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Agentic process automation
Agentic AI that reads context, makes decisions and executes multi-step processes end to end — without a human in the loop for every action.
AI for financial services: use cases, governance and how to start
From fraud detection to hyper-personalized banking, we help financial institutions use AI to operate with precision, speed, and trust.
In short
In financial services, AI is used today to detect fraud, read and structure documents, monitor regulatory change, support analysts and advisers, and automate back-office workflows. The real value sits where volume meets evidence: decisions that must be fast, explainable and traceable to a source. Credit scoring of individuals is high-risk under the EU AI Act, so governance has to be designed in, not added later.
Smarter, faster, safer.
- Real-time fraud detection and risk scoring
- Hyper-personalized financial services
- AI-driven credit scoring and loan automation
- Regulatory compliance monitoring with NLP
- Predictive analytics for trading and investment
- Virtual assistants for customer support
Solution plays
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AI Act governance for underwriting
Life and health pricing with AI is high-risk under the EU AI Act, and the obligations landed on 2 August 2026 — risk management, data governance, technical documentation, logging, human oversight. We build the governance layer around the models you already run.
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AI compliance monitoring
Continuous monitoring of regulatory changes, internal policy adherence and audit readiness — so your compliance team leads strategy instead of chasing paperw…
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AI contract intelligence
Extract obligations, flag risks and track key dates across your entire contract portfolio — without your legal team reading every line.
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AI dashboard builder
NL dashboard generation grounded in a governed semantic layer—analysts build in minutes, business users self-serve, IT keeps control. Powered by Synapse.
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AI document intelligence
Extract, classify and act on information from contracts, reports, invoices and forms — at the speed and scale no human team can match.
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AI financial reporting
Automate the assembly, reconciliation and narrative of financial reports — so your finance team spends time on analysis, not on data wrangling.
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AI fraud detection
Real-time AI that detects anomalous patterns, flags suspicious transactions and reduces false positives — so your risk team acts on signal, not noise.
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AI governance, risk & control
Build the controls, audit trails and risk framework that turn AI deployments from a liability into a governed, defensible part of your operations.
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AI HR assistant
Handle employee queries, automate routine HR processes and free your HR team to focus on the work that actually requires human judgement.
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AI learning & development
AI-powered learning that personalises content, tracks skill gaps and keeps your workforce ahead of what the business needs — without the overhead of traditio…
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AI onboarding assistant
Guide every new customer, employee or partner through onboarding with an AI assistant that adapts to their context, answers their questions and keeps them mo…
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AI patient & citizen assistant
An AI assistant designed for regulated environments — healthcare, public services and financial services — that handles sensitive queries with the accuracy, compliance and empathy they require.
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AI readiness & maturity audit
Independent assessment across data, talent, governance and tooling—with a sequenced remediation plan your teams can actually execute next quarter.
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AI research assistant
An AI research layer that searches, synthesises and structures information from internal and external sources — so your teams spend time on judgement, not on…
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AI risk intelligence
AI that aggregates, monitors and prioritises risk signals across your organisation — so your risk function acts on evidence, not on periodic reports.
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AI sales assistant
Give your sales team an AI assistant that qualifies leads, surfaces the right content at the right moment, and keeps CRM data accurate — without adding headcount.
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AI strategy & roadmap
Independent AI strategy: opportunity inventory, value sizing, build/buy decisions, governance design and a 90-day plan your board and teams can defend.
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AP/AR automation
End-to-end AP/AR on Synapse: capture, three-way match, approval, and audit trail. Finance owns the exception queue; the rest runs on rails.
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Data quality, lineage & governance
Lineage, quality SLAs, PII handling, and consent tracking on the pipelines AI consumes—so models pass audit and analysts trust the data.
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Employee onboarding concierge
A digital concierge that answers policy questions, completes paperwork, and routes to the right human—so new hires reach productivity faster.
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Enterprise AI-SDLC rollout
Eval harness, governed prompts, security gates and adoption telemetry—engineering ships faster, CISO signs off. Powered by Enterprise AI-SDLC.
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Enterprise Knowledge AI
Connect your documents, systems and expertise into a governed knowledge layer that anyone in your organisation can query — and trust.
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Executive decision dashboards
Narrative reporting with causal drill-down, proactive alerts and board-ready exports—built for the executive context. Powered by Synapse.
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Fraud & financial controls
Continuous transaction, vendor, and expense monitoring on Thinkia Sentinel—with risk-ranked triage and SOX-grade audit lineage from day one.
