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Tell us the financial workflows you want to automate and the compliance constraints you're working within. We'll scope the right solution and give you a fixed cost.
Financial document review (contracts, prospectuses, regulatory filings) consuming analyst time that LLMs could handle more efficiently?
Report generation, client communication drafting, and compliance documentation being produced manually at high cost per document?
Generative AI in Finance
Financial services are generating more documents, data, and decisions than teams can process manually. Generative AI in finance applies LLMs to the work that's currently bottlenecked on human review -- contract analysis, financial report generation, underwriting support, regulatory document processing, and client communication.
We build generative AI applications for financial services that work within your compliance constraints, handle sensitive data with appropriate security architecture, and deliver accuracy standards that financial decisions require.
Financial document analysis, extraction, and summarisation using LLMs trained for accuracy
Automated financial report generation from structured data with human review workflow
Underwriting support tools that surface relevant policy and risk data for decision makers
Compliance-ready architecture with data handling that meets your regulatory requirements
RaftLabs builds generative AI applications for financial services -- contract and financial document analysis, automated report generation, underwriting support tools, regulatory document processing, and client communication automation using LLMs including GPT-4o, Claude, and Gemini. Generative AI in finance requires specific data handling controls (no sensitive financial data sent to public LLM APIs without appropriate agreements), audit trails for AI-assisted decisions, and human review workflows for outputs used in financial decisions. Most financial AI projects deliver in 8--16 weeks at a fixed cost.
LLMs can process financial documents at a rate no analyst team can match
The economics of generative AI in finance aren't about replacing financial judgment -- they're about removing the information processing bottleneck that sits before it. An underwriter reviewing 50-page loan applications, an analyst summarising quarterly filings, a compliance team reviewing contract language: generative AI handles the reading and extraction, and human expertise handles the decision.
The constraint is accuracy and compliance. Financial AI applications require document processing accuracy standards and data handling controls that most general-purpose AI tools don't meet.
What we build
Financial document analysis
LLM pipelines for reading and extracting structured data from financial documents -- loan applications, credit agreements, financial statements, insurance policies, and regulatory filings. Entity extraction, date and term identification, clause analysis, and cross-document comparison. Confidence scoring and exception routing for low-confidence extractions requiring human review. The document processing that takes hours of analyst time per document and reduces it to minutes.
Automated report generation
Financial report generation from structured data inputs -- portfolio performance reports, client investment summaries, credit risk assessments, and regulatory submissions. Template-driven generation producing consistent, accurately formatted reports that analysts review and approve rather than draft from scratch. Commentary generation explaining performance trends and anomalies. The report production workflow that shifts analyst time from writing to reviewing.
Compliance document review
Automated review of contracts, loan agreements, and financial documents against regulatory requirements and internal policy -- flagging non-compliant clauses, missing required disclosures, and terms outside approved ranges. Compliance summary generation for audit documentation. Policy and regulatory change impact assessment across your document library. The compliance review that catches issues before they become regulatory problems.
Underwriting support tools
AI-assisted underwriting tools that surface relevant information for credit, insurance, or commercial underwriting decisions -- policy precedent retrieval, risk factor summarisation, applicant document analysis, and comparable case lookup. Decision support, not automated decision making. The information is presented to the underwriter; the judgment remains with the human. Accelerates underwriting without removing the qualified human from the decision.
Client communication automation
Personalised client communication drafts generated from portfolio data, transaction history, and market events -- investment updates, account summaries, advisory correspondence, and rebalancing recommendations that relationship managers review and personalise before sending. Tone and compliance guardrails applied at the generation layer. The communication volume that wealth management and advisory teams produce manually, accelerated.
RAG pipelines for financial knowledge
Retrieval-augmented generation systems connecting LLMs to your internal financial knowledge bases -- product documentation, regulatory guidance, credit policy, and historical case data. Question-answering over large internal document libraries with source citation. Onboarding tools for new analysts. Compliance FAQ systems for client-facing teams. The internal knowledge that's currently locked in documents and inaccessible at the moment it's needed.
Generative AI for financial workflows -- built for accuracy and compliance
Document analysis, report generation, and compliance automation built for financial services data handling requirements. Fixed cost.
Related services
Generative AI Development Services -- full generative AI application development
RAG Pipeline Development -- retrieval-augmented generation systems
AI Document Intelligence -- document processing and extraction
LLM Integration -- LLM API integration into existing systems
Generative AI Integration -- adding AI capabilities to existing products
Frequently asked questions
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 with appropriate BAAs, 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 -- LLM extraction pipeline, document upload interface, structured output, and human review workflow -- typically runs $25,000--$60,000. A comprehensive financial AI platform with multi-document analysis, report generation, compliance checking, and integration with your core financial systems typically runs $60,000--$150,000. Cost depends on document variety and complexity, integration requirements, accuracy validation depth, and compliance controls required. We scope every project before pricing it.