positives, improving both compliance and customer experience - as the Dutch Security TechFund noted when backing Fraud Dynamics' automated, anomaly‑driven models (Security TechFund backs Fraud Dynamics for automated anomaly‑driven fraud detection).

The practical “so‑what”: combining behavior‑based scoring, dynamic risk algorithms, and trusted intelligence sharing can cut investigation time, lower reimbursements, and make fraud a manageable operational risk for Dutch banks.

“Accommodating regulation changes should not be just a tickbox exercise - it's an opportunity to take control of our own destiny. It's important to proactively embrace the changes.”

Predictive cash‑flow and liquidity forecasting turn guesswork into a repeatable discipline for Dutch finance teams: combine an operational cash‑flow forecast (listing every inflow and outflow), rolling forecasts, and scenario analysis so monthly cash positions are visible and stress‑tested against slow-paying customers or sudden tax bills - the Netherlands Chamber of Commerce recommends monthly or quarterly cash‑flow forecasts to reveal when extra funding is needed (how to write a financial plan).

Practical models mix bottom‑up driver forecasts with top‑down scenarios to calibrate funding needs and inform decisions like extending supplier terms or offering early‑payment discounts (EY's modelling guidance explains these approaches).

Automation and integrated feeds make forecasts live: extract receipts with OCR, reconcile transactions, and centralise multi‑currency balances so FX drag no longer surprises your liquidity line - a readiness that matters when more than 40% of Dutch businesses struggle with late or unpaid invoices and over 4,000 firms hit insolvency in 2024 (Airwallex's guide shows why calculating cash flow regularly and trimming FX costs matter) (complete guide to cashflow management for Dutch businesses).

The practical payoff is simple: a forecast that flags a shortfall days or weeks early lets treasury act - reprice, delay, or draw contingency credit - turning panic into a planned, low‑cost fix (see cash‑forecasting best practices from J.P. Morgan for mid‑sized firms).

“We enjoy better exchange rates than traditional banks, with no hidden fees. With access to over 60 trade currencies including USD, HKD, and SGD, we have significantly reduced our transaction costs across the US, Hong Kong, and Singapore.”

Automated transaction capture and reconciliations turn the accounts-payable bottleneck into a predictable pipeline: AI-powered OCR extracts header fields and detailed line items, NLP interprets descriptions and tax codes, and matching logic completes 2‑way/3‑way reconciliations so invoices flow straight into ERP systems instead of inboxes - cutting what once took

about 17 days

of manual work down to hours or minutes in many deployments.

Benchmarks show extraction accuracy and resilience vary by document quality and fine‑tuning, so Dutch firms should test vendors on real invoices (see the invoice OCR benchmark for tool comparisons) while demanding layout‑agnostic models and human‑in‑the‑loop correction.

Best‑practice solutions combine line‑item extraction, anomaly detection and validation rules (VAT, PO, currency) to raise straight‑through processing rates and reduce costly errors; they also bake in GDPR‑minded controls and encryption to keep supplier data private.

The practical payoff is immediate cash‑management clarity and fewer supplier disputes - one misread line item can no longer derail a payment run when systems reliably extract, validate and reconcile at scale.

Invoice OCR benchmark • AI invoice OCR and line‑item extraction

Intelligent financial commentary and an accelerated close are no longer nice-to-haves for Dutch finance teams - they're practical levers to meet tighter supervisory expectations while freeing people for strategy: purpose-built systems like BlackLine's Verity promise to automate reconciliations, draft variance explanations and generate audit‑ready narratives so the month‑end isn't a scramble but a managed cadence (BlackLine Verity: Trusted AI for the Office of the CFO); agentic platforms take that further by running cross‑system reconciliations, flagging exceptions and producing board‑ready commentary in seconds, with real ROI - some vendors report finance teams reclaiming 150–300 hours per month and shrinking close cycles from days to hours (Concourse AI Agents for CFOs).

For Dutch banks and corporates juggling regulatory transparency and tight audit trails, this means cleaner controls, repeatable narratives, and faster decisions - imagine walking into a board meeting with a concise, sourced explanation for every surprise instead of a stack of spreadsheets.

