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

Ireland's financial services sector is at an inflection point: banks and fintechs face heavier regulation, faster customer expectations and fiercer competition, so AI and GenAI are being deployed to automate credit decisions, speed fraud detection and deliver hyper‑personalised services at scale - a transformation HCLTech argues can restore competitive edge in a market that's become an international hub (Ireland is now the only English‑speaking Common Law jurisdiction in the Eurozone).

Research suggests AI already touches nearly 15% of Irish IT spend and could add roughly €2 billion and 20,000 jobs, but effective adoption must balance benefits with oversight under the new EU AI Act and Central Bank scrutiny (see regulatory guidance).

Closing the skills gap is practical and urgent: targeted programmes such as the AI Essentials for Work bootcamp registration can teach prompt writing and everyday AI skills for finance teams to deploy tools responsibly and drive measurable efficiency gains.

“Responsible AI is crucial to ensure ethical use of technology, safeguard customer data, and maintain regulatory compliance.”

Selection of the Top 10 use cases and the accompanying practical prompts began with regulatory reality: every candidate had to be defensible under the EU AI Act and the Central Bank's emerging supervisory expectations, so priority was given to high‑impact, high‑control workflows where governance, explainability and data handling are clear and testable (see the Maples guidance on risk and regulatory considerations for Irish regulated firms).

Procurement and vendor risk framed the shortlist - proof‑of‑concept trials, careful due diligence on data use, and contractual controls around model training and sub‑outsourcing were non‑negotiable, in line with William Fry's procurement checklist.

Prompt design followed proven, pragmatic rules: break complex jobs into one‑step tasks (summarise a section first, then synthesize recommendations), be explicit about data sensitivity and verification, and craft outputs that map to an audit trail so supervisors can see why a decision was made - drawing on practical examples from finance‑focused prompt libraries such as DFIN's financial reporting prompts.

The result is a ranked, regulator‑aware Top 10 that pairs concrete prompts with the governance controls Irish firms will need to run safe pilots, scale responsibly and retain auditability as use expands.

“With such a broad spectrum of potential uses for AI, there will be cases where judgements need to be made about whether it is appropriate to use AI for a particular process or business problem. Supervisors will focus on the decision making process around any such judgements to assess whether they are sufficiently transparent, with clarity over who is accountable for any decisions made.”

Vertex AI can turn the piles of regulatory filings, client MSAs and loan documents that clog Irish back‑offices into a searchable, auditable knowledge base - by attaching PDFs and plain text to multimodal prompts and asking Gemini to extract, summarise and synthesize answers without the need to skim pages yourself; Google's Document Understanding guide explains how to add PDFs, recommended PDF best practices (use text‑rendered pages, split long files) and per‑model limits such as 50MB file sizes and EU token quotas for heavy workloads (Google Cloud Vertex AI Document Understanding guide and PDF best practices).

Practical pilots can be stood up quickly with the Agent Builder approach - upload PDFs to Cloud Storage, create a data store and test conversational search and follow‑ups to surface the exact clause or risk note a regulator might ask about (Tutorial: build a Vertex AI Agent Builder document search app).

Real‑world demos show the same pattern working for contracts: agreement summarisation and plain‑language Q&A can help Irish teams accelerate due diligence and customer onboarding while preserving an audit trail for compliance reviewers (DocuSign pilot: agreement summarisation with Google Cloud Vertex AI).

Commonwealth Bank's playbook for enhanced virtual assistants offers practical inspiration for Irish firms: CBA reports a 50% reduction in scam losses and a 30% fall in customer‑reported frauds after deploying AI safety features and GenAI that analyses “over 20 million payments daily,” while its conversational agent Ceba automated 200+ tasks and helped cut call‑centre wait times by 40% - real outcomes Irish banks can target to speed onboarding, tighten fraud detection and free staff for higher‑value advisory work (read more on CBA's fraud and loan impact).

CBA's model also scales proactive alerts (rising from 20,000 to 35,000 per day) and points to heavy efficiency gains - annual credit reviews shrunk from 14 hours to a potential two - showing how conversational AI plus transaction monitoring can translate into faster credit decisions and better SME support (see the Ceba case study and CBA Customer Engagement Engine write-up).

“With more than one in three Australians and almost one in four businesses considering us their main financial institution, we have a huge customer base to serve. Their preferences and expectations continue to shift, and we aim to meet them by delivering distinct, differentiated and compelling propositions. Technology, and AI in particular, are critical in meeting this ambition. AI allows us to deliver better experiences to more customers at a faster rate, and we're already seeing significant benefits in a variety of use cases.”

For Irish banks and fintechs looking to move from volume to value in fraud operations, Amazon Bedrock Agents offer a clear pattern: constant, multimodal monitoring that filters routine noise and escalates real incidents with an auditable trail.

