--- title: "Financial Services AI: Systems, Models and Data" url: https://ypai.ai/financial-services-ai-solutions/ description: "YPAI builds AI systems and custom models for financial services, and delivers the specialist data, annotation and evaluation needed to improve them." source: "src/copy/routes (route /financial-services-ai-solutions/)" --- # We build financial AI. And the data behind it. > YPAI builds AI systems and custom models for financial services, and delivers the specialist data, annotation and evaluation needed to improve them. From customer and employee assistants to document intelligence, agentic operations, decision systems and specialist models, YPAI turns financial workflows into production AI. Our AI Data & Evaluation teams collect, annotate and evaluate the financial, multilingual and specialist data behind those systems. Engage either service line independently. Connect both when performance depends on the system and its data. AI Implementation · AI Data & Evaluation · Connected Delivery ## A pilot impresses. Production asks three harder questions. ### Was the data ever the institution’s? - Generic corpora do not contain your product terminology, your low-quality scans, your rare events or your exceptions. A model that has not seen them fails on them. ### Does the system respect authority? - An answer from outside the approved sources, or an action beyond the user’s rights, is a control failure before it is a model failure. ### Is anything tested before release? - Without an evaluation set that represents production, every change to the model, prompt, retrieval or tool ships on impression. ## Buy the system. Buy the data. Or both, from one team. ### Delivered into production. - A working AI application, model or automated process for a defined financial workflow, with its integrations and controls. ### Traceable through the lifecycle. - The conversations, documents, taxonomies, evaluation sets and specialist review that train, test and improve financial AI. ### One team owns the failure. - The system and the data it runs on, built and fixed by the same people. Operated from Norway, EEA residency by default. ## This is what we build. And what it runs on. An adviser copilot inside an authenticated client session. The part that answers and acts was built for the institution. The part that learns was trained on financial data produced and evaluated for it. Illustration of a system YPAI builds. No institution, client or record is real. ### Which of this client’s accounts move to the new fee schedule on 1 October? Two savings accounts move to the new schedule on 1 October. The custody account is unaffected. Routed to the relationship manager for approval This exchange and its variants rerun as an evaluation set before every release ### Answers only from approved sources, acts only within the adviser’s authority - Retrieval over the fee schedule and the account register, cited inline. The fee waiver is routed to the relationship manager because the institution’s rules say so. ### Understands the question the way a banker asks it - Adapted and tested on financial conversations, difficult queries and regional language variants. ### Tested before every release - The same exchanges, with hard variants, form the evaluation set. A change to the model, prompt, retrieval or tool passes that set or does not ship. ## Where financial AI earns its place. Four workflows we build for most often. Each is a different system on different data, inside the institution’s own rules. ### Documents and financial operations - Unstructured financial material turned into a controlled workflow, with review where it belongs. - KYC and KYB workflows, onboarding and application processing, lending files, claims, reconciliation and regulatory-reporting support. - Annotated documents, extraction ground truth, entity relationships, exception taxonomies and hard negatives. Registered address differs from the identity document Routed to review · 1 exception ### Customer and employee intelligence - Assistants and copilots that answer from approved knowledge and escalate by your rules. - Digital banking assistants, contact-centre assistance, adviser copilots, policy assistants, call summarisation. - Financial conversations, intents, entities, difficult queries and regional language variants. Confirm identity, then lift the travel block. Card policy 4.1 cited. ### Financial crime, identity and risk - Stronger signals, less avoidable noise, and consequential decisions that stay reviewable. - AML and transaction monitoring, fraud investigation support, sanctions and adverse-media assistance, investigator copilots, threshold calibration. - Case-history evaluation sets built around false negatives, alert noise and uneven performance across products and markets. Structuring pattern across four accounts in eleven days Confirm · Escalate · Close. The investigator decides. ### Markets, research and decision support - Research compressed without losing the connection to source, calculation and review. - Research assistants over filings and news, credit and investment research, memo and committee-paper generation. - Internal records, licensed