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

Taiwan's financial sector is sprinting from pilot projects to practical deployment: an April 2025 FSC survey shows about 1 in 3 institutions now use AI - led by domestic banks (87%), life insurers (67%) and property insurers (45%) - and nearly half of AI users have added generative AI (61 institutions, a 21‑point year‑over‑year jump).

Primary drivers are operational efficiency (30%), headcount reduction (18%) and better customer experience (15%), while the FSC's non‑binding Guidelines push firms to balance innovation with governance, explainability and third‑party controls (see FSC Guidelines).

National investments - including an NT$50 million training phase that feeds industry internships and a multi‑year target to build tens of thousands of AI professionals - plus studies projecting multi‑trillion TWD gains from AI, make the case plain: Taiwan needs practical, job‑focused skills.

For teams aiming to turn AI into measurable impact, Nucamp's 15‑week AI Essentials for Work bootcamp teaches prompt craft, tool use, and workplace applications to move projects from sandbox to production.

Metric	Value
Institutions surveyed	383
Implemented AI	126 (~1 in 3)
Banks adopting AI	87%
Life insurers adopting AI	67%
Property insurers adopting AI	45%
Generative AI adopters	61 (48% of AI users)
Top reasons for adoption	Efficiency 30% · Reduce manpower 18% · CX 15%

Selection of the Top 10 AI prompts and use cases was driven by Taiwan's regulatory and operational realities: priority went to applications that align with the FSC's lifecycle and six core principles (governance, fairness, privacy, robustness, explainability, sustainability) and that score highly on measurable business impact, data readiness and controllable risk - factors the FSC flags when assessing AI systems (client‑facing impact, personal data use, autonomy, system complexity and stakeholder breadth).

Local policy signals - from the Taiwan AI Action Plan 2.0 and the “guidance‑before‑legislation” approach - pushed emphasis toward talent‑friendly, sandboxable projects that can be validated inside the FinTech regulatory sandbox and scaled with contractual third‑party controls and explainability checks (see the FSC Guidelines and Taiwan AI Action Plan).

Practical data considerations were equally decisive: use cases that can tap Taiwan's centralised, high‑quality sources (for example, the unified invoicing system that even links receipts to a national lottery) were ranked higher because reliable inputs reduce false positives and speed deployment.

The result: prompts chosen to balance high business value with clear governance, traceability and a pathway to production in Taiwan's evolving legal framework.

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

Automated financial reporting and disclosure drafting is one of the clearest productivity wins for Taiwan's financial firms, where generative models can transform dense regulatory filings into crisp, board‑ready summaries - but only if governance keeps pace.

The FSC's Financial Industry AI Guidelines demand lifecycle controls, explainability and third‑party oversight for deployed systems, so a drafting copilot must log data lineage, preserve sources for PDPA review, and surface model confidence and rationale on each sentence (Taiwan Financial Supervisory Commission Financial Industry AI Guidelines).

Taiwan's risk‑based evaluation ecosystem - including the Administration for Digital Industries' AI Product and System Evaluation Guidelines - classifies uses like credit‑affecting disclosures as potentially high‑risk, triggering independent validation and stricter vendor contracts (Taiwan AI Product and System Evaluation Guidelines by the Administration for Digital Industries).

Practical implementation means pairing prompt engineering with enforceable supplier clauses, sandbox testing under the FinTech framework, and auditable provenance so that a machine‑drafted footnote arrives with the same traceability as a human signature.

Trend analysis over a five‑year window is the diagnostic lens that separates temporary bumps from structural change - by collecting clean revenue and expense series, choosing time‑series or regression methods, and visualizing moving averages and seasonality, finance teams can convert noisy monthly ledgers into hire‑or‑hold decisions and cash‑runway alarms (see practical steps from 180ops on revenue trend analysis).

For Taiwan's banks and insurers, where national signals like the Taiwan AI Action Plan 2.0 and modular efforts such as FedGPT are accelerating automation, a five‑year view is less about perfect prediction and more about scenario planning: anchor models in historical drivers, build base/downside cases, and stress test vendor, capital and hiring assumptions so boards see credible tradeoffs instead of wishful lines on a chart (the Limelight guide explains why multi‑year forecasting matters for strategic choices).