AWS's

Agents as escalators

demo shows how agents extract frame sequences, reason over temporal context, and suppress trivial triggers (think wind or birds) while applying a graduated response model from

log only

to

immediate response

so human teams see far fewer false positives (Agents as escalators: real‑time video monitoring with Amazon Bedrock Agents).

For transaction streams and account networks, AWS Partners outline a deployable pattern that pairs Bedrock Agents with Amazon Neptune to translate natural‑language probes into graph queries, return fraud scores and embed responses into front‑end decision workflows - ideal for fast, explainable AML and chargeback handling (Anti‑fraud solutions using Amazon Bedrock Agents and Amazon Neptune).

And where evidence lives across documents and ledgers, GraphRAG on Bedrock Knowledge Bases connects relationships across datasets so agents can spot multi‑hop fraud chains rather than isolated anomalies - delivering targeted alerts and audit‑ready reports that scale investigation capacity without swamping compliance teams (GraphRAG for financial fraud detection on Amazon Bedrock Knowledge Bases).

Dun & Bradstreet brings practical, audit‑ready signals to underwriting and lending automation for Irish financial institutions: the Commercial Credit Score (CCS) is a 200+‑variable delinquency predictor that estimates the likelihood a firm will be 90+ days past due and is available for UK and Ireland deployments (D&B Commercial Credit Score (CCS)); alongside this, PAYDEX provides a dollar‑weighted 0–100 measure of payment timeliness (80 = strong prompt payment, 100 = best) that helps distinguish reliable customers from risky ones (How to read a D&B report: PAYDEX).

Combined with D&B Credit Advantage - which lets lenders bulk upload accounts‑receivable, match records to D‑U‑N‑S numbers and surface an aged‑debt vs. predicted‑failure dashboard - these tools let Irish banks and fintechs turn thousands of ledger rows into a colour‑coded risk map, feed score thresholds into automated limit decisions, and keep a traceable portfolio view for compliance and collections prioritisation (D&B Credit Advantage).

Deutsche Bank's capital markets research and investment analytics give Irish capital‑markets teams a practical edge: the firm's Research Insights hub aggregates topical analysis for corporates and institutions (Deutsche Bank Research Insights hub), while the Quant Pulse series and Podzept podcasts unpack portfolio construction, risk‑on themes and policy shifts - recent episodes even walk through Fed signals, Jackson Hole takeaways and previews of ECB meetings that matter to Euro‑area investors (Deutsche Bank Quant Pulse and Podzept research archive).

For Irish asset managers and treasury desks, the combination of CIO commentaries and systematic research is especially useful: DB's investing insights translate macro views into actionable outlooks, and their quant work shows how diversified strategies like

Portfolio 365

can still eke out gains (a recent note cited a 9‑basis‑point improvement) even as markets oscillate - an analytic lifeline when a single ECB statement can swing yields and loan appetite overnight (Deutsche Wealth Investing Insights page).

PwC Ireland's regulatory-compliance and code‑change advisory should centre on the concrete requirements the EU AI Act and banking supervisors now demand: a searchable model inventory, formal risk classification of models, rigorous data governance and human‑in‑the‑loop checkpoints - all with staged transition windows from 6 to 24 months and penalties that can reach €7.5m–€35m (or 1–7% of global turnover) if controls are missing (see EY's EU AI Act primer).

Practical workstreams include automated audit‑trail generation for model updates, versioned code‑change pipelines that preserve explainability for credit and AML models, and end‑to-end testing harnesses that map RAG/GenAI outputs back to source evidence so supervisors can verify decisions; these are the same guardrails financial‑crime vendors recommend when embedding AI into AML stacks.

Given the EBA's finding that EU banks already use AI across credit scoring, AML/CFT and customer support, Irish firms benefit from combining governance playbooks with RegTech that surfaces bias, documents provenance and operationalises human oversight to keep innovation auditable and defensible (EY guide to the EU AI Act and implications for business compliance, Lucinity analysis of the EU AI Act's impact on financial‑crime detection tools).

“Fighting money laundering and terrorist financing contributes to global security, integrity of the financial system, financial stability, and sustainable growth.”

Sage and Microsoft Ireland are well placed to help Irish banks and fintechs move from one‑size‑fits‑all offers to genuinely personalised financial recommendations and scalable marketing by adopting proven segmentation and data‑platform patterns: build a unified customer profile (think a CDP that follows a client across mobile, web and branch), layer dynamic micro‑segmentation and predictive analytics to serve a “segment‑of‑one,” and use real‑time chatbots and prescriptive ML to turn signals into timely product nudges and targeted campaigns.

