The Complete Guide to Using AI in the Financial Services Industry in Taiwan in 2025

AI is reshaping Taiwan's financial services in 2025 by turning routine bookkeeping into real-time insight engines - boosting efficiency, spotting anomalies across millions of transactions, and powering continuous forecasting that helps banks and insurers act faster; see how global finance teams use AI to improve decision‑making and efficiency in Wolters Kluwer and Workday analyses, while local momentum is reflected in events like Taiwan AI Day 2025 event.

At the same time Taiwan's Financial Supervisory Commission has issued practical Taiwan Financial Supervisory Commission AI guidelines that stress governance, explainability and lifecycle controls - so institutions can adopt AI under “controllable risk” conditions.

For practitioners and managers who need hands‑on skills to apply these tools responsibly, Nucamp's Nucamp AI Essentials for Work bootcamp syllabus teaches practical promptcraft, tool use, and workplace applications to make AI a productivity multiplier rather than a black box risk.

Program	Length	Early bird Cost	Link
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work syllabus (Nucamp)

“AI and ML free accounting teams from manual tasks and support finance's effort to become value creators.” - Kainos Group Head of Finance Matt McManus

AI in Taiwan's financial sector is shifting from pilots to production-ready services: expect fraud and anomaly detection, automated credit decisioning and real‑time forecasting to coexist with new revenue plays such as AI‑enabled trade finance and asset‑management insights that capitalise on Taiwan's export and semiconductor strength; Taiwan's broader 2025 economic outlook shows AI driving export demand and private investment even as geopolitical and energy risks loom (see Taiwan's economic outlook for 2025), while banks and insurers race to embed models alongside cloud and cybersecurity upgrades identified in IMD's analysis of the sector's digital transformation.

Practical use cases range from AI‑powered portfolio signals that lean on faster chip cycles (now compressed to roughly a one‑year product cadence) to virtual‑bank customer journeys and operational automation that cut costs and speed decisions; for a compact list of action‑oriented prompts and real workplace examples, review the Top 10 AI Prompts & Use Cases for Taiwan's financial services.

The net effect: institutions that pair sound governance with focused pilots can turn Taiwan's hardware‑led AI momentum into smarter, faster financial products for customers and corporates alike.

“Approximately 55% of Taiwanese firms in the financial services industry have embraced a well-coordinated digital strategy, significantly outperforming the global average of 30%.”

Taiwan's 2025 AI strategy stitches big public spending, chip‑centric industry alliances and tighter governance into a single push to become an “AI Island”: the government's high‑profile AI New Ten Major Construction programme (an NT$190–200 billion investment) channels funds and thematic investment vehicles through the National Development Council and NSTC to scale AI chips, sovereign compute and commercial platforms, while industry alliances like the AI on Chip Taiwan Alliance and the newly formed AI Innovation Application Alliance marshal TSMC, Hon Hai and local cloud and IoT players to turn hardware strength into applied services; at the same time the NSTC's draft AI Basic Act - submitted to the Executive Yuan for review in early 2025 - and new Ministry of Digital Affairs evaluation frameworks aim to balance openness (TAIDE and the TAIWANIA 2 supercomputing push for local LLMs) with risk‑based rules, sandboxes and vendor oversight so banks and insurers can move from pilot to production under clearer governance, even as civil society and legislators debate the right mix of innovation, transparency and safeguards for data and rights.

Read the plan and legislative context for more detail: the NT$200B initiative and the NSTC draft AI Basic Act.

Initiative	Lead	Highlight
AI New Ten Major Construction (NT$190–200B)	National Development Council / NSTC	Funding pools to boost chips, sovereign compute and industry adoption
Draft AI Basic Act	NSTC → Executive Yuan	Risk‑based principles on transparency, data governance and enforcement
TAIDE / TAIWANIA 2	National Centre for High‑Performance Computing / NARLabs	Localised LLMs and national computing power for AI R&D

“Without proper AI regulations, Taiwan risks chaotic applications and hindered industrial development; citizens could be ‘running naked in the AI wave'.”

The new AI Basic Law draft stitches high‑level principles with practical tools - but it's deliberately a framework rather than a bright‑line rulebook, so financial firms must watch both the law and the sectoral guidance that will follow.

Announced by the NSTC in mid‑2024 and moved through government review in 2025, the draft sets out seven core principles (human autonomy, privacy and data governance, transparency and explainability, security, fairness, accountability and sustainable development), tasks the Ministry of Digital Affairs with a risk‑classification framework compatible with international practice, and promotes regulatory sandboxes and public‑private partnerships to keep innovation moving while standards are worked out (see the Lexology analysis of the NSTC draft AI Basic Law: Lexology analysis of the NSTC draft AI Basic Law and Lee & Li's practical summary of policy and sectoral rules).

