Top 5 Jobs in Financial Services That Are Most at Risk from AI in Belgium - And How to Adapt

Belgian financial services can't treat AI as a distant tech trend - adoption jumped from 13.81% in 2023 to 24.71% in 2024, and the EU's risk‑based AI Act already singles out finance-relevant use cases (credit scoring, pricing, fraud detection) as potentially high‑risk, bringing strict governance, logging and human‑oversight rules plus heavy fines (up to €35M or 7% of turnover) if compliance fails; see the practical Belgium briefing on Trustworthy AI in Belgium briefing (Actlegal) and the evolving EU AI Act national implementation plans that show Belgium still finalising competent authorities and has no national sandbox yet.

That combination of fast uptake, legal exposure and unclear national oversight makes a clear priority: map models today, strengthen model governance, and reskill staff - practical short courses such as Nucamp's Nucamp AI Essentials for Work bootcamp (15-week syllabus) are one concrete step for teams to gain usable AI skills and prompt‑writing techniques that reduce operational and regulatory risk.

Selection of the Top 5 at‑risk roles started with a practical, use‑case first read of industry and vendor research: whitepapers that document where RPA and AI already bite into day‑to‑day finance work (data entry, document checks, reconciliation, loan intake and routine credit decisions).

Sources such as the Kinective whitepaper on RPA and AI, Tungsten's run‑through of RPA use cases (fraud checks, loan chatbots and fast document verifications) and Fortra's guide to RPA in banking were mined for repeatable patterns - high volume, rule‑based tasks that automation reliably reduces or accelerates - and then mapped onto common Belgian job descriptions in front‑ and back‑office teams.

Prioritisation weighted two factors: task routineness (how often a task follows fixed rules) and operational impact (time or compliance pain it removes); for a visceral example, an automated document checker cited in Tungsten can validate paperwork in as little as 30 seconds, turning hours of manual review into seconds and reshaping which junior roles are most exposed.

The result is a focused short‑list of roles where reskilling and governance will matter most for Belgian employers and workers.

These two technologies have applications in every area of the banking value chain right from customer origination and on-boarding to the back-office processing of loans, deposits, managing investments and the closure of a customer account.

Entry-level financial analysts in Belgian banks face clear exposure because GenAI excels at the routine, data-heavy tasks that junior roles often perform - data cleansing, first-pass forecasting and bulk report generation - which EY warns will “automate repetitive tasks so people can focus on complex interactions and creativity” and puts finance among the sectors with high AI exposure; that means the classic morning of spreadsheet drudgery can increasingly be compressed into minutes, shifting the job from number‑crunching to interpretation.

The practical path for juniors is twofold: first, learn to augment models - master data validation, prompt‑aware query design and basic model governance so outputs are auditable; second, strengthen the human skills that AI can't easily copy (storytelling, stakeholder judgement, and exception handling).

Employers should back this with training and tighter controls so productivity gains translate into safer, fairer decisions (Belgian firms already report notable AI benefits in the EY European Barometer), and individuals can start with focused courses and local use‑case playbooks such as the Nucamp guide to using AI in Belgian financial services to build hands‑on competence and regulatory awareness quickly.

EY analysis of GenAI's labour impact and a practical Nucamp AI Essentials for Work syllabus: guide to using AI in Belgian financial services make useful starting points.

“Consumer concerns about AI responsibility impact brand trust, placing CEOs at the forefront of these discussions. Executives must address these issues by developing responsible strategies to mitigate AI risks and being transparent about their organization's use and protection of AI.” - Raj Sharma, EY Global Managing Partner - Growth and Innovation

Customer service reps in Belgian banks and insurers are squarely in AI's sights because voice bots and conversational agents can now take routine queries, automate ticketing and even personalise routing - BSG's voice‑bot case study recorded a 25% lift in conversions - while enterprise rollouts show AI cutting average handle time for new agents by roughly 30% in live deployments, shrinking the window where junior reps add value; see BSG's write‑up on voice bots and NTT Data's Japan Post Bank case study for the practical proof.

That doesn't mean human contact disappears: research from Zendesk, Devoteam and others shows the upside is a hybrid model where AI deflects low‑value work and agents become problem‑solvers, empathetic escalators and AI copilots.

Practical steps for Belgium: start small with pilots, protect GDPR and logging workflows, retrain front‑office staff to use agent‑assist tools and multilingual RAGs, and follow a pilot‑first roadmap such as the Nucamp AI Essentials for Work bootcamp syllabus to get teams from repetitive scripts to oversight, prompt‑engineering and customer judgement in months rather than years.

So, can AI replace agents? No.

Junior credit underwriters in Belgian banks are squarely in the path of automation because modern AI can do the heavy lifting that once defined entry‑level underwriting - intelligent document processing and LLMs can extract and cross‑check numbers, flag inconsistencies and produce decision‑ready summaries that cut days of paperwork to minutes, driving the productivity gains vendors cite (20–60%) and even reducing inconsistent credit‑policy findings (V7 Labs guide to AI commercial loan underwriting).