The “so what?”: a disciplined five‑year diagnostic often reveals one clear lever - pricing, headcount or product mix - that alone can move the needle on margins and cash runway, turning trend‑finding into prioritized action instead of endless reports.

Factor	3‑Year Forecast	5‑Year Forecast
Strategic horizon	Short to medium term	Long term, directional planning
Accuracy	Higher, grounded in recent data	Lower, based on assumptions
Use cases	Resource planning, hiring, product rollouts	Market entry, IPO readiness, long‑range strategy
Level of detail	More granular (monthly/quarterly)	High‑level drivers and scenarios
Risk management	Tracks variance from base case	Built for uncertainty with scenario analysis

Audit support in Taiwan needs to combine hard‑nosed audit standards with modern anomaly detection: design analytical procedures that mirror AU Section 329's call for

plausible relationships

and precision, then log every exception so it links back to the engagement team's risk assessment (PCAOB AU Section 329 auditing standard details).

Practical steps start with crisp expectations and flux analyses that surface the one ledger line or timing shift that changes an opinion, followed by skeptical inquiry and corroboration as the standards require; documentation must explain how expectations were formed and which balances need attention (Collemi Consulting preliminary analytical review procedures in a financial statement audit).

For Taiwanese banks and insurers, modular LLM copilots - like the FedGPT approach - can accelerate anomaly triage and produce auditable trail notes, but controls remain essential: test data reliability, disaggregate by business unit for precision, and route high‑risk exceptions to human investigators.

The

so what

is simple and vivid: a well‑documented AI flag plus one corroborating source can turn a noisy month‑end spike into a single, defensible audit adjustment rather than weeks of ad hoc digging, shaving audit hours while preserving professional skepticism and regulatory traceability (FedGPT modular LLM approach for anomaly triage).

Document analysis and research acceleration in Taiwan's financial sector hinge on marrying OCR and Intelligent Document Processing (IDP) to compliance-ready workflows: modern OCR/IDP tools can convert invoices, bank statements and long regulatory filings into structured JSON or Excel exports, create immutable digital audit trails, and push searchable transaction data into reconciliation engines so a month‑end task that once took days becomes an hours‑long exception hunt (KlearStack shows extraction accuracy and audit benefits that drive this shift).

IDP platforms also cut processing time - often by 50% or more - while routing edge cases to humans for review, which preserves skepticism for high‑risk items and reduces false positives during regulator reviews (see Roboyo on IDP).

For Taiwan specifically, choosing OCR that handles Traditional Chinese and multi‑language documents is essential to keep vendor onboarding and PDPA checks fast and defensible; with the right APIs, teams can automate bulk extraction, flag anomalies for triage, and deliver auditable evidence to boards or supervisors without rebuilding legacy pipelines (KlearStack and Authme provide practical paths to scale and compliance).

Customer-facing personalization and virtual financial advisers are fast becoming mainstream in Taiwan: robot advisers now manage NT$6.9 billion and serve about 167,000 investors, triggering the FSC to tighten oversight and launch algorithm reviews to guard against manipulation and protect investor rights (Taipei Times report: Financial Supervisory Commission algorithm review of robo-advisers in Taiwan).

Practical personalization - risk profiling, goal-based nudges, fractional shares and automatic rebalancing - lowers the barrier to entry and improves retention; Taipei Fubon's Nano Investment shows how a LINE-integrated robo service with a real-time customer-journey dashboard and automated onboarding can scale reach and tailor advice to life goals (The Digital Banker case study: Taipei Fubon Nano Investment LINE-integrated robo-advisor).

Behind the scenes, scalable data and API platforms help deliver differentiated insights and rapid feature rollouts while meeting compliance needs (LSEG digital solutions for robo-advisors: investor digital platforms and APIs).

The so what: personalization turns modest balances into durable customer relationships - an accessible, regulatory-aware path to broader financial inclusion and measurable AUM growth.

