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

Switzerland's financial sector is at an AI inflection point: a FINMA survey of around 400 licensed institutions found roughly half already use AI in day-to-day work, another 25% plan to adopt it within three years, and an astonishing 91% of AI users now deploy generative AI - reshaping customer service, credit and risk workflows while raising governance questions.

These fast-moving gains come with clear trade-offs: data quality, explainability and third‑party dependencies top the risk list in FINMA and industry analyses, so strategic oversight and staff training matter as much as models and cloud stacks.

For firms and practitioners that want to move beyond pilots, industry guides and analyses from Unique.ai and others highlight stepwise frameworks for governance and RAG-enabled use cases; practitioners can also build practical skills through training like the AI Essentials for Work bootcamp to close the talent and readiness gap.

The top 10 prompts and use cases were chosen with Swiss realities in mind: priority went to applications that align with FINMA's supervisory expectations (governance, inventories, documentation, explainability and independent review) and the Federal Act on Data Protection, reflect market uptake and maturity from FINMA's survey of ~400 licensed institutions (about 50% use AI day‑to‑day; 25% plan adoption within three years; adopters report ~5 live apps and 9 in development; 91% use generative AI), and address the highest operational risks flagged by supervisors - data quality, explainability and outsourcing to third‑party/BigTech providers.

Selection also weighed practical compliance benefits (e.g., lower false positives in AML/transaction monitoring) and deployability: clear data lineage, monitoring/alerting for model drift, and contractual safeguards for procurement.

Sources and legal-context guidance from Chambers' Artificial Intelligence 2025 summary and FINMA's survey informed the risk-based lens used to surface use cases with tangible governance and business impact for Swiss firms.

See the linked materials for the regulatory anchors and market figures.

Swiss banks are already proving that multilingual virtual assistants are more than novelty - they cut costs, speed service and keep channels open around the clock: Spitch's omnichannel conversational platform powers voice-biometrics and LLM-enabled assistants used by PostFinance, Migros Bank, Swisscard and St.Galler Kantonalbank to offer 24/7 support, automatic document ordering and even a voice-driven Personal Finance Manager in the SGKB app (Spitch omnichannel virtual assistant).

Deployments report tangible gains - automatic handling of a large share of routine queries, faster answers and fewer CRM update hours - while the broader Swiss market already lists dozens of specialist providers to choose from (Top virtual assistant companies in Switzerland).

The memorable payoff is simple: a caller can be recognised by voice, like a secure fingerprint, turning long waits into near-instant self‑service while freeing human agents for complex, regulated work - provided implementations keep data protection and bank policies front and centre.

Mastercard's suite of AI tools - from Decision Intelligence and Decision Intelligence Pro to market‑ready Transaction Fraud Monitoring - shows how real‑time scoring, behavioural biometrics and even synthetic‑fraud generation can tighten the payments funnel and cut manual work for Swiss compliance teams; a Mastercard study on training GANs for synthetic fraud generation demonstrates improved classifier performance and is a natural fit for scenario testing and stresstesting models (Mastercard AI Garage research).

In practice, the company scans roughly 160 billion transactions a year and scores events in as little as 50 milliseconds, while its Transaction Fraud Monitoring product can operate at 10 ms on‑premise (100–120 ms in cloud), letting banks intercept fraud at pre‑authorization and reduce false positives - measured uplifts include 2–3x better detection and a 7.4% increase in approvals for one acquirer (Business Insider on Decision Intelligence, Mastercard Transaction Fraud Monitoring).

For Swiss institutions the payoff is concrete: faster card blocking, fewer wrongful declines for customers, and realistic synthetic scenarios for model validation - provided governance, bias checks and human review stay front and centre.

"AI enables real-time detection of suspicious transactions by identifying patterns and anomalies impossible for human analysts to spot at scale."

Zest AI brings a practical bridge between Swiss lenders' need to lend responsibly and the rich promise of alternative data: its platform ingests cash‑flow signals (rent, utilities, mobile fees) and flags timing patterns like duration of delinquencies and gaps between missed payments so models capture repayment behaviour beyond bureau scores, which is especially useful for thin‑file or new‑to‑market borrowers.

