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

Belgium's financial sector is at a regulatory and technological crossroads: supervision by the National Bank of Belgium (NBB) and the Financial Services and Markets Authority (FSMA) means banks, insurers and investment firms must balance operational gains from AI with strict rules on outsourcing, data protection and market conduct, and the EU's new AI Act adds layered obligations and heavy fines for non‑compliance.

Practical concerns - from cloud outsourcing checklists prepared for NBB/FSMA oversight to the AI Act's high‑risk rules covering credit scoring and underwriting - make governance, human oversight and documented risk management non‑negotiable; see the NBB and FSMA compliance checklist for Belgian financial services and Goodwin's briefing on EU AI Act obligations for financial services (Goodwin briefing).

Upskilling non‑technical staff to run safe pilots and write robust prompts is a fast, practical step - outline training is available in the AI Essentials for Work bootcamp - AI training for workplace - and a single misconfigured model can cost reputational damage as fast as a multi‑million euro penalty.

Methodology: selection focused on measurable impact, regulatory sensitivity and quick learnings for Belgian financial teams - we prioritized high‑value, high‑risk workflows (credit scoring, fraud/AML, regulatory requests and reporting) where pilots can move from “trending” signals to “realized” value; selection criteria were drawn from BCG's playbook to focus on value, embed GenAI into transformation and scale in sequence (BCG's ROI guide), the two‑part Trending vs Realized ROI framework from Propeller to set short‑term KPIs and mid‑term payback triggers, and Devoteam's governance and KPI checklist to ensure audit trails, adoption metrics and model monitoring were baked into each prompt.

Prompts were scoped for Belgian constraints - multilingual outputs, NBB/FSMA vendor and data rules, and GDPR‑safe testing - and stress‑tested as small pilots with clear baselines, dashboards and go/no‑go scaling criteria to avoid the common trap of high investment with low return.

Read more in BCG ROI guide on AI transformation and ROI, Propeller Trending vs Realized ROI framework and Devoteam governance and KPI playbook.

Measuring results can look quite different depending on your goal or the teams involved. Measurement should occur at multiple levels of the company and be consistently reported. However, in contrast to strategy, which must be reconciled at the highest level, metrics should really be governed by the leaders of the individual teams and tracked at that level. - Molly Lebowitz, Propeller Managing Director

Multilingual chatbots that reply fluently in Dutch, French, German and English are one of the fastest ways Belgian banks and insurers can raise customer satisfaction while keeping call‑centre costs in check - but they must be built with regulation front‑of‑mind: under the EU AI Act most support bots are “limited risk” and must clearly inform users they are interacting with AI (transparency rules effective 2 Feb 2025), while any assistant that crosses into financial advice can be treated as high‑risk and requires detailed documentation and human oversight; see the practical rundown of chatbot classifications and deadlines in this EU AI Act: Chatbots classification and compliance guide.

Operationally that means localized disclosure messages, language‑specific escalation paths to human agents, strict audit logs for each conversation and vendor/cloud checks aligned with NBB/FSMA supervision - guidance and a compliance checklist for Belgian financial institutions is available from regulators and partners via the NBB/FSMA resource page (NBB and FSMA compliance checklist for Belgian financial services).

The “so what?” is simple: a bot that can't prove language‑aware transparency and human‑in‑the‑loop controls risks regulatory fines in the millions and reputational damage long after a pilot was supposed to scale.

Belgian banks and insurers should treat real‑time fraud and AML as both a compliance imperative and a customer‑protection win: HSBC's AI programme now screens over 1.2 billion transactions a month, detecting 2–4× more suspicious activity while cutting alerts by about 60% and speeding detection to eight days after the first alert, a concrete efficiency gain that Belgian teams can emulate with strict audit trails and human‑in‑the‑loop controls (see HSBC AI money laundering detection case study HSBC AI money laundering detection case study).

Agentic AI patterns - autonomous detectors that can immediately hold a payment, trigger a case team, and auto‑generate SARs - are emerging as the practical way to close the gap between flagging and stopping fraud, but they must be deployed with explainability, GDPR‑safe data pipelines and NBB/FSMA‑aligned vendor checks; Wipro's review of AI in fraud/AML outlines frameworks for real‑time monitoring and governed agentic response (Wipro fraud and AML AI frameworks for real‑time monitoring).

The ASIC challenge against HSBC Australia is a sharp reminder: lacking real‑time interception let criminals “drain customers' accounts,” so Belgian teams should prioritize fast, auditable interception rules and clear escalation paths to avoid regulatory and reputational damage (ASIC legal challenge against HSBC Australia over customer account drainage).

"Ayasdi claims to have unearthed many new cases and patterns directly correlated to fraud – as well as reducing HSBC's false positives (cases when HSBC's existing rules would have flagged for laundering risk when no such risk actually existed) by 20%."

Belgian lenders can materially sharpen underwriting by combining traditional bureau inputs with alternative signals - from bank account flows and open‑banking transaction histories to digital footprints and utility or rental payment patterns - to better assess applicants who lack a long credit history; a field study shows that adding retail/transaction data can raise approval rates for people without credit records from 16% to between 31% and 48% (SSRN paper Who Benefits from Alternative Data for Credit Scoring), a vivid reminder that more data can mean more responsible access.