Metric	Value
Robot adviser assets under management (AUM)	NT$6.9 billion
Year‑over‑year AUM growth	42%
Investors using robot advisers	167,000 (up 17% YoY)
Firms offering robo advisory	16
Top providers by AUM	Cathay United Bank NT$1.87B · First Commercial Bank NT$1.5B · Hua Nan Commercial Bank NT$700M

Taiwan's surge in e‑wallets, instant transfers and online banking means fraud detection and AML are no longer back‑office chores but frontline risk control: real‑time transaction monitoring, behavioural analytics and dynamic risk scoring stop illicit flows before funds move.

Modern platforms - like Tookitaki's FinCense - combine burst‑speed detection, adaptive AI and cross‑institution typologies to catch account takeovers, mule networks and synthetic IDs while aligning with FSC expectations, and SEON and Eastnets show how AI‑driven models reduce noise and prioritise true threats so analysts focus on high‑value cases.

The practical payoff is immediate and vivid: a bank spotting multiple small transfers to the same overseas recipient can freeze accounts and halt laundering within minutes, turning a sprawling investigation into a single decisive action.

Implementation priorities for Taiwan are clear - real‑time data ingestion, explainable risk scores, multilingual name screening and collaborative intelligence-sharing - so institutions can meet stricter AML/CFT expectations without creating customer friction; the outcome: faster onboarding of scenarios, fewer false positives, and demonstrable audit trails for regulators.

Metric	Reported Outcome
Onboarding AML scenarios (Tookitaki)	~50% faster
Reduction in false positives (Tookitaki)	Over 40%
Automate fraud checks (SEON)	Up to 95%
Reduce fraudulent registrations (SEON)	~90%
Accelerate manual reviews (SEON)	~90%

Loan processing, credit scoring and underwriting automation are becoming the practical linchpin for Taiwanese lenders that need speed plus auditability: by combining Intelligent Document Processing, LLM‑based analysis and alternative data feeds, institutions can turn weeks of manual review into hours or minutes while preserving explainability and compliance.

V7's deep dive shows AI handling messy financial statements and unstructured contracts to surface hidden risks and deliver productivity gains of roughly 20–60% (V7 AI commercial loan underwriting guide), and Tavant's LO.AI demonstrates how end‑to‑end automation can cut application‑to‑decision time by as much as 80% (Tavant LO.AI automated loan underwriting case study).

For Taiwan's thin‑file SMEs and gig‑economy borrowers, embedding alternative data into credit models expands access while improving risk signals - Accumn's alternate‑data approach shows how GST, payroll and bank flows enrich underwriting logic (Accumn alternative-data credit underwriting case study).

The “so what”: one real example turned a 12–15 day approval horizon into a 6–8 day decision, freeing underwriters to focus on complex deals and shrinking time‑to‑yes into a competitive edge.

Metric	Reported Outcome
Productivity gains (V7)	20–60%
Application‑to‑decision reduction (Tavant LO.AI)	Up to 80%
Underwriting time / auto‑decision (Zest AI)	Save up to 60% · Auto‑decision ~80%

“Zest AI brought us speed. Beforehand, it could take six hours to decision a loan, and we've been able to cut that time down exponentially. Zest AI has helped us tremendously improve our efficiency and member experience.”

Wealth and asset management are entering a pragmatic era where advisor “copilots” and desktop augmentation turn busywork into client‑facing value: compliant copilots such as additiv's AdvisorCopilot give advisors instant, policy‑aware answers and market context on demand, while Microsoft Copilot patterns help surface portfolio statistics and real‑time market trends during client chats or emails (additiv AdvisorCopilot (compliant advisor copilot for wealth management), Microsoft Copilot use cases for asset and wealth management).

Complementary tools - AI notetakers that capture meetings, extract action items and push them into CRMs - are now table stakes for efficiency and auditability (AI notetakers buyer's guide for financial advisors).

For Taiwan firms, the practical payoff is crisp: faster, compliant meeting prep and post‑meeting follow‑ups that scale personalised advice without stretching advisor capacity, so a single morning of calls can deliver dozens of tailored, traceable client next steps instead of scattered notes and friction.

