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

Taiwan's Financial Supervisory Commission has set a clear wake‑up call for banks and insurers: adopt AI, but do it under “controllable risk” conditions - formalized in non‑binding Guidelines published 20 June 2024 that lay out four AI life‑cycle stages, six core principles (governance, fairness, privacy, robustness, explainability, sustainability) and stronger third‑party oversight for vendors and data handling; read the Baker McKenzie summary of Taiwan FSC AI Guidelines for the full guidance Baker McKenzie summary of Taiwan FSC AI Guidelines.

For finance professionals in Taipei and beyond, that means reskilling is no longer optional - practical workplace AI skills like prompt design, risk assessment, and explainability are essential; explore the Nucamp AI Essentials for Work syllabus for actionable, job‑focused training Nucamp AI Essentials for Work syllabus.

Methodology: the risk ranking used a pragmatic, cross‑checked approach tailored to Taiwan's regulatory and market context: first, map day‑to‑day tasks to proven AI use cases (OCR/IDP, RPA, automated credit scoring, LLM drafting) following the task‑mapping method described in the Complete AI Training analysis of at‑risk roles Complete AI Training - task‑mapping and regulatory criteria; second, overlay that map with an enterprise risk mindset - assessing model, data privacy, vendor concentration and explainability risks as advised by Mayer Brown's guidance on treating AI like any other enterprise risk Mayer Brown - applying an enterprise risk mindset to AI; and third, validate findings against common AI risk categories (bias, model drift, operational resilience, third‑party risk) and practical controls from InnReg's fintech playbook InnReg - AI risk management in financial services.

Tasks were scored by repetitiveness, regulatory sensitivity (decisions that affect consumers or require explainability), and current adoption trends - so roles dominated by high‑volume, rules‑based workflows rose to the top of the list, while jobs requiring nuanced judgment, client empathy or cross‑functional oversight scored as more resilient.

This blended method keeps the focus squarely on where Taiwanese institutions must both govern AI and invest in human upskilling.

Bank tellers and frontline customer‑service agents in Taiwan face some of the clearest near‑term disruption from AI: a June survey found businesses expect an average 29.2% of jobs to disappear over the next decade, and “bank clerks” were singled out among knowledge‑intensive roles with a 35.2% risk estimate - a real signal that routine, transaction‑heavy branch work is vulnerable (Taipei Times report on Taiwan AI job‑risk survey (June 2024)).

The Financial Supervisory Commission's own rollout shows momentum - roughly 126 of 383 institutions (about one in three) have put AI into production, with banks leading adoption and intelligent customer service listed among top use cases - so expect chatbots, automated ID/OCR flows and in‑branch digital greeters to multiply (Asian Banking & Finance: FSC AI adoption data in Taiwan).

Customers are open to smarter automation for routine decisions (62% comfortable with AI on credit card applications) but still demand a seamless omnichannel handoff and human help for high‑emotion events, so the future of frontline work is likely hybrid - think escalation specialists and empathy‑led problem solvers rather than cash‑counting clerks, not to mention the occasional knee‑high, R2D2‑like robot greeting the foyer.

“human‑machine collaboration,” - Bingo Yang (楊宗斌), survey spokesperson

Back‑office operations - settlements, reconciliation and payments processing - are the clearest example of where Taiwanese banks and payment firms can both cut costs and trip over compliance if automation is rushed: rule‑based work like matching statements, posting payments and cross‑checking ledgers is prime RPA territory, but only when paired with strong oversight and change control as outlined in Crowe's RPA program governance guidance (Crowe RPA program governance best practices).

Local providers already use robots to stitch together different ERPs and run payroll, AR/AP and expense checks for multinational subsidiaries - a practical playbook Evershine documents for Taiwan BPOs using RPA to solve the “two‑books” problem that otherwise creates tax and compliance risk (Taiwan BPO services supported by RPA for payroll and AR/AP).

At the same time, payments and settlement automation must respect Taiwan's EPI rules (caps on stored value and transfer limits, reserve and trust requirements), so technology roadmaps should be coordinated with legal teams under the Act Governing Electronic Payment Institutions (Taiwan Act Governing Electronic Payment Institutions overview).

Think of reconciliation like untangling a million tiny receipts across mismatched ERPs: robots speed the work, but governance, data protection and a documented audit trail are the seatbelt that prevents a regulatory crash.