The practical payoff: higher confidence in numbers, fewer post‑close adjustments, and more time to translate results into action.

“We do not aim to be the smartest AI model. Instead, we aim to be the most trusted platform for the Office of the CFO by delivering future-ready financial operations through agentic AI that goes beyond automation to empower our customers to anticipate, decide, and act faster.”

Dutch financial firms should move from ad‑hoc caution to organised readiness: the EU AI Act imposes staggered obligations (from transparency and AI literacy already rolling out to provider rules for general‑purpose models and the later high‑risk conformity gates), so maintaining an inventory, clear governance, and GDPR-aligned DPIAs is now a board‑level task - see Deloitte's practical breakdown of timelines and provider/deployer duties for the Act (Deloitte EU AI Act practical breakdown and timelines).

National enforcement is already taking shape: the Autoriteit Persoonsgegevens is positioned as a key supervisor and Dutch authorities are publishing updated guidance that local teams must track closely (Pinsent Masons Netherlands AI Act guidance and enforcement update).

For product and model risk, the planned regulatory sandbox gives firms a practical path to test controls under supervision before high‑risk rules bite - the sandbox will centralise supervised testing and help translate EU rules into implementable checks for AML, credit scoring and model governance (Netherlands AI regulatory sandbox launch expected by 2026 analysis).

The so‑what is concrete: a short, audited risk register plus one sandboxed test can convert an amorphous compliance headache into a repeatable evidence pack for auditors and supervisors, avoiding multi‑million euro fines and reputational fallout.

“the definitive sandbox starts at the latest in August 2026,”

Efficient contract and supplier management in Dutch financial services increasingly relies on GenAI to turn messy contract troves into actionable insight - automatically extracting payment terms, renewal clauses and liability allocations so procurement, legal and treasury teams can spot risk and value at a glance; Conduent's overview shows how GenAI-driven contract analytics can slash manual review time and surface negotiation levers (Conduent GenAI procurement contract analytics overview).

Practical steps for Netherlands-based firms include starting with a Netherlands‑compliant supplier template and clear IP/audit terms - Genie AI publishes a ready Standard Supplier Agreement for the Netherlands that covers pricing, quality and risk allocation (Genie AI Netherlands Standard Supplier Agreement template) - and using a vendor procurement checklist to secure audit rights and liability protection before integrating models (Dutch financial services vendor procurement checklist for AI model integration).

The payoff is concrete: faster renewals, fewer surprises at audit, and a searchable contract “scoreboard” that turns buried clauses into negotiation ammunition.

“It's not necessarily the models, it's really the data,” Lee told Data Management Insight.

Augmented knowledge bases and agent enablement let Dutch bankers and advisors turn internal playbooks, pricing rules and customer signals into timely, audit‑ready recommendations: tools like EY's Smart Advisor show how AI can leverage a bank's own data to produce valid insights for relationship managers to structure and price deals (EY: Using AI to augment pricing intelligence for banks).

Coupling that pricing intelligence with an augmented core and agentic assistants creates the kind of seamless, personalised interaction Finastra describes - where product ideas, compliance checks and customer context surface together so advisors can respond instantly without hunting through silos (Finastra: Augmented banking - the next frontier).

For the Netherlands this is practical: local teams can deploy curated knowledge graphs and governed agents to speed client conversations while building clear data‑ownership and explainability guards so automation scales without eroding trust.

The memorable payoff is simple - instead of paging through folders, an advisor sees a sourced pricing option and the right controls alongside the client record, turning time‑consuming research into a concise, actionable moment.

Conversational assistants are becoming a fast route to faster, fairer customer resolution in the Netherlands: pilots and rollouts show they don't just cut queues, they reshape the customer journey so simple requests are solved instantly and humans handle the tricky, high‑value work.

Dutch evidence is compelling - ING's Netherlands pilot served about 20% more customers in early weeks while locking in strict guardrails to avoid risky advice (ING GenAI customer service case study) - and Lunar's GenAI native voice approach demonstrates how voice‑native models can handle natural interruptions and offer 24/7 personalised help, targeting a future where a large share of routine calls are contained by AI (Lunar GenAI Native Voice Assistant).

