How AI Is Helping Financial Services Companies in Belgium Cut Costs and Improve Efficiency

Belgian banks and insurers are moving fast from pilots to measurable value: the 2025 Belgian AI Barometer finds 85% of institutions now host an AI unit, over 60% have validated roadmaps and more than 60% are rolling out GenAI for customer service and coding assistance - so AI is already shifting daily workflows and productivity priorities in Belgium's financial sector (2025 Belgian AI Barometer report - Fintech Belgium).

Regulators and boards are watching closely because the technology brings efficiency gains alongside systemic and operational risks highlighted by the ECB's analysis of AI's benefits and vulnerabilities (ECB analysis of AI benefits and vulnerabilities), while employees call for far more hands‑on training.

For Belgian teams starting to harness GenAI safely, practical workplace upskilling - like Nucamp's Nucamp AI Essentials for Work bootcamp - turns strategy into usable skills, helping firms capture ROI without losing control of data, governance or customer trust.

“The changing environment of AI, encompassing traditional AI and the nascent GenAI and agentic AI, will unequivocally reveal which organizations possess superior data and enterprise architecture. This architectural strength will become the decisive competitive advantage for deploying AI services with optimal speed to market.” - Anthony Belpaire, Head of AI at BNP Paribas Fortis

Belgian banks and insurers are slashing back‑office costs by chaining together intelligent document processing (IDP), RPA and AI‑driven KYC so paperwork that once bottlenecked lending and claims becomes near‑instant fuel for decisions - imagine a Friday claim checked in real time rather than languishing in a Monday queue.

IDP+RPA speeds data extraction and straight‑through‑processing (STP) for loans and claims, generative models cut model‑training time for new document types from weeks to minutes, and workflow platforms and “copilots” stitch extraction to compliance checks and case management so human reviewers only see the 10–20% of complex exceptions.

Vendors report big wins: higher STP and No‑Touch rates, faster turnarounds and material cost reductions that let teams redeploy people into revenue‑generating work rather than repetitive data entry - a pragmatic recipe for Belgian firms balancing AML/KYC burdens and customer expectations (see Netcall: Automating back-end processes to match front-end customer experience and Tungsten TotalAgility workflow automation platform).

“By utilising AI, these tools can accurately extract data from a variety of documents – and populate it into an easy-to-interpret interface. This means – if an insurance claim is submitted on a Friday, instead of being added to a queue to be reviewed by a human worker on a Monday, the documents can be checked in real-time. If anything is missing, customers can be notified right away, even on the weekend.” - Pol Brouckaert, Director Netcall EU

Belgian financial teams can point to concrete, repeatable wins when justifying AI investments: SAS reports a global bank that cut customer‑complaint handling time by 20–40%, increased complaints managed by 20% and reduced costs by 8–15% after deploying SAS Viya, outcomes echoed in community write‑ups showing response time drops of 30–40% and complaint resolution improvements of 20–25% - savings that translate into fewer weekend backlog‑bottlenecks and faster service for busy Belgian customers (SAS: AI in Banking and SAS Viya case study, SAS Community article on complaints management with generative AI).

Other SAS case studies show complementary gains - Seacoast boosted risk‑adjusted revenue per customer by 30%, CIMB shrank time spent finding the right data from 80% to 20%, and Daiwa raised purchase rates 2.7x with analytics - evidence that Belgian banks and insurers can expect both cost reduction and top‑line lift when AI is tied to clear workflows.

For Belgian beginners, these figures make the “so what?” obvious: AI can turn manual queues into near‑real‑time decisions and reallocate staff from data drudgery to higher‑value customer work; practical pilots that mirror these SAS examples are a low‑risk way to prove value before broader rollouts (Guide to GenAI chatbots and customer service in Belgium (2025)).

“We want our customers to have peace of mind that they can access us, and we'll be there for them. Understanding the customer and streamlining their experience with the use of technology, including AI, is essential to our commitment.” - Osamu Hasegawa, Director of the Artificial Intelligence Office, Daiwa Securities

Belgian banks and insurers typically deploy AI by building central AI units and validated roadmaps, then stitching best‑of‑breed tools into hybrid stacks that pair cloud platforms and workflow engines with specialised partners - a pragmatic “pilot‑to‑scale” pattern seen across the market.

Frontline choices trend toward document‑first automation (IDP + RPA + workflow orchestration) for immediate cost takeout, while GenAI copilots and chatbots handle customer service and developer productivity; organisationally, this is implemented via competence centres, governance playbooks and external advisers who accelerate integration and compliance.

Vendors and partners mix global players and local specialists: enterprise cloud and Microsoft Copilot ecosystems, consulting firms that run scale playbooks, and niche vendors for IDP and orchestration - a landscape described in the FinTech Belgium 2025 AI Barometer report, PwC GenAI in Financial Services event coverage, and Cognizant Benelux Generative AI adoption insights.

“The changing environment of AI, encompassing traditional AI and the nascent GenAI and agentic AI, will unequivocally reveal which organizations possess superior data and enterprise architecture. This architectural strength will become the decisive competitive advantage for deploying AI services with optimal speed to market.” - Anthony Belpaire, Head of AI at BNP Paribas Fortis

Belgium's AI ecosystem is deliberately multi‑layered: a national

AI 4 Belgium

coordination effort sets ethics, skills and uptake goals while regions supply the cash, talent pipelines and testbeds that make AI practical for banks and insurers - see the European Commission's Belgium AI Strategy for the full picture (Belgium AI Strategy report - European Commission AI Watch).