Routine credit officers and junior underwriters in Taiwan - those who apply scorecards, verify documents and make thousands of small consumer and SME credit decisions - are among the most exposed to automation because their work is repeatable and rules‑based; lenders increasingly deploy automated credit scoring, OCR/IDP and LLM drafting to triage applications, but the Financial Supervisory Commission's AI guidance makes clear that deployment must preserve explainability, governance and strong third‑party oversight (Taiwan Financial Supervisory Commission AI guidelines overview), and Taiwan's privacy regime means identity and credit data need special care (Taiwan PDPA and biometric data considerations for financial services).

Practical adaptation is straightforward: shift from rule execution to oversight - designing explainable scorecards, validating model inputs, and managing exceptions - while exploring technical mitigations such as federated learning for cross-bank model improvement to improve models across banks without exchanging raw customer records.

Think of the change like replacing a paper conveyor belt with a monitored assembly line: throughput jumps, but without seatbelts (controls, recourse and transparency) the regulatory and reputational cost could be steep.

Compliance, KYC and regulatory‑reporting clerks in Taiwan are squarely in the spotlight as rule‑bound identity checks meet faster, cheaper automation: the March 2025 PDPA amendment is building a new Personal Data Protection Commission (PDPC) with centralized incident reporting (Article 12), an inaugural public‑sector DPO mandate (Article 18) and a six‑year transition to bring private‑sector supervision under the PDPC - so every data breach or questionable data use will land in one place (Taiwan PDPA amendment and PDPC details).

At the same time banks must keep performing risk‑based CDD, enhanced due diligence and suspicious‑activity reports to the IBMOJ, and that compliance choreography is now being read against the FSC's AI guidance that stresses privacy, explainability and stronger third‑party oversight for any automated KYC or monitoring tool (FSC AI guidelines for the financial industry) and sectoral AML/KYC rules described in banking practice guidance (Taiwan banking regulation: KYC & SAR requirements).

The practical takeaway for clerks is clear: routine ID‑matching and filings will be automated, but human expertise that validates edge cases, audits vendor models and documents incidents - think of a clerk who no longer stamps forms but inspects the one counterfeit in a stack of a thousand - will be the job that remains indispensable.

Junior investment analysts and routine equity/fixed‑income researchers are squarely in the sights of automation: robo‑advisors and AI‑driven portfolio tools now handle algorithmic asset allocation, automated rebalancing, tax‑loss harvesting and routine screening - functions that historically filled junior analysts' days - while the global robo‑advisory market is expanding rapidly (Fortune Business Insights robo‑advisory market forecast 2024).

In Taiwan this matters because Asia‑Pacific adoption is among the fastest growing regional trends, and local wealth teams are already experimenting with multilingual, privacy‑safe virtual advisors to serve Taipei clients (multilingual privacy‑safe virtual financial advisors for Taipei residents).

The practical career move is clear: trade routine model runs for skills that robo systems can't buy - model validation, explainable‑AI storytelling for clients, hybrid advisory design and vendor/governance oversight - so junior roles evolve from spreadsheet assembly to supervising the algorithms that now do the heavy lifting (IMARC Asia‑Pacific robo‑advisor market outlook).

For finance professionals in Taiwan the bottom line is straightforward: blend practical AI tool skills with governance instincts and local networks so automation becomes an opportunity, not a threat.

The central government is backing that shift - plans range from the Executive Yuan's multi‑year AI funding and talent targets (an annual NT$9–10 billion allocation to build the nation's AI industry) to MODA's policy toolkit that supplies computing power, a sovereign training corpus and talent‑certification guidelines - so there are both public resources and a national push to grow AI jobs and standards (Taiwan Executive Yuan AI industry development plan, MODA AI initiatives and policy toolkit).

Practical moves that pay off locally: learn prompt design and explainability, master AI‑enabled workflows, and pivot into oversight roles that audit models and manage exceptions; short, job‑focused programs like the Nucamp AI Essentials for Work (15 weeks) teach exactly these workplace skills and include prompt writing and role‑based AI practice (Nucamp AI Essentials for Work syllabus (AI at Work: Foundations, Writing AI Prompts, Job-Based Practical AI Skills)).

Picture a future where human experts sit shotgun to the algorithm - less keystrokes, more judgement - and Taiwan's national investments plus targeted reskilling make that seat the safest place to be.