market sources, the desk’s own methodologies and graded outputs. Coverage moved with funding cost, not with operating cash flow [1] Note 12 · [2] Segment table · analyst review ## Build the system. Assistants, document AI, workflow agents, decision models and custom applications, delivered into production. ## Build the data the system runs on. Financial conversations, documents, taxonomies, evaluation sets and specialist review, traceable through the model lifecycle. The AI Data & Evaluation service line ## When the failure sits in the data, the team that built the system fixes the data. A document type missing from the training set, an unsupported language variant, weak labels, an evaluation set that does not represent production. No hand-off between a system integrator, a model vendor, an annotation company and an evaluation supplier. Each service line remains independently purchasable. ## Built in Norway, for institutions that answer to European regulators. Commercial model APIs, managed enterprise platforms, open-weight or self-hosted models, or a routed combination. Public cloud, private cloud, on-premises or hybrid. - Your private cloud, on-premises or hybrid Before a pilot starts, YPAI defines the accepted unit of work and attaches metrics, thresholds and a release gate to it. A model passes when it performs the financial task at the agreed level, not when it produces an impressive demonstration. Operates inside GDPR, DORA, the EU AI Act, AML and KYC obligations, MiFID II and your own model-risk and outsourcing standards. These obligations set how the work is run. - [Private enterprise deployment](https://ypai.ai/ai-implementation/private-enterprise-deployment/) - How the evaluation plan is built - [AI security and control](https://ypai.ai/ai-security/) ## What we tell institutions before they start. ### [GDPR and AI: Enterprise compliance requirements](https://ypai.ai/blog/compliance/gdpr-and-ai-articles-compliance/) GDPR applies directly to AI training data collection, model outputs, and automated decisions. What enterprise compliance officers must address in 2026. ### [EU AI Act Article 10: data governance checklist](https://ypai.ai/blog/compliance/eu-ai-act-article-10-data-governance/) ### [CTO's guide to sovereign AI: architecture and costs](https://ypai.ai/blog/infrastructure/ctos-guide-sovereign-ai-architecture-costs/) ## Bring us the financial work that needs to change. Start with a workflow, a prototype, a model, a dataset gap or a production failure. YPAI will define the shortest credible path from the current state to a measurable production deployment. Define · Design · Prove · Build · Release · Improve Pilots are scoped to your specification. Scope and commercial terms are agreed per project before the pilot begins, every pilot carries a SOW, acceptance criteria and a DPA, and production is a separate decision after pilot review and acceptance. Enquiry details are treated as confidential. Across banking, payments, lending, insurance, asset and wealth management, capital markets, financial technology and financial infrastructure. One brief. A project lead reads it, not a queue. ## What institutions ask before they scope. Five questions we hear in the first conversation. The rest are answered there. ### Can our data stay in the EEA, or inside our own environment? Yes. Project-specific processing, access, storage, review and delivery controls are designed around the data classification and the institution’s requirements, and defined before access begins. EEA-based, client-environment, private-cloud, on-premises and hybrid configurations are assessed and implemented per use case. ### Does every project need generative AI? Only where it is the strongest option. The architecture may use deterministic automation, retrieval, conventional machine learning, predictive models, specialist neural models, foundation models, agents or a combination. YPAI selects the least complex architecture that meets the performance and operating requirements. ### Can you work inside our stack, and with a prototype another team built? Yes. YPAI can assess and integrate existing models, cloud platforms, data infrastructure, enterprise applications and internal development work, including a prototype built by another team. The engagement does not require one model provider or a rebuild of systems that already meet the requirement. ### Do you build custom models, or configure existing ones? Both. YPAI can develop task-specific models, adapt foundation or open-weight models, implement fine-tuning and distillation, create model-routing and ensemble architectures, and integrate the result into production applications. Custom development is used when it offers a measurable advantage over configuring an existing model. ### Is YPAI a data vendor or a systems builder? Both. YPAI has two independently purchasable service lines. AI Implementation designs and builds production AI systems, models, integrations and operating controls. AI Data & Evaluation collects, annotates and evaluates the specialist data used to train, test and improve them. The two are connected when the system and its data need to be developed together.