How AI Is Helping Financial Services Companies in Portugal Cut Costs and Improve Efficiency

Portugal's financial sector is already moving from experiment to scale: the Bank of Portugal's Alya platform now automates document summarisation, complaint triage and market‑sentiment warnings - helping supervisors move beyond sampling to more frequent, structured inspections - and national policy under the AI Portugal 2030 plan is building talent, HPC and testing infrastructure to support such pilots.

Consumers are part of the equation too: 67% of account holders used mobile banking in 2024, yet nearly 70% worry about how AI handles personal data, so banks must pair automation with ironclad security and clear communication (research shows perceived service quality and security drive trust).

That blend - practical automation, regulatory alignment and workforce reskilling - is where real cost savings and efficiency gains appear, and options like the 15‑week AI Essentials for Work bootcamp can equip teams with prompt‑writing and applied AI skills to deploy solutions safely and effectively.

“So far, it has not been possible to automate the entire rules, but [Alya] has been a major accelerator for certain processes,” Lima tells Central Banking.

AI matters for Portugal's banks and fintechs because it is the bridge between straight‑line cost cuts and the customer‑centric services that actually keep customers - security and perceived service quality are the currency of trust.

A national study of Portuguese mobile‑banking users finds that quality and security strongly predict trust, while clear relative advantage drives intention to use AI features, so any automation that speeds processing or personalises offers must also prove it protects data (Portugal mobile-banking study on AI trust and security).

At the same time, supervisors and operators are already reaping the operational wins: the Bank of Portugal's Alya platform automates document summarisation, complaint triage and market‑sentiment warnings - and notably flagged Credit Suisse at the top of news and research during last year's crisis - showing how AI scales oversight and early warning without adding headcount (Bank of Portugal Alya AI initiative for document summarisation and market sentiment).

“invisible, connected, insights‑driven”

Strategically, Forrester argues that generative AI and agentic systems let banks become advisors; in Portugal that means pairing RPA and genAI pilots with ironclad governance so savings and better customer journeys arrive together (Forrester report on reshaping banking with generative AI).

Process automation in Portugal's financial services is where rule‑bound toil meets fast, reliable scale: RPA excels at high‑volume, repetitive tasks - think reconciliations, accounts payable/receivable, onboarding, credit checks and document digitisation - so Portuguese banks and fintechs can free staff for higher‑value work while trimming costs and error risk (see a practical list of common RPA use cases at SS&C Blue Prism RPA use cases).

When combined with OCR and AI, those bots become “digital workers” that run 24x7, interact with legacy systems and accelerate compliance and reporting, letting teams close books faster or triage customer messages automatically (one Portuguese example automates triage and drafts compliant replies with ClickUp AI automated triage and reply drafting).

Real‑world wins elsewhere - State Street time‑to‑trade reduction case study cut time‑to‑trade by 49% and John Lewis forensic fraud checks case ran 20,000 forensic fraud checks in a single week - show the scale of potential gains if pilots are tied to governance and human‑in‑the‑loop controls (Infor RPA platform common flows like invoice processing and legacy integration).

The so‑what: a few well‑chosen bots can turn a back‑office bottleneck into overnight throughput, shaving days off processes that once sucked up headcount and attention.

“What took a person a minimum of six weeks to complete during the onboarding process, we got done with Blue Prism digital workers in just two days.” - Silvina Montemartini, Head of RPA, Santander

Intelligent customer engagement in Portugal is increasingly a hybrid game: generative AI and purpose‑built agents handle the straight‑line, high‑volume queries while smart routing and human handoffs keep complex, compliance‑sensitive work in expert hands - CTT's “Helena” is a local example, logging over 281,000 responses in three months, lifting NPS by 40 points and widening daily engagement by 60% as call‑centre volumes fell (Devoteam case study: Helena AI for CTT customer service).