Industry playbooks stress that personalization isn't just bigger segments but machine‑driven, behaviourally aware recommendations backed by clear consent and GDPR controls (see why real‑time chatbots lift CSAT in the Kommunicate write‑up and why dynamic/micro‑segmentation drives relevance in Retail Banker International).

For Irish teams, the practical win is measurable: fewer irrelevant offers, higher conversion on up‑sell and cross‑sell, and marketing that scales without sacrificing privacy - skills that can be learned via targeted upskilling for finance staff to keep governance tight while experiments run fast.

“You've got to start with the customer experience and work back toward the technology, not the other way around.”

AccountsIQ - Treasury Forecasting, Cashflow & Liquidity Management: Irish treasuries need fast, auditable forecasts that stop surprises and free teams for strategy - short‑term, transaction‑level forecasting helps “avoid overdrafts and minimize idle cash,” while rolling and scenario‑based approaches keep longer horizons realistic and board‑ready (see practical cash flow forecasting methods).

Connecting ERPs and bank APIs into a treasury platform or TRMS gives daily cash positioning, real‑time visibility across multi‑entity, multi‑currency accounts and the clean data needed for machine‑learning uplift; practical guides stress automation, variance analysis and collaboration with FP&A so forecasts become decision tools rather than spreadsheet chores (learn about TRMS and forecasting tools).

For Irish firms, the payoff is concrete: fewer emergency borrowings, smarter working‑capital choices (lower cash conversion cycles) and faster, regulator‑friendly reporting - start with daily cash checks, clear data feeds and simple scenario prompts to prove value before scaling AI‑driven models.

“The ‘special sauce' of forecasting is the human element: knowing how to interpret the data and anticipate market uncertainty.”

Google Cloud's tooling makes automated reporting and visualization a practical, audit‑ready step for Irish financial teams: Contact Center AI's advanced reporting provides ready‑made dashboards for performance, queue and virtual‑agent analytics (note: the advanced extension must be enabled and is available only in supported regions such as europe‑west2) so compliance and ops can spot a queue jam or rising abandon rate in real time before SLA risk crystallises (Contact Center AI advanced reporting dashboards - Google Cloud CCAI platform).

For portfolio, product or transaction KPIs, export search and event data into BigQuery and surface board‑ready Looker dashboards - Vertex AI Search guides the BigQuery export and Looker setup so teams can turn raw events into materialized views and interactive KPI tiles that update automatically for reporting and stress testing (BigQuery to Looker KPI dashboards guide - Google Cloud).

The result for Irish banks and fintechs is faster, evidence‑linked decisioning: fewer surprise escalations, richer root‑cause drilldowns and a single source of truth for regulators and audit teams.

Chartered Accountants Ireland and Sia are translating regulatory pressure into practical capability for Irish finance teams: the Institute's “Six tips” for building AI literacy make clear that Article 4 of the EU AI Act (AI literacy obligations in force from 2 February 2025) turns training into a compliance baseline, not a nice‑to‑have - see the guidance for tailored, role‑specific courses, sandboxes and ongoing mentorship (Chartered Accountants Ireland: Six tips for building AI literacy, Chartered Accountants Ireland guidance on artificial intelligence).

Their new Virtual Reality Professional Scepticism programme gives junior and trainee auditors an immersive, consequence‑free space to practise judgement and spot bias or hallucinations before outputs hit customers - a hands‑on route to auditability and confidence that pairs neatly with governable AI sandboxes and measurable KPIs for uptake and oversight.

“[VR] gives learners a consequence-free space to make mistakes, get real-time feedback, refine their approach, and even test the outcome of a wrong approach. This promotes active skill application and helps learners build confidence in their skills, which is essential for a well-rounded professional accountant.” - Aisling Mooney

Getting started in Ireland means adopting a clear, staged playbook: pilot small and instrument everything, then lock governance onto every step so innovation remains auditable and defensible.

Begin with a time‑boxed proof‑of‑concept that avoids live customer data and pressures vendors on contract terms - William Fry's procurement guidance explains why trials, synthetic data and focused due diligence reduce legal, operational and reputational risk (William Fry AI in Financial Services procurement guidance).

Parallel to pilots, implement a model inventory, risk classification and ongoing monitoring that maps back to the Central Bank's supervisory expectations and the regulatory landscape outlined for Irish firms (Maples AI risk and regulatory considerations for Irish regulated firms), and take advantage of national supports such as the AI regulatory sandbox and skills programmes in the 2024 National AI Strategy.

Close the loop by investing in people: practical upskilling - for example the AI Essentials for Work bootcamp - gives teams the prompt‑writing, data‑handling and governance skills needed to scale safely while protecting customers and preserving trust (AI Essentials for Work bootcamp registration).

“With such a broad spectrum of potential uses for AI, there will be cases where judgements need to be made about whether it is appropriate to use AI for a particular process or business problem.”