Generative AI in finance that reads the documents your analysts shouldn't have to.

Generative AI delivers the most value in financial services for: (1) Document analysis, reading and extracting structured data from loan applications, contracts, insurance policies, financial statements, and regulatory filings at a fraction of the manual review time. (2) Report generation, producing first drafts of financial analysis reports, client summaries, and portfolio updates from structured data that analysts then review and approve. (3) Compliance document review, checking documents against regulatory requirements, flagging non-compliant clauses, and generating compliance summaries. (4) Client communication, drafting personalised client updates, investment summaries, and advisory correspondence that relationship managers review before sending. (5) Underwriting support, surfacing relevant policy, precedent, and risk data to underwriters during the decision process. Workflows requiring regulatory-grade accuracy without human review are not yet appropriate candidates.

Financial data security requires specific architectural decisions. We use private LLM deployments (Azure OpenAI, AWS Bedrock, Anthropic Claude on private infrastructure) with data processing agreements that prohibit training on your data, rather than sending sensitive financial data to public APIs. For data that can be processed via public API under an appropriate data processing agreement, we implement data minimisation (sending only the relevant excerpt, not the full document). All financial data is encrypted in transit and at rest. Access is role-controlled and audited. We confirm the appropriate architecture based on your specific data classification and regulatory requirements during scoping.

LLM accuracy for structured extraction from financial documents (pulling specific values, dates, and terms from contracts and financial statements) typically reaches 90-98% with prompt engineering and validation layers, higher with fine-tuning on your document types. For unstructured summarisation and analysis, accuracy is harder to measure precisely, which is why human review workflows are standard for any AI output used in a financial decision. We implement validation pipelines: LLM extraction, confidence scoring, human review queue for low-confidence extractions, and accuracy reporting over time. We build in the measurement from day one so you can track and improve accuracy systematically.

A focused financial document analysis tool with LLM extraction, document upload interface, structured output, and human review workflow typically runs $25,000 to $60,000. A full financial AI platform covering multi-document analysis, report generation, compliance checking, and core system integration typically runs $60,000 to $150,000. Cost depends on document variety, integration requirements, accuracy validation depth, and compliance controls. We scope every project before pricing it.

Yes. Most financial AI projects we build connect to existing core banking systems, CRMs, document management platforms, or portfolio management systems via API. Common integrations include Salesforce, Temenos, Finastra, and custom-built policy or case management systems. We confirm integration feasibility and scope the connection points in week 1 before any development starts. Integration complexity is a major cost driver, so this is a question we ask early.

Yes. We sign NDAs before any discovery conversation where financial document types, system architecture, or client data is discussed. For production systems handling actual financial data, we also sign data processing agreements and confirm the appropriate LLM deployment model (private cloud, API under a data processing agreement, or on-premise) before scoping begins. Client confidentiality is standard practice, not a negotiation.