Flanders backs an annual EUR 32M action plan (EUR 15M for company adoption, EUR 12M for basic research, EUR 5M for training and outreach) complemented by VLAIO and FWO research grants, Wallonia runs DigitalWallonia4.ai (≈EUR 18M/year) and the TRAIL‑funded ARIAC project (EUR 32M, 2021–2026) that plans ~50 PhDs, while Brussels channels Innoviris funding (a dedicated ~EUR 22M programme and roughly EUR 44M invested since 2017) into R&D and seed support.

Policy and funding aim to close the skills gap and unlock public data (Data.gov.be) while the national convergence plan stresses trustworthy deployment, cybersecurity and public‑private cooperation - a pragmatic backbone for Belgian financial firms aiming to scale pilots without reinventing the funding wheel (Belgium National Convergence Plan for AI - DIG.watch, Trustworthy AI guidance for Belgium - Act Legal).

The result: rapidly rising enterprise uptake (from ~13.8% to ~24.7% in 2023–24) and a real pipeline of funded projects that can move labs into live banking workflows.

Belgian banks and insurers face familiar AI hazards - bias, hallucinations, data leakage, supplier concentration and heightened cyber‑risk - but the policy and playbook to manage them is clearer than ever: the ECB's assessment of AI in the financial system flags systemic amplifiers like widespread adoption and a small number of dominant AI suppliers, plus concrete threats such as AI‑powered phishing and deepfakes that can scale attacks on customer channels (ECB analysis of AI benefits and risks in the financial system); practitioners therefore pair that vigilance with operational controls from the industry playbook - tight data governance, inventorying and monitoring of models, third‑party controls, and human‑in‑the‑loop checks.

Practical mitigations recommended by governance specialists include updating risk inventories, proactive legal and compliance engagement, regular model auditing and explainability work, and clear vendor management - summarised in Capco's guidance on GenAI governance (Capco: Five Foundational Elements for GenAI Governance in Financial Services), while platform vendors and architects stress that data governance must come first before GenAI experiments (Databricks: Simplifying Data Governance for AI-Driven Financial Services).

The upshot for Belgian firms: treat governance as a live programme - policies, monitoring, audits and a named accountability chain - so a convincing weekend deepfake can be caught by process rather than panic.

For Belgian beginners, implementation best practices mean turning high‑level rules into everyday habits: start with an AI readiness assessment, catalog models and data, and make data governance the non‑negotiable foundation so teams can interrogate lineage, quality and consent; then layer a lightweight AI committee or governance board that assigns clear owners, accountability and an audit trail for each use case so decisions are explainable and auditable - think of governance like a bank reconciliation for models.

Prioritise quick wins (IDP, KYC, customer copilots) under strict monitoring, produce artefacts such as model cards and DPIAs, and bake continuous validation and user training into deployment cycles; these steps map to the five‑part operational framework (readable guidance on lifecycle checks) and to Belgium's market reality where the EU AI Act and local roadmaps demand traceability and documentation (Sailpeak 2024 AI report on Belgian financial services).

Practical controls - project intake gating, vendor risk reviews, and regular audits - make compliance achievable without stifling pilots, so firms can scale responsibly while keeping regulators and customers confident (Crowe insight on AI governance in finance).

Effective AI governance requires broader stakeholder involvement and oversight. Strong data governance provides the essential foundation for ...

Begin with a short, practical roadmap: run an AI readiness assessment and inventory data and models, then pick two high‑impact, low‑risk pilots - think customer onboarding automation, intelligent document processing (IDP) or real‑time fraud detection - that mirror Belgian case studies and scale quickly; use the Euranova Belgian bank AI case study: asset‑liability uplift and fraud prediction as a template for pairing asset‑liability uplift with fraud models that “predict the likelihood of a transaction being fraudulent before it is completed”.

Build governance and documentation from day one (model cards, DPIAs, vendor reviews) and tap regional enablers and funding routes highlighted in the national AI strategy and the Sailpeak and FinTech Belgium 2024 AI in Financial Services barometer to reduce cost and risk when moving pilots to production.

Address talent gaps with targeted upskilling and simple metrics that matter in Benelux - productivity plus revenue impact - and follow industry guidance to reuse synthetic data and audit trails for safer testing (Avanade Benelux banking AI readiness and generative AI report).

The checklist: assess readiness, choose IDP/onboarding/fraud pilot, secure data governance, assign owners and KPIs, run short iterative sprints, and document results for regulator and board review.

“We want our customers to have peace of mind that they can access us, and we'll be there for them. Understanding the customer and streamlining their experience with the use of technology, including AI, is essential to our commitment.” - Osamu Hasegawa, Director of the Artificial Intelligence Office, Daiwa Securities

Belgian beginners should treat AI as a pragmatic toolkit: start small, prove value, and lock governance and data controls in from day one - pick two low‑risk pilots (IDP for onboarding or an RM “copilot” in private banking) that deliver measurable time savings and clearer audit trails, then scale the winners.

EY's guidance on augmenting Relationship Managers shows how automation can free hours of admin so advisers spend more time on strategic client conversations (EY: How to Leverage AI for Growth and Cost Efficiency in Private Banking), while the Sailpeak / FinTech Belgium Barometer makes the case that Belgium's market and funding landscape now supports rapid pilots and meaningful rollouts (Sailpeak: AI in Financial Services Report 2024).

Pair each pilot with a DPIA, model inventory and vendor checklist, assign owners, and commit to short sprints that produce regulator‑ready artefacts; the quickest “so what?” is operational time reclaimed for revenue‑focused work rather than data drudgery - often visible within weeks, not years.