By combining these inputs with robust machine‑learning workflows and an eye toward explainability, Zest AI helps translate complex model patterns into actionable underwriting signals that regulators and customer‑facing teams can interrogate; this follows the broader industry guidance on making ML outputs auditable and human‑interpretable (see FICO explainable AI in credit risk).

For practitioners wanting to operationalise these signals, Plaid alternative credit data catalog describes the same building blocks Swiss banks can permission‑link into their decisioning.

The pragmatic

so what

is clear: when models include verified cash‑flow and bill histories, lenders can safely expand access to credit without blind spots that traditional scores miss.

Swiss firms using AI for credit, onboarding or claims need a clear playbook for explainable automated decisions: the revised FADP (effective 1 Sept 2023) requires controllers to tell individuals when automated decision‑making is used, describe the logic and envisaged consequences, and give data subjects the chance to be heard or request human review - safeguards that make “regulatory‑safe” denial letters more than a checkbox (they must be concise, intelligible and actionable).

Practical steps include flagging high‑risk flows for a DPIA, keeping a register of processing activities, and designing denial notices that explain the key decision criteria without exposing trade secrets; recent CJEU and ADM guidance stresses exactly this balance between transparency and IP protection (so an explanation is meaningful, not a math lecture).

For Swiss practitioners, Securiti's FADP summary and guidance on DPIAs and subject rights is a useful Swiss‑specific reference, while legal analysis of ADM explainability clarifies what to include in access and denial communications to reduce disputes and enforcement risk.

Missing these requirements carries real consequences: fines of up to CHF 250,000 and reputational cost when customers feel shut out of decisions that affect their finances.

“Data protection should not be seen as an obstacle that slows down the company's growth. The opposite is true: data protection creates trust and security on the path of the company's digital transformation.” - Yasin Kücükkaya, Data Protection Officer

Aumico helps Swiss finance teams move financial reporting from reactive spreadsheets to repeatable, explainable insight by automating variance analysis, peer benchmarking and corporate performance narratives so boards get clear answers not noise; by treating variance analysis as a detective control that surfaces volume, price and efficiency drivers it turns month‑end surprises into a flashing amber on the CFO's dashboard rather than a late‑night scramble.

Practical levers include a monthly/quarterly cadence, materiality thresholds and column‑style flux reporting, stitched to ERP data for trusted actuals - practices covered in NetSuite's guide to flux/variance analysis - while AI‑enabled platforms can generate exec‑ready explanations and assign owners to investigate variances, such as Numeric's variance automation playbook and Abacum's budget‑vs‑actuals approach.

For Swiss institutions subject to tight audit and governance expectations, Aumico's approach combines clean data lineage, automated variance workflows and benchmarked corporate analysis so management sees both the what and the why in time to act.

For Swiss quant teams and traders who need repeatable, auditable strategy development, TrendSpider's visual Strategy Tester turns backtesting from an IT project into an operational step: configure hypotheses, pick timeframes from 1‑minute to monthly, set entry/exit rules and run tests across decades of history to see how a rule behaves in different market regimes (TrendSpider backtesting strategy configuration examples, TrendSpider Strategy Tester documentation).

The platform supports non‑programmers with point‑and‑click rule building, AI-assisted model labelling and an exportable results table, so risk teams can review KPIs and trace exactly which condition triggered an entry - a practical fit for Swiss governance needs where explainability and audit trails matter.

A striking detail: TrendSpider can draw every hypothetical entry and exit on the chart and - using up to 50+ years of history - complete full backtests in seconds, letting practitioners iterate dozens or hundreds of ideas before allocating capital.

For institutions mapping AI workflows to FINMA expectations, this workflow helps separate ideas that “look good” from strategies that survive real‑world stress and independent review.

FINMA Guidance 08/2024 places AI squarely inside Switzerland's AML and KYC playbook: supervised institutions are expected to strengthen AI governance, modernise data estates and adopt automated KYC/KYB, sanctions and PEP screening while preserving explainability and audit trails (FINMA Guidance on Artificial Intelligence and AML (08/2024)).

The guidance sits alongside Circulars 2018/3 and 2023/1 and the 2025 AML overhaul described by Moody's, which highlights the move to perpetual KYC, event‑driven risk triggers and automated UBO checks that now link into a central register covering more than 600,000 legal entities; the stakes are tangible - enforcement in 2025 exceeded CHF 100 million and senior managers can face accountability under AMLA and FINMA practice.