The bill also preserves an R&D “safe harbour” so early research isn't over‑regulated, but civil‑society groups like TAHR and the Judicial Reform Foundation warn that broad exemption clauses and vaguely defined agency roles could create gaps - TAHR specifically flagged risks of overlap with the Personal Data Protection Act - so financial institutions should align existing FSC guidance and vendor controls with the draft's lifecycle and risk concepts while preparing for industry‑specific rules and certification requirements to land in the coming months.

Draft feature	Practical effect for finance
Seven core principles	Provides ethical foundation (privacy, explainability, fairness) that FSC guidance expects firms to follow
MODA risk classification	Will enable sectoral regulators to impose proportionate controls on high‑risk AI (e.g., credit decisioning)
Sandboxes & R&D exemptions	Facilitates experimentation but raises accountability questions for deployed systems

“Early communication with stakeholders is crucial,” they say.

Taiwan's Financial Supervisory Commission (FSC) has turned the abstract idea of “trustworthy AI” into a practical, risk‑based checklist for banks and insurers: its non‑binding “Core Principles” and June 2024 Guidelines ask firms to treat AI as a lifecycle problem - system planning & design, data collection & input, model building & validation, then deployment & monitoring - and to build governance checkpoints at each stage so models don't drift from pilot to production without controls.

The FSC's six core principles - governance & accountability, fairness and human‑centric values, privacy & customer rights, robustness & security, transparency & explainability, and sustainable development - are woven through vendor oversight, contractual data clauses, differentiated explainability expectations for in‑house vs.

acquired models, and practical risk factors (client impact, data use, autonomy, complexity, stakeholder reach and recourse options). In short: regulators expect institutions to map use cases to risk, document decisions, harden third‑party contracts (encryption, migration and audit rights), and keep human review where autonomy could materially affect customers - think of AI governance as a safety net that follows a model from blueprint to live service.

Read the FSC announcement and Baker McKenzie's practical summary for the full checklist and examples of third‑party due diligence.

AI life‑cycle (FSC)	Six core principles (FSC)
1) System planning & design 2) Data collection & input 3) Model building & validation 4) Deployment & monitoring	1) Governance & accountability 2) Fairness & human‑centric values 3) Privacy & customer rights 4) Robustness & security 5) Transparency & explainability 6) Sustainable development

Operational controls in Taiwan's financial sector now centre on treating third‑party AI and cloud relationships as an extension of a firm's own risk lifecycle: the FSC's AI Guidelines ask institutions to assess supplier expertise, concentration and the monitorability of risks, and to lock those responsibilities into contracts that spell out scope, confidentiality, dispute mechanisms, personnel rules and explicit regulator access; Chambers' fintech guide also notes that outsourcing agreements must include compliance, risk‑management and data‑access permissions for supervisors, while Baker McKenzie's summary highlights mandatory data‑protection clauses (encrypted transmission, storage security and disposal) and written or digital records of delegated matters to support oversight.

For teams piloting novel models, Taiwan's regulatory sandbox - governed under the Sandbox Act that has let innovators test fintech concepts since 2018 - remains the pragmatic route to try high‑risk use cases with temporary waivers, but entry requires FSC approval and clear plans for post‑pilot licensing and controls.

In short: successful operational controls are pragmatic and contractual - rigorous supplier due diligence, defined audit and encryption clauses, continuous monitoring and a clear sandbox exit plan turn vendor relationships from a compliance headache into an auditable chain of accountability.

Read the FSC guidance on AI lifecycle and third-party management for lifecycle and third‑party specifics and the Chambers practice guide on outsourcing and regulatory sandboxes for implementation detail.

Control	What to include (source)
Contract scope & obligations	Scope, compliance, confidentiality, dispute resolution, personnel rules (Chambers)
Data protection clauses	Encrypted transmission, storage security, disposal after service termination (Lexology / Baker McKenzie)
Audit & records	Written/digital records of delegated matters; regulator data‑access permissions (Chambers)
Supplier oversight	Assess knowledge, experience, concentration risk; retention of audit rights (Lexology)
Sandbox option	FSC approval under Sandbox Act for experimental waivers; post‑sandbox licensing review (Chambers)

Privacy, IP and liability are now core operational controls for any AI deployment in Taiwan's financial sector: the Personal Data Protection Act (PDPA) applies extraterritorially to any processor handling Taiwanese nationals' data and - with the PDPC due to begin operations and stronger PDPA amendments moving through the Legislative Yuan - firms should treat data risk as litigation and regulatory risk, not a checklist.

Practical implications for banks and insurers include strict notice-and-consent requirements for secondary AI uses, mandatory security and incident‑record programmes (with some sectors required to report material breaches within 72 hours), and a real fines regime (administrative penalties can reach up to NT$15 million for serious violations), so contractual clauses, encryption, retention policies and audit rights must be tightened when using third‑party models or cross‑border compute.

Expect the public sector DPO rulebook to ripple into private practice during the PDPC transition, and remember that sectoral regulators like the FSC will enforce financial‑sector specifics (outsourcing, data localisation and supervisory access) on top of PDPA duties - a single misconfigured data feed or careless vendor agreement can therefore trigger multi‑agency scrutiny.