Practical paths to deploy this safely include partnering with vetted alternative‑data vendors (see a curated list of providers and data types in the RiskSeal roundup of top alternative data providers for credit) and validating models with privacy‑preserving synthetic datasets so testing never exposes live customer files (see the Nucamp guide on using synthetic data for compliant testing - AI Essentials for Work syllabus).

For Belgian teams the operational checklist should include documented data provenance, GDPR‑safe pipelines, auditor‑friendly explainability and conservative fallbacks so models extend credit responsibly rather than replacing human review.

Agentic automation for loan onboarding turns a tangled, document‑heavy journey into a structured, auditable workflow: a supervisor agent delegates to data‑extraction, validation, compliance and underwriting sub‑agents that pull text from pay‑stubs and bank statements, cross‑check scores, apply lending rules and either auto‑issue a pre‑approval or flag an exception for human review - effectively turning weeks of backlog into minutes of processing in many cases (see the Amazon Bedrock mortgage example for the full agentic IDP pattern).

For digital lending the same Bedrock Agents pattern handles KYC, credit checks and email notifications end‑to‑end (the DigitalDhan reference shows how agents orchestrate PAN/Aadhaar verification, risk scoring and loan creation), while AWS's production guidance on AgentCore and Bedrock outlines the identity, observability and governance features needed to run these agents safely at scale in regulated environments.

For Belgian banks and insurers, that means a repeatable playbook: automate routine approvals, bake in a compliance agent and RACI handoffs for exceptions, and keep immutable logs so auditors and supervisors can trace every decision with human review steps clearly marked - so automation accelerates throughput without outsourcing accountability.

Regulatory‑compliance automation is the practical backbone Belgian financial teams need to turn EU rules from a checklist into daily operations: automated AI inventories, risk classification, immutable conversation and event logs, and ready‑to‑export technical documentation let banks and insurers answer regulator requests quickly and with audit‑grade evidence rather than ad‑hoc paper trails.

The EU AI Act sets a clear risk‑based framework with phased deadlines and transparency, human‑oversight and post‑market monitoring obligations for high‑risk systems (see the European Parliament's EU AI Act overview), and financial services firms face sector‑specific expectations to document data quality, explainability and governance before deployment (Goodwin's briefing covers implications for banks and insurers).

Extra attention is needed for registration, conformity assessments and serious‑incident reporting for Annex III use cases; non‑compliance carries steep penalties and extraterritorial reach (see the ModelOp summary of obligations and fines).

A simple operational example makes the point: when a supervisor asks for a model's training logs, an automated compliance pipeline can deliver them in minutes - or, without automation, trigger costly investigations and regulator notices that ripple through credit, AML and outsourcing reviews.

Document processing and automated financial reporting are becoming operational necessities in Belgium: the shift to mandatory structured e‑invoicing via Peppol and the EN 16931 standard (effective 1 Jan 2026) turns invoices into machine‑readable feeds that drop manual keying and speed reconciliation, while public‑sector digitisation shows scale - IRIS's automated VAT and tax‑form pipeline scans in regional centres (Ghent and Namur) and was designed to handle up to a million pages a day, proving the throughput possible when OCR, IDP and workflow engines are combined; see the ClearTax e‑invoicing in Belgium guide ClearTax guide to e‑invoicing in Belgium and IRIS automated tax form processing case study IRIS automated tax form processing case study.

Modern IDP platforms (for example, SER/Doxis) layer advanced OCR, NLP and validation so banks can auto‑ingest invoices, extract line‑items, generate audit‑grade trails and push zero‑touch entries into ERPs - turning PDF mountains into searchable data lakes while reducing errors and improving VAT and regulatory reporting timeliness; learn how Doxis links capture to compliance in the SER Doxis intelligent document processing overview SER Doxis intelligent document processing (capture to compliance).

The practical “so what?”: one well‑tuned OCR/IDP chain can convert back‑office drag into same‑day closing and audit exports, provided capture quality, legacy integration and GDPR controls are solved up front.

“We are processing much more than we used to with the same amount of people. We can do more without hiring people and follow up many more quotations.”

Treasury teams at Belgian banks can move from retroactive reporting to proactive balance‑sheet steering by pairing traditional ALM with AI‑driven, real‑time analytics: the European Central Bank's liquidity framework shows why this matters - euro‑area excess liquidity stood at 2,654,267 (EUR millions) as of 2025‑09‑03, and central banks manage markets via open‑market operations that directly affect short‑term rates (ECB daily liquidity framework and analysis).

Modern Asset‑Liability Management embraces dynamic stress‑testing, integrated credit and liquidity modelling, and continuous risk simulations so institutions can reprice funding, assess intraday squeezes and test multi‑factor scenarios quickly; Wolters Kluwer's ALM overview highlights how data quality, unified taxonomies and AI‑enabled stress tests turn ALM into a strategic decisioning platform (Wolters Kluwer ALM and liquidity challenges overview).