For Dutch banks and insurers the practical win is immediate: lower average handle time, higher containment, and a smoother handoff with context so customers avoid repeating details - turning a frazzled midnight call into a calm, guided resolution and freeing agents for relationship work that actually grows loyalty.

“The GenAI Native Voice Assistant will help make banking more accessible to all demographics, no matter the age, background or financial situation.”

AI is rewiring how Dutch deal teams find and close opportunities: relationship‑intelligence platforms and AI agents surface warm intro paths and rank targets so a small team can evaluate many more companies - indeed one study notes AI can spot roughly 195 relevant companies in the time a junior analyst evaluates a single name - turning scattered signals into a predictable pipeline.

Practical wins for Netherlands‑focused M&A include automated enrichment and relationship scoring (platforms like Affinity map firm-wide contacts and warm paths to targets), secure VDR workflows that slash due‑diligence timelines (SmartRoom reports up to ~30% faster diligence with AI‑enhanced document triage), and integrated deal tracking that respects GDPR and enterprise security standards so sensitive bid data stays protected.

For mid‑market funds and PE teams in Amsterdam and beyond, the result is faster outreach, higher response rates, and fewer missed opportunities - while predictable, auditable AI steps (enrichment, scoring, red‑flag extraction) make it easier to demonstrate controlled processes to auditors and supervisors.

The tangible “so‑what”: more qualified meetings booked, shorter time‑to‑offer, and lower intermediary fees because firms can leverage their own networks and data to win deals before they hit the broader market.

Tool / Category	Practical Benefit
Affinity relationship intelligence platform	Automatically identifies warm introduction paths and scores relationships to boost conversion
SmartRoom secure virtual data room (VDR) for private equity	AI document triage and secure workflows can cut due‑diligence timelines by ~30%
DealRoom (deal tracking)	Integrated deal rooms and secure document exchange with Amsterdam presence for EU deals

“If you can run scenarios quicker, you can run more of them.”

For Dutch financial teams, strategic spend insights are the difference between reactive procurement and a confident, controllable cost base: AI-powered spend analysis automates the extract–cleanse–classify cycle so data prep can fall by up to 90% and savings opportunities surface 3–5x faster, turning messy ledgers into prioritized actions and measurable negotiation leverage (Sievo spend analysis guide for procurement teams).

Start by locking down a practical spend taxonomy and automating classification so tail‑spend and maverick buying are visible rather than hidden - maverick patterns can quietly erode margins and responsiveness unless flagged by real‑time controls and approval flows highlighted in Zycus's approach to stopping rogue purchases (Zycus guide to stopping maverick buying in procurement).

In the Netherlands, where regulatory and ESG demands add complexity, the immediate wins come from supplier consolidation, payment‑term optimisation and using peer benchmarks to strengthen negotiation posture; modern platforms don't just show problems, they propose quantified next steps and conversational queries so category managers can act within weeks, not quarters (Suplari 14 tips for using spend analysis in procurement).

The memorable payoff: a single, trusted spend view that turns fragmented invoices into one-click initiatives - faster savings, fewer surprises at audit, and procurement finally operating at the speed of the business.

Start small, but start deliberately: Dutch financial institutions should begin with an AI inventory and an AI/data readiness assessment to map what's in use, which systems qualify as high‑risk, and where GDPR and the EU AI Act intersect - Grant Thornton's practical breakdown explains why readiness spans technology, data, customer acceptance and

compliance readiness

(including the conformity assessments required before placing high‑risk systems on the market) (Grant Thornton AI readiness guide for financial institutions).

Use the EU AI Act tools to classify systems and test obligations early (EU AI Act compliance checker tool), then pick quick, low‑risk pilots (marketing, reconciliations, or agent‑assisted customer flows) to build skills and evidence.

For Dutch teams, the practical payoff is tangible: one short, audited risk register and a sandboxed test turns an amorphous compliance headache into a repeatable evidence pack for auditors and supervisors - and upskilling options like the AI Essentials for Work course can get teams prompt‑writing and governance-ready fast (AI Essentials for Work course registration (Nucamp)).

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