"RaftLabs elevated my ideas and brought them to life when everything seemed impossible."
Grady Lakshmono
CoFounder, Gula (acquired by Runchise)
Generative AI in Finance Services
Generative AI in finance that reads the documents your analysts shouldn't have to.
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 designed around your regulatory requirements
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
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The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
01
Financial document review (contracts, prospectuses, regulatory filings) consuming analyst time that LLMs could handle more efficiently?
02
Report generation, client communication drafting, and compliance documentation being produced manually at high cost per document?
Plain answer
RaftLabs builds generative AI applications for financial services across the US, UK, Europe, Canada, and the UAE: document analysis, report generation, underwriting support, and compliance review.
What to remember
- RaftLabs builds generative AI applications for financial services covering document analysis, report generation, underwriting support, and compliance review.
- A focused v1 launches at a fixed price, then expands into the full platform, with compliance-ready architecture designed in from the start.
- Deployments use private LLM infrastructure (Azure OpenAI, AWS Bedrock) so financial document content never passes through a model provider's training pipeline.
The 50-page loan file that used to eat an afternoon.
An underwriter opens a 50-page loan application. Income statements, credit agreements, three years of filings. Two hours of reading before a single judgment call gets made, and then the next file in the queue looks exactly the same.
Now an LLM reads the file first. It pulls the figures, the dates, and the terms into a structured summary, flags the low-confidence fields for a human, and routes the rest. The underwriter opens the file already knowing where to look.
The model does the reading. The person does the deciding. That is the whole point.
The economics of generative AI in finance are not about replacing financial judgment. They are about removing the information-processing bottleneck that sits in front of it. An underwriter reading 50-page loan applications, an analyst summarising quarterly filings, a compliance team checking contract language: the model handles the reading and extraction, and the person handles the decision.
McKinsey estimates that generative AI could add $200 billion to $340 billion a year to the global banking sector. Most of that comes from higher productivity in document-heavy work: credit analysis, compliance review, and client reporting (McKinsey Global Institute, "The economic potential of generative AI", 2023). The demand is not in question. Turning it into production software a regulator will accept is.
- potential annual value gen AI could add to global banking, mostly from productivity in document-heavy work
- $200B-$340B
- McKinsey Global Institute, 2023
- of banks were already using generative AI tactically by 2024, yet few had moved it into governed production
- 78%
- eMarketer, 2024
Where most finance AI stalls is accuracy under audit. A general-purpose chatbot that is right most of the time becomes a liability the moment its output feeds a credit decision, a suitability assessment, or a regulatory filing. An LLM will state a fabricated figure with the same confidence as a real one. So every extraction runs through confidence scoring and a human review queue, and every AI-assisted decision is logged with the model version, the prompt, and the retrieved sources, so the reasoning can be reconstructed after the fact.
Data residency is the other decision that comes early. Financial document content and PII cannot pass through a public model provider's training pipeline. We deploy on private LLM infrastructure (Azure OpenAI Service, AWS Bedrock) inside your own cloud tenancy and region, under data processing agreements that prohibit training on your data.
RaftLabs has been shipping production software since 2015, with products in production across fintech, healthcare, hospitality, and logistics, and a 4.9/5 rating on Clutch. One team scopes the workflow, builds the system, and hands it over, with your regulatory requirements designed in from the start.
The first decision is not what to build.
It is whether software is the right answer at all. We use four possible recommendations before discussing a development scope.
- 01
Buy
Choose a maintained product when it handles the core workflow and the remaining gaps can be solved with configuration, a connector, or a small process change. Paying for software you do not have to maintain is often the better trade.
- 02
Repair
Keep the existing product when users still get value from it and the code can be understood safely. Start with evidence: where the code lives, how the data is organized, what is tested, which outside services it depends on, who can access what, and how updates go live.
- 03
Build
Own the system when the workflow shapes margin, customer experience, speed, or a product customers pay for. Start with one complete path and the system connections that path cannot work without.
- 04
Wait
Do not turn a changing policy into permanent software. Stabilise the rules, name the workflow owner, and measure the cost of the current process before paying for a product.
Not sure which path is yours? Bring it to a 30-minute call. Leave with the answer in writing.
Generative AI pays off where document review sits before the decision.
Everything on the left should already be true for your operation. Even one thing on the right, and a general-purpose tool or a manual process is the safer call for now.