Practical next steps for Belgian teams include fixing data lineage, validating models with GDPR‑safe synthetic sets, and instrumenting fast, auditable dashboards that supervisors can query on demand (GDPR-compliant synthetic data for model validation and compliant testing).

Belgian banks and insurers can turn AI-driven personalization into a real competitive lever by surfacing tightly targeted product recommendations and dynamic pricing exactly when customers need them - think mortgage or insurance suggestions that arrive the moment a customer is shopping for a home or booking travel - using real-time analytics and a unified customer view rather than broad segments; Luxoft – Hyper-Personalization in Banking (tailored recommendations and robo-advice), while FICO – Unlocking Hyper-Personalization with Applied Intelligence (scalable, auditable decisioning) explain the applied-intelligence architectures that make individualized decisioning and pricing scalable and auditable.

The upside is measurable - lower acquisition costs and higher cross-sell rates reported in industry studies - but Belgian teams must balance that revenue upside with privacy and trust:

"Personalization paradox" warns that overly aggressive offers can feel creepy. The Personalization Paradox - Banks, Privacy and Trust (The Financial Brand)

So GDPR-safe consent models, explainable pricing logic and clear opt-outs are mandatory to keep customers and supervisors comfortable.

Synthetic data is rapidly becoming the safest shortcut for Belgian banks and insurers to train and validate models without touching live customer files: by design it mimics transaction patterns and edge‑case fraud scenarios while avoiding direct personal identifiers, and when paired with differential privacy it can even offer a mathematical proof of protection (see practical GDPR guidance at Synthetic data GDPR guidance - Labelvisor).

But regulators and auditors expect evidence, not buzzwords - Recital 26's anonymisation threshold and repeated warnings that

“a dataset that looks synthetic may still behave like the original”

mean teams must run re‑identification tests, keep generation provenance and model cards, and adopt hybrid workflows that pre‑train on synthetic data then fine‑tune on carefully governed samples (detailed compliance tradeoffs are explained in EM360Tech's guide Is synthetic data GDPR compliant - EM360Tech guide).

For Belgian use cases - fraud simulation, AML stress tests and credit‑scoring prototypes - the practical playbook is simple: choose platforms with built‑in privacy metrics, validate synthetic performance against real benchmarks, and document every DPIA and audit trail so innovation scales without creating a costly regulatory surprise (see finance‑focused examples and validation practices at Synthetic data privacy validation practices - Datahub Analytics).

Belgian banks and insurers should treat AI-augmented cybersecurity as an enabler, not a gatekeeper: EY's research shows that integrating AI into detection, response and recovery can slash mean time to detect and respond by roughly 28% and let security teams move from firefighting to enabling safe AI rollouts across the business, freeing capacity to support product teams and speed pilots to market - Secure Creators report detection/response improvements of over 50% and even save more than 150 days on average in breach handling.

Practical steps for Belgium include consolidating tool sprawl, prioritising early CISO involvement in AI projects, and pairing agentic automation for high-volume, low-judgment tasks with human review to manage adversarial risks such as prompt injection and data poisoning; see EY's playbook on transforming cybersecurity to accelerate AI value and the EY study on agentic AI for rapid, auditable scaling of detection and response.

These moves not only reduce operational cost (median annual savings from simplification and automation is around US$1.7m) but also build the trust supervisors and customers demand, turning security into a competitive asset rather than a compliance burden - so one well-orchestrated AI defence can be the difference between a contained incident and a cascading regulatory headache.

“We ingest more than 10 billion data events each day… We wouldn't be able to manage that volume without ML and AI.” - Gajan Ananthapavan, Global Head of Security Operations, Intelligence and Influence, ANZ Bank

Belgian financial teams ready to turn momentum into measurable value should follow a short, practical checklist: pick 1–2 high‑impact, regulator‑sensitive pilots (credit decisioning, AML alerts or e‑invoicing), measure outcomes with disciplined ROI metrics and dashboards, and bake governance and human‑in‑the‑loop controls into every sprint - advice echoed by PwC's GenAI event on scaling pilots into production PwC GenAI in Financial Services report (Belgium).

Prioritise data quality and integration early (the 2025 Belgian AI Barometer shows 85% of institutions now have dedicated AI units but cites integration complexity and data readiness as top barriers), and reduce risk by using synthetic data for validation, clear DPIAs, and diverse vendor contracts to avoid supplier concentration.

Pair top‑down sponsorship with frontline ownership - Oliver Wyman's compliance playbook recommends executives enable tools while teams design use cases and retain final decision authority - and close the skills gap with focused workplace training (for example, Nucamp's AI Essentials for Work course trains non‑technical staff to run safe pilots and write robust prompts Nucamp AI Essentials for Work course).

The “so what?”: with measured use cases, governance by design and targeted upskilling, pilots stop being experiments and start delivering sustainable ROI.

“The 2025 AI Barometer paints a clear picture: AI is no longer something futuristic, but a core component of strategic growth for Belgian financial institutions.” - Raf De Kimpe, CEO, FinTech Belgium