Tools built for CX - like Lyro/Tidio - show how automation trims waiting times and boosts self‑service (Endeksa, which operates in Portugal, cut waiting times 59% and grew leads 138% after rollout) while platforms such as ClickUp AI can automate triage and draft compliant reply templates for Portuguese customer messages, keeping human oversight where regulators demand it (Tidio blog: AI-generated customer support case studies including Endeksa results, ClickUp AI customer message classification and triage examples).

The practical payoff is clear: faster first replies, higher deflection rates and a leaner service operation that still preserves the human touch for the moments that matter.

“Lyro allows us to use the power of LLM.” - Olek Potrykus, Head of Customer Experience at Tidio

Portugal's fight against fraud is shifting from static rulebooks to adaptive, learning systems that can surface unusual patterns in large institutional datasets: work on anomaly detection for the Portuguese central credit register shows how novel data‑quality and outlier methods help monitoring at scale, turning a haystack of credit‑register entries into a manageable set of suspicious cases (BIS IFC bulletin: machine learning for anomaly detection).

At the transaction level, ML‑driven payment screening emphasises a staged approach - unsupervised models to map “normal” behaviour, then supervised learning and continuous feedback loops plus human‑in‑the‑loop review - to reduce false positives while catching emerging fraud patterns earlier (IntellectEU: intelligent payment screening with machine learning).

Practical deployments in Portugal should therefore combine on‑premise hosting for regulatory control, explainable outputs for compliance, and targeted reskilling so analysts can validate model flags; guidance on high‑risk use cases such as credit scoring under the AIA reminds institutions that smarter detection must come with stronger governance (Nucamp AI Essentials for Work - credit scoring governance (2025)), making fraud teams faster and regulators more confident.

For Portuguese banks and fintechs looking to squeeze more value from the same data and teams, GPU acceleration is a practical lever: NVIDIA's finance brief highlights that GPU platforms can deliver dramatic speedups -

“milliseconds can mean millions”

in trading - reporting a 1,000X improvement over prior backtesting benchmarks, while the RAPIDS ecosystem (cuDF, cuML, cuGraph) brings zero‑code‑change accelerations that turn pandas and scikit‑learn pipelines into GPU‑native workflows (NVIDIA GPU-accelerated trading and backtesting brief, RAPIDS GPU-accelerated data science ecosystem).

Real, measurable wins elsewhere - faster Parquet IO, cuML speedups ranging from 5–175x, and case studies of financial adopters cutting training and processing times by orders of magnitude - mean Portuguese teams can move from slow, costly batch jobs to near‑real‑time risk scoring, fraud model refreshes and intraday analytics without rewiring existing codepaths.

The so‑what: adopting CUDA‑X/RAPIDS tools lets institutions reallocate compute budgets into more experiments and tighter governance, so models that once stalled overnight can iterate within a single business day (CUDA‑X Data Science libraries and use cases).

Portugal is building a practical middle ground between cloud‑hosted LLMs and local control by leaning into EuroHPC‑backed AI factories and national policy: the country is a partner in one of the first European AI factories linked to the MareNostrum 5 system, with an on‑territory interface to help SMEs and researchers access HPC and model‑testing resources and even the Vision supercomputer's 10‑petaflop class capacity for large experiments (Portugal's participation in European AI factories (MareNostrum 5)).

That industrial push sits alongside a strict compliance frame: the EU Artificial Intelligence Act applies directly in Portugal, national supervisors such as ANACOM and the CNPD are already on the roster, and GDPR constraints - especially around training data, deletion and explainability - make on‑prem or sovereign deployments attractive for financial firms that must keep sensitive datasets tightly governed (Portugal AI legal and regulatory overview (AI Act, GDPR, ANACOM, CNPD)).

Industry momentum for “sovereign AI” reflects this demand for local data and culture‑aware models, driven as much by cloud localisation and competitiveness as by regulators (Drivers behind sovereign AI investment in Europe), so banks and fintechs should pair on‑prem model hosting with ironclad contracts, human‑in‑the‑loop controls and documented conformity checks to stay both innovative and compliant.