Capability	Example vendors
Compliant, on‑demand advisor Q&A	additiv, CogniCor
Real‑time portfolio insights & recommendations	Microsoft Copilot, CogniCor
Meeting capture, action items & CRM sync	CogniCor, Pulse360, Finmate, Jump
Compliance‑forward deployment (audit trails)	Focal, CogniCor, additiv

“There is a seemingly overblown fear of AI by the advisor community.”

Finance operations automation in Taiwan starts with better OCR and ends with a faster, auditable close: modern IDP pipelines capture invoice headers, but the real multiplier is reliable line‑item extraction that feeds AP, PO‑matching and GL coding without days of rekeying.

Choose engines that support Traditional Chinese and multi‑language PDFs, batch processing for month‑end surges, and secure exports into ERP - Azure Document Intelligence prebuilt invoice model documentation shows how invoice fields and line items can be turned into structured JSON for downstream workflows, while specialist tools that emphasise template‑free line‑item capture speed up coding and reduce errors such as DocuClipper's line‑item extraction feature.

For tougher, messy invoices a hybrid OCR+LLM pattern can lift line‑item recall into the high‑90s and slash manual corrections, turning a month‑end slog into an hours‑long exception hunt and freeing controllers to focus on cash strategy rather than typing numbers - see the RaftLabs article on OCR and LLM hybrid invoice data extraction for details.

Metrics:
Line‑item extraction accuracy: ~97% (RaftLabs hybrid OCR+LLM)
Peak OCR accuracy cited: Up to 99% in some tasks (VirtualWorkforce)
Manual data entry cost reduction: Up to 80% (VirtualWorkforce)
Invoice processing speed: ~79% faster in benchmarks (Brex)
Compute cost improvement (LLM pipeline): ~70% lower per‑document cost (RaftLabs)
Controller time saved (case): 40 hours/month (Centime case study)

Real‑time market and newsflow monitoring is now table stakes for Taiwan's finance teams: regulators and bureaus publish actionable data that quickly reshape risk and strategy, so watch the Financial Supervisory Commission's press releases and data feeds for capital‑market signals (Financial Supervisory Commission (FSC) press releases and data feeds) and the Banking Bureau's rolling statistics for credit, SME lending and NPL trends (Taiwan Banking Bureau rolling statistics); a vivid illustration of why this matters is the FSC's recent data showing sector exposure to China fell NT$201.3 billion year‑over‑year and about NT$48.56 billion from the prior month, a swing that can change portfolio and compliance priorities overnight (see reporting on the exposure dip by Focus Taiwan) (Focus Taiwan report on FSC data showing China exposure dip).

To turn noise into insight, competitive intelligence teams combine regulator scraping, sentiment trackers and consultant playbooks, and modular LLM approaches such as FedGPT can speed custom alerting and analyst triage while keeping integrations lightweight and governance explicit (FedGPT modular LLM approach for custom alerting and analyst triage); the payoff is clear - faster, auditable decisions that convert headline‑driven volatility into one strategic, defensible action.

Ready-to-run next steps for Taiwan teams: treat AI adoption as a seven‑pillar maturity journey - start with a clear, business‑led AI strategy, pick one measurable pilot, shore up data and a cloud‑ready platform, train role‑specific users, and bake governance into every launch so pilots scale into repeatable ROI; Grant Thornton's seven‑pillar checklist is a practical road map for this sequence (Grant Thornton seven‑pillar AI maturity checklist for asset management).

Align each pilot with the FSC's non‑binding Taiwan Financial Supervisory Commission Guidelines for AI in the Financial Industry - risk‑classify the use case, enforce third‑party clauses and audit trails, and consider the FinTech sandbox for high‑risk experiments - then measure one primary metric (cycle time, error rate or dollars saved) and iterate.

For practical upskilling, Nucamp's 15‑week Nucamp AI Essentials for Work 15‑week bootcamp teaches prompt craft, workplace tools and role‑based application so teams can move from sandbox to production with governance and verifiable value.

Program	Length	Early Bird Cost	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work 15‑Week Bootcamp

“Start small and make governance repeatable,” Gulati said.