For practical guidance read the comprehensive ICLG chapter on Taiwan data protection, the STLI summary of the PDPA amendments and Chambers' 2025 practice guide on sectoral rules and enforcement.

Issue	Practical effect for AI in finance (TW)
Extraterritorial scope	PDPA covers foreign entities processing Taiwanese nationals' data - vet offshore vendors and contracts
Breach reporting	Sector rules may require 72‑hour reporting; PDPA requires timely notification to data subjects
Penalties	Fines up to NT$15M for serious breaches; FSC uses sector powers for financial firms
PDPC & DPOs	PDPC launch (Aug 2025) and DPO regimes increase supervisory scrutiny and governance expectations
Cross‑border transfers	Transfers allowed but subject to restrictions for national interest or inadequate protection - document risk assessments

Explainability, cybersecurity and national‑security risks now sit at the centre of any Taiwanese financial AI rollout: the FSC's AI Guidelines make clear that institutions must be able to

explain

how a model reaches decisions (with tighter explainability for in‑house or contractor‑built systems and acknowledged limits for commercially acquired models), and must publish reports or technical disclosures to maintain transparency and market trust - see the FSC AI Guidelines summary from Baker McKenzie for the lifecycle and explainability requirements.

At the same time system robustness and continuous cybersecurity monitoring are mandatory expectations across the AI life cycle, with contracts, encryption and vendor oversight called out as frontline controls.

Taipei's Ministry of Digital Affairs has even flagged imported consumer‑grade models (the DeepSeek warning) as potential vectors for cross‑border telemetry and information leakage, a reminder that a single misconfigured data feed or careless vendor agreement can escalate into multi‑agency scrutiny.

The practical takeaway for banks and insurers: map each use case to explainability needs, lock security and migration rights into supplier contracts, and treat national‑security flags as a core risk in vendor selection and sandbox plans (see the Lee & Li note on MODA's DeepSeek advisory).

Risk	Regulatory guidance / source
Explainability & transparency	FSC AI Guidelines summary by Baker McKenzie
Cybersecurity & vendor controls	FSC lifecycle guidance on robustness, encryption, and contracts (Baker McKenzie)
National security / cross‑border telemetry	MODA advisory and DeepSeek warning (Lee & Li)

Taiwan's market ecosystem in 2025 blends deep silicon muscle with a fast‑maturing services layer, creating fertile ground for AI in financial services: SEMI forecasts Taiwan investments of roughly US$28 billion in 2025 to expand HPC and advanced packaging that underpin local model hosting and edge AI, startup showcases like SuperAI 2025 and AWS's Startup AI Connect are elevating high‑impact teams and cross‑border partnerships, and global meetups such as OCP APAC signal the island's role as a regional AI convergence point.

At the same time analysts put Taiwan's AI‑as‑a‑Service market at about USD 9.2 billion in 2025 with strong multi‑year growth ahead, while national strategy targets (large VC pools, massive upskilling and multi‑trillion NTD output ambitions) mean banks and insurers will face abundant vendor choices, rising local capability and intense competition for talent - so expect adoption to be practical and partner‑driven rather than purely experimental.

Read the SEMI briefing on Taiwan's investment surge, the SuperAI coverage for startup momentum, and the AIaaS market note for practical sizing.

Metric	Figure / target	Source
Taiwan investments (AI/HPC & advanced packaging)	~US$28 billion (2025)	SEMICON Taiwan 2025 press conference and investment briefing
Taiwan AIaaS market size	USD 9.2 billion (2025)	Taiwan AI-as-a-Service (AIaaS) market report (2025)
National strategy highlights	1,000,000 AI professionals; 100‑billion NTD VC fund; 15 trillion NTD output value target	Taiwan national AI strategy (2025) briefing and targets

Practical adoption in Taiwan boils down to a clear, staged roadmap: begin by mapping each AI use case to the FSC's lifecycle and six core principles - plan and design, secure the right data inputs, validate models, then deploy with continuous monitoring - and treat risk as the selector for explainability, human review and third‑party controls (see the FSC guidelines summarized by Baker McKenzie summary of FSC AI guidelines); next, harden vendor contracts (encryption, migration and audit rights), document testing and recourse options, and use Taiwan's regulatory sandboxes to trial high‑impact services before scaling.

Parallel regulatory threads - MODA's evaluation frameworks and the NSTC draft AI Basic Act - mean institutions must fold national risk classifications and data‑governance expectations into procurement and PDPA compliance plans (Lee & Li / Chambers analysis of Taiwan AI regulations), because a single misconfigured data feed can prompt multi‑agency scrutiny.

Operationally, require independent validation for high‑risk models, keep explainability records, and invest in practical upskilling so teams can turn policy into product - practitioner training such as Nucamp's 15‑week AI Essentials for Work syllabus - Nucamp.

the “so what” is simple: following these steps turns compliance from a drag into a repeatable path for safe, competitive AI services in Taiwan's tightly regulated financial market.

Program	Length	Early bird Cost	Link
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work syllabus - Nucamp