Practically, the Guidance nudges firms toward integrated platforms and RegTech that reduce false positives, enable AI‑assisted entity verification and keep transaction monitoring auditable, so compliance can be both faster and demonstrably effective (further context in Moody's analysis).

For Swiss insurers looking to cut cycle times and lift auditability, IMT Solutions pairs 15+ years of outsourced delivery with cloud migration, AI/MLOps, RPA and synthetic monitoring to automate back‑office, underwriting and claims workflows - work it lists on its company site and a dedicated case study for an “IMT Automate Insurance Processing for Swiss Insurer” shows local relevance (IMT Solutions: Trusted IT Outsourcing Provider, IMT case studies).

Practical implementations stitch intelligent document processing (OCR → NLP → IDP), rule‑based straight‑through processing for low‑risk files, and human‑in‑the‑loop checks for exceptions so that first‑notice‑of‑loss intake, adjudication and payments become auditable, standards‑aligned flows rather than late‑night spreadsheet triage - all while following ISO‑grade security and scalable delivery models.

Industry guides on automated claims processing and ICMS platforms outline the same building blocks and benefits (faster settlements, fewer errors, clearer audit trails), making IMT's stack a fit for Swiss governance, compliance and efficiency goals (Automated claims processing, Claims process automation guide).

Model monitoring and forecasting turn cash visibility from a rear‑view exercise into a forward‑looking control loop that Swiss treasuries and risk teams can actually act on: automated, AI‑enabled forecasts (short, medium and long horizons) reduce manual patchwork and surface timing mismatches that otherwise force expensive last‑minute borrowing, while scenario and stress‑testing workflows let CFOs rehearse best/worst cases and understand the cash impact of rate or FX moves (GTreasury cash flow forecasting guide: comprehensive cash flow forecasting for treasuries, Kyriba cash forecasting overview: what is cash forecasting?).

Practical controls include rolling forecasts, data‑lineage checks and automated alerts when predictive performance slips - a simple drift alarm can turn what used to be a month‑end scramble into an early amber on the dashboard - and governance must link these signals to approval gates and contingency funding plans as recommended in industry studies emphasising the rising urgency of accurate cash planning (EY insights: cash forecasting is more urgent than ever).

The memorable payoff for Swiss firms is resilience: faster, auditable decisions and fewer surprises when markets move.

Switzerland's Information Security Act (ISA) has turned cyber incident response into an operational requirement for firms that underpin essential services - including finance - so detection and reporting aren't optional: attacks on critical infrastructure must be reported to the NCSC within 24 hours (effective 1 Jan 2025).

That tight window makes AI‑driven anomaly detection a practical first line of defence - real‑time network analytics can surface subtle deviations, cut investigation time and even automate parts of the compliance trail - while human triage and clear playbooks keep decisions explainable and auditable.

Practical steps mirror industrial guidance: baseline normal activity, deploy ML‑based anomaly detectors that adapt to new threats, integrate with existing SOC tools, and link alerts to the NCSC's secure reporting channel so a discovered incident can be logged, supplemented and tracked.

The memorable test is simple: detect an intrusion early enough to file the 24‑hour report, or face fines and tender bans; AI helps spot the anomaly before it becomes a regulatory sprint.

See Exeon overview of the Swiss ISA reporting obligation and ISAGCA guide to implementing AI anomaly detection for concrete implementation tips.

Getting started in Switzerland means treating governance, procurement and training as a single, practical checklist rather than separate projects: begin by cataloguing every AI tool and third‑party component in a central inventory and classify each item by materiality and FINMA risk criteria (see FINMA Guidance 08/2024) so pilots don't turn into blind spots; extend existing risk frameworks to cover model risk, data quality and explainability as recommended in Unit8's governance guide and build clear RACI lines with named Model Owners, stewards and an AI oversight board; tighten procurement with due diligence, contractual SLAs for data lineage and audit rights, plus vendor testing and independent review; operationalise continuous testing, post‑market monitoring and drift alerts so performance slips are caught early; and close the loop with role‑based AI literacy and scenario training for front line, risk and procurement teams - practical training like the AI Essentials for Work bootcamp helps embed these skills.

Think of the inventory as a smoke alarm: one well‑placed system can turn a small governance spark into early action, keeping innovation on the right side of regulation and trust.