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Financial Services

AI Solutions We Can Build for Banks

What Technspire builds for banks and financial-services teams: AI customer service on Azure OpenAI, retrieval-augmented knowledge platforms, AML and fraud-detection pipelines, and M365 Copilot rollouts. This page describes our offerings. Talk to us about applying them in your organisation.

reference architecture, not a client story

Why banks invest in AI

Retail and corporate banks share a small set of structural pressures: high inbound customer-service volume, knowledge fragmentation across decades of internal documentation, growing compliance burden under PSD2, DORA, and the EU AI Act, and demand from operations teams for productivity uplift. AI shifts these from cost centres into competitive surfaces. The work is not the AI itself; the work is wiring it into the bank's data, identity, and compliance fabric without breaking any of them.

Technspire builds the kind of AI workloads that survive a Finansinspektionen examination: scoped data access, audit trails, human oversight where the regulation demands it, and architecture choices that keep customer data inside the bank's tenancy.

What we can build

AI customer service on Azure OpenAI

Conversational interfaces for retail banking customer support, integrated with the bank's CRM and core banking systems. Retrieval-augmented generation grounds answers in approved knowledge sources; PII detection and redaction sit at the boundary; agent assist surfaces draft responses to human agents for high-stakes queries.

Stack: Azure OpenAI, Azure AI Search, Azure Functions, Azure API Management, Entra-backed identity, Application Insights for cost and quality telemetry.

Retrieval-augmented knowledge platforms

Internal RAG systems that index policy manuals, regulatory documentation, product specifications, and customer correspondence. Hybrid keyword and vector search with semantic re-ranking returns the right document for the right user, scoped by Entra group membership.

Stack: Azure AI Search, Azure OpenAI embeddings, Azure Storage with immutability for compliance, Microsoft Graph for source integration, Foundry IQ where available.

AML and fraud-detection pipelines

Transaction-monitoring and anti-money-laundering pipelines that combine rule-based scoring with ML classifiers. Production-shaped deployments with explainability surfaces for compliance officers, audit logging that satisfies Finansinspektionen, and human-in-the-loop review for any flagged action.

Stack: Azure Machine Learning, Azure Stream Analytics, Microsoft Fabric where in scope, Sentinel for cross-cutting security signals.

M365 Copilot rollouts for operations

Structured deployment of Microsoft 365 Copilot for back-office banking operations: governance design, role-based licensing, permission-scoping in SharePoint and Teams, and the change-management work that determines whether the rollout produces measurable productivity uplift or shelfware.

Stack: Microsoft 365 Copilot, Entra Conditional Access, Microsoft Purview for data classification, Copilot Studio for custom workflows.

Built for the regulatory perimeter

Every workload we ship for financial-services teams accounts for: GDPR data-subject rights, PSD2 strong customer authentication, DORA ICT-resilience requirements, EU AI Act classification (most banking AI use cases land in Annex III high-risk), and Finansinspektionen guidance on third-party ICT services. The architecture decisions that satisfy these are not retrofitted; they are baked into the deployment from day one.

We provide the technical documentation auditors expect: Annex IV templates for high-risk AI systems, Article 12 logging schemas, human-oversight design records, and data-governance evidence under Article 10.

How a typical engagement works

  1. Discovery (2–4 weeks). Architecture review of the existing platform, integration mapping for the systems the workload must touch, risk classification under the EU AI Act, and a scoped proposal with named engineers and a fixed first-deliverable date.

  2. First vertical slice (4–8 weeks). One working slice in production or staging: a single customer-service intent, a single fraud rule, a single retrieval index. Real telemetry, real evaluation, real handover plan.

  3. Sustained delivery. Iterative expansion of the slice into the full workload, with the bank's internal team progressively taking ownership. Documentation, evaluation suites, and on-call runbooks land alongside the code.

Want to discuss what we can build for your bank?

Short conversations are free. Bring an architecture question, a procurement decision you are weighing, or a compliance scenario you want a second opinion on. We respond within one business day.

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