That opportunity comes with two urgent demands for Belgian teams: tighten model governance and retool roles toward oversight. Practical responses include running focused pilots that automate spreading and collateral checks while keeping humans in the loop for borderline cases, building explainable audit trails so adverse actions can be justified in line with regulatory expectations, and training juniors to specialise in exception analysis, policy calibration and vendor validation rather than raw data entry - skills that raise value and lower legal risk (see CFPB guidance on credit denials by lenders using AI).

For Belgian underwriters, short, practical reskilling (playbooks and bootcamps focused on AI for finance) plus a pilot‑first roadmap will be the difference between redundancy and becoming the essential human oversight that trustworthy GenAI lending needs (Nucamp AI Essentials for Work bootcamp syllabus).

The biggest competitive edge AI offers underwriters is “risk digitization,” by which “information from disparate sources and formats is automatically parsed, evaluated, and, through a risk taxonomy, mapped into a computer-readable format that machines can understand and triage.”

Back‑office operations specialists in Belgium - the teams that run settlement, reconciliation and regulatory reporting - face immediate pressure as RPA and AI sweep through repetitive, rule‑bound work: vendors report cost reductions of 40–70% and claim that regulatory reports once taking two weeks can be produced in as little as two hours, so Belgian firms that lag risk both cost and speed disadvantages (see AutomationEdge's write-up on RPA for back-office benefits).

The upside for workers is clear but conditional: successful automation needs strong governance, segregation of duties, audit trails and human oversight to avoid over‑reliance and compliance pitfalls highlighted in the Technology Landscape on RPA (EthicsBoard).

Practical transition plans start with a pilot‑first roadmap that targets high‑volume, low‑exception processes (statements, reconciliations, settlement confirmations), sets up a Centre of Excellence and clear controls, and reskills staff toward exception analysis, bot orchestration and regulatory validation; a Belgium‑centric pilot playbook such as the Nucamp AI Essentials for Work pilot‑first roadmap can accelerate that shift from manual drudgery to higher‑value oversight while keeping regulators and auditors satisfied.

Junior accountants and tax processors in Belgium are among the most exposed roles because the core of their day - invoice posting, reconciliations, VAT checks and month‑end closes - is precisely what AI and specialised engines now automate, yet that same change creates a clear runway into higher‑value work: Robert Walters notes a structural shortage (32,000 accounting vacancies in the first half of the year) even as digitalisation shifts tasks from entry‑level drudgery to advisory, ESG reporting and compliance; pairing that demand with tools that flag anomalies (Silverfin's case studies show AI catching inconsistencies a human might miss) means the smart move is reskilling, not resisting.

Practical next steps for Belgian teams are concrete: train juniors on AI‑assisted bookkeeping and anomaly review, build playbooks for exception handling and tax nuance, run small pilots that combine bot automation with human oversight, and use always‑on AI coaching to accelerate on‑the‑job learning - a change that turns the typical junior role from repetitive processor into the firm's early‑warning system and client adviser.

See the careers briefing at Robert Walters careers briefing on switching to accounting in Belgium and Silverfin's guide to AI in accounting for practical models and examples (Silverfin guide: The Future of Accountants and AI in accounting).

“At Docyt, we have spent over five years building high-quality synthetic datasets using in-house expertise of expert accountants doing high-quality data labeling that captures the complexity of real-world bookkeeping. This dataset is a critical component of our HpAI architecture, enabling it to deliver precise, context-aware automation. We are now bringing this foundational AI architecture to accounting firms in the form of Docyt AI Copilot, enabling them to deliver client bookkeeping at scale, and with precision.”

Practical next steps for Belgian employers and workers start with a clear inventory and a pilot‑first approach: map every AI system in use, assess whether it's classed as high‑risk under the AI Act, run small PoCs on high‑volume processes and keep humans in the loop while building audit trails and vendor controls - steps Osborne Clarke warns are compulsory when introducing workplace AI and when consulting worker representatives under Belgium's CBAs (information and consultation duties) Osborne Clarke guidance on AI at work in Belgium.

Parallel actions employers must take are obvious from the EY barometer: invest in hands‑on, live training to close the skills gap and embed AI literacy across HR and operations so staff can use and challenge outputs safely EY Belgium: practical AI training to maximize workforce AI potential.

For workers, practical reskilling - short bootcamps, prompt engineering and oversight skills - is the fastest route from displacement risk to oversight roles; for teams that want a ready playbook, Nucamp's 15‑week AI Essentials for Work syllabus covers foundations, prompt writing and job‑based AI skills to accelerate that transition Nucamp AI Essentials for Work syllabus.

Finally, prepare for the AI Act timeline (initial rules in force, key employer provisions phasing in through 2025–2026), document decisions, and keep social partners involved so efficiency gains don't become legal or reputational risks.