“Sovereign AI is a relatively new term that's emerged in the last year or so.” - Chris Gow, Cisco

Optimization and decision automation are prime targets for Portuguese banks and asset managers seeking sharper returns and leaner operations: quantum‑inspired techniques such as the QUBO framework promise faster, accurate portfolio mixes by combining classical methods with quantum‑inspired penalty‑coefficient estimation and a two‑stage search preprocessor that was validated on a decade of quarterly data (arXiv paper: Quantum‑inspired QUBO portfolio optimisation), while practical toolkits and examples - like MathWorks' walkthroughs of VQE/CVaR and MATLAB support for quantum workflows - show how these approaches can be prototyped on familiar stacks (MathWorks guide: quantum computing for portfolio optimization with VQE and CVaR).

For risk teams, Fujitsu's “Digital Annealer” and related quantum‑inspired services turn enormous combinatorial problems (the copybook warning: more possibilities than there are atoms in the universe) into tractable reverse‑stress tests and intraday rebalancing runs, letting firms run richer scenario sweeps far more often without prohibitive compute cycles (Fujitsu Digital Annealer: quantum‑inspired optimization for financial services).

The practical payoff for Portugal: more frequent, higher‑quality allocation decisions, faster stress tests and decision automation that converts slow batch processes into next‑day (or intraday) insights, freeing teams to focus on strategy rather than manual tuning.

Start with a compact, risk‑aware implementation plan that ties AI pilots directly to measurable business outcomes: prioritise high‑volume, low‑risk wins (an internal digital assistant or automated triage) to build muscle and governance before tackling credit scoring or automated trading, which fall under high‑risk rules (MiCA, DORA, GDPR and the EU AI Act).

Run a proof‑of‑concept in a non‑critical environment, choose deployment (public cloud, private cloud, on‑prem or hybrid) based on data‑control and resilience needs, and conduct a formal risk assessment and DPIA for any high‑impact system; AdNovum's checklist of cloud‑vs‑on‑prem tradeoffs and six compliance factors helps frame those choices (AdNovum: How Banks and Fintechs Adopt AI the Safe Way).

Make governance practical: maintain an outsourcing register, document human‑in‑the‑loop rules, and prepare audit trails so supervisors can inspect models quickly; Portugal's FinLab and legal primers explain notification, outsourcing and supervisory touchpoints for pilots (Fintech 2025 Portugal - Trends and Regulatory Guidance (Chambers)).

Finally, pair rollout with targeted reskilling and a living playbook for model monitoring and incident recovery - start small, prove value, then scale under documented controls (Credit‑scoring governance guide for Portuguese financial services (2025)).

Next steps for Portugal's banks and fintechs are straightforward and pragmatic: scale the pilots that already show clear payoffs (suptech like Banco de Portugal's Alya proves document analysis and market‑sentiment flags can move supervision from slow sampling to near‑real‑time insight) while treating trust and security as the non‑negotiable core - research shows perceived service quality and security drive adoption, so every automation rollout must include human‑in‑the‑loop checks, DPIAs and customer‑facing transparency to keep adoption growing (Bank of Portugal Alya AI supervision platform, which already speeds document triage and flagged market stress in 2023).

Pair that risk‑aware scaling with open pilot pathways to industry partners (for example the Visa Innovation Program Europe fintech partnership) and targeted reskilling so staff validate models and design safe journeys - short, practical courses such as the Nucamp AI Essentials for Work bootcamp (15 weeks syllabus) turn skeptical teams into capable operators.

The combination of measured pilots, fintech partnerships, and workforce investment is the clearest route from one‑off wins to economy‑wide efficiency - think turning months of manual review into fast, auditable flags that free experts for higher‑value work.

“So far, it has not been possible to automate the entire rules, but [Alya] has been a major accelerator for certain processes.” - Filipa Lima, Bank of Portugal