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HR policy assistant
Grounded answers with citations, jurisdictional variants, multilingual coverage, and human handoff—powered by Enterprise Knowledge AI.
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Legacy modernization with AI co-pilots
AI-assisted refactoring on COBOL, mainframe, and aging stacks—governance gates, regression tests, and audit trail from day one.
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Regulatory change monitoring
Continuous regulatory monitoring on Synapse with jurisdiction-aware feeds, control mapping, and audit-ready evidence—built for compliance ownership.
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Support cost reduction
Automate the queries that don't need a human, reduce cost-per-ticket, and protect CSAT — without replacing the agents your customers actually value.
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Third-party due diligence AI
Continuous third-party diligence on Thinkia Sentinel—sanctions, ownership, cyber posture, ESG—with audit-ready evidence and risk-owned review gates.
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Treasury cash-flow intelligence
Real-time treasury positions, FX exposure, and intercompany flows on Synapse—with stress scenarios and audit lineage treasury can defend.
The European angle: EU AI Act and sector rules
The EU AI Act (Regulation (EU) 2024/1689) lists in Annex III AI systems used to evaluate the creditworthiness of natural persons or establish their credit score as high-risk, with an explicit exception for systems used to detect financial fraud. Life and health insurance pricing and risk assessment, and AI used in hiring and people management, are also on that list. High-risk means risk management, data governance, technical documentation, logging and effective human oversight, whether you build the model or buy it as a deployer.
With the Digital Omnibus on AI in force since 27 July 2026, most Annex III obligations apply from December 2027, sixteen months later than first planned. The Omnibus does not change AI literacy (Article 4), mandatory since 2 February 2025, or Article 50 transparency, which applies since August 2026. Check the consolidated text on EUR-Lex or the AI Act Service Desk before planning around a date. The Regulation also lets financial institutions fold part of these duties into the internal governance they already run under financial services law.
The AI Act does not replace the rest of the rulebook. DORA governs ICT risk and third-party providers, which includes model and cloud suppliers; GDPR covers personal data and automated decisions; and supervisors already expect sound model risk management. This page is orientation, not legal advice: confirm obligations with counsel and official EU sources.
How to start
- Build an inventory of the models and AI tools already in use, including those bought from vendors, and classify each one against Annex III.
- Pick one document-heavy process with a clear owner, such as onboarding, regulatory monitoring or fund and product documentation, and define how success will be measured.
- Design traceability from day one: every answer or score linked to its source, model version and the person who reviewed it.
- Decide where data and models run (your infrastructure, EU cloud or external APIs) according to data sensitivity and DORA third-party requirements.
- Move to production with human oversight in the workflow and monitoring for drift and fairness, then extend to adjacent processes.
Success stories in this sector
Thinkia products that fit
Decisions you will face
Frequently asked questions
Is every AI system in a bank high-risk under the EU AI Act?
No. Annex III targets specific uses, such as credit scoring of individuals, life and health insurance pricing and AI in hiring. Fraud detection is expressly excluded from the credit-scoring category. Assistants, document processing and internal analytics usually fall under transparency or minimal-risk rules, though each case should be classified and recorded.
We buy our credit models from a vendor. Are we still responsible?
Yes, as a deployer. You must use the system as instructed, assign human oversight, keep logs and make sure input data is relevant. If you substantially modify or rebrand the model, you may take on provider duties too, so contracts should say who documents what.
Where does generative AI add value without touching regulated decisions?
In reading and structuring large volumes of documents, answering questions over internal knowledge with citations, monitoring regulatory change and preparing reports for analysts. These uses save time while a person keeps the decision.
Can we use external LLM APIs with customer data?
It depends on data classification, contracts and where processing happens. Many institutions combine options: sensitive workloads on their own or EU infrastructure, less sensitive ones on external APIs, with a gateway that controls routing, logging and costs.
How do we explain an AI-assisted decision to a supervisor or a customer?
By designing for it: logs that let you retrace an outcome, the sources behind each answer, documented limits of the model and a record of human review and overrides. Explainability added after go-live rarely survives an audit.
Further reading
- Human-in-the-Loop AI: A Practical Blueprint for Regulated Industries
- AI Fairness Monitoring: From Defensive Cost to Strategic Advantage
- Explainable AI (XAI): Building Trust and Transparency in AI-Driven Enterprise
- Deterministic AI: The Key to Enterprise-Grade Agent Reliability
- Secure On-Premises AI: Why the Mainframe Is Making a Strategic Comeback
Key terms
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