A fit
Document-heavy financial workflows (contracts, filings, underwriting, reporting) consuming analyst time that follows the same pattern every time.
Compliance and data-security constraints that rule out public AI tools, with regulatory requirements the system has to meet.
Budget for a scoped build and a decision-maker who can define what accuracy standard 'done' means.
Not a fit
A workflow that needs regulatory-grade accuracy with no human review in the loop.
A standard need a configured off-the-shelf tool already covers well.
No sensitive-data or compliance dimension, just a simple question-and-answer responder.
What we build
Generative AI applications we build for finance
Financial document analysis
LLM pipelines that extract structured data from financial documents at the accuracy financial decisions require: loan applications, credit agreements, financial statements, insurance policies, and regulatory filings. Low-confidence fields route to a human review queue. We deploy on private infrastructure such as Azure OpenAI Service and AWS Bedrock, so document content never passes through a model provider's training pipeline.
Automated report generation
Financial report generation from the structured data already in your portfolio management system, risk platform, or CRM: portfolio performance reports, credit risk assessments, client investment summaries, and management information packs. Your compliance-approved templates define the structure while the LLM fills in narrative commentary, and every report routes to an analyst for review and sign-off.
Compliance document review
Automated review of financial documents against regulatory requirements, internal credit policy, and consumer protection obligations before execution or distribution, covering loan agreements (FCA, Regulation Z/TILA, ASIC), derivative contracts, and fund prospectuses. Output is a pass/fail report per check with the triggering passage, the regulatory basis, and a recommended fix, with material issues flagged separately from wording preferences.
Underwriting support tools
AI-assisted underwriting tools that compress information gathering so underwriters spend more time on judgment. The tool does not make decisions. It assembles structured applicant summaries, retrieves policy guidance and comparable historical cases via RAG with citations, and summarises risk factors. Every AI-generated input is logged with model version and sources, so decisions can be reconstructed after the fact.
Client communication automation
Personalised client communication drafts generated from data already in your portfolio system and CRM, so relationship managers review and approve rather than draft from scratch. Covers quarterly performance letters, onboarding welcomes, rebalancing recommendations, and event-triggered updates, with compliance guardrails enforcing required disclaimers and flagging regulatory risk language for mandatory human review.
RAG pipelines for financial knowledge
RAG systems purpose-built for financial knowledge: credit policy manuals, product approvals, regulatory guidance, and credit committee minutes. Hybrid retrieval combines vector search with exact term lookup for regulatory references like "IFRS 9". Access control filters results to authorised documents. Every response cites its source, and every query is logged to an immutable audit record.
Which financial workflow is buried in document review?
Walk us through it. We'll tell you what an LLM can handle, what stays with a human, and what it costs to build.
How it works
From scope to shipped
Every project follows the same four phases. Scope is locked and price is fixed before development starts.
- Phase 101
Discovery and scope
We map the financial workflow, the document types, the regulatory constraints, and the accuracy requirements. You leave the first phase with a written scope document and a fixed-price quote. No development starts without your sign-off.
- Phase 202
Design and architecture
Data architecture decisions, LLM deployment model (private cloud vs API under a data processing agreement), extraction pipeline design, and human review workflow. Decisions made here are far cheaper than the same decisions made late in the build.
- Phase 303
Build, integrate, and QA
Working pipeline at a staging environment early in the phase. Regular demos. Accuracy testing runs in parallel with the build using your document samples, not after build completes.
- Phase 404
Launch and post-launch support
Production deployment with monitoring and accuracy dashboards activated on launch day. Post-launch support is included in every project.
What clients say
What clients say about our AI work
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
I found RaftLabs to be the perfect partner for Perceptional, with their expertise in helping startup founders build MVPs, a free consultation, a prototype that matched my vision, and their unwavering support.
Amer Abu Khajil
Founder, Peak Studios & Perceptional
"I definitely recommend RaftLabs, especially to solo founders like me. Their clear communication and detailed discussions have always helped me make better decisions."
Gil Nugraha
Founder at UrShipper
Proof
AI applications we've shipped
- 01
Conversational AI platform for automated customer interviews
- from interview completion to usable insights
- 48 hours
- 02
Voice AI platform for large-scale group decision-making
- users in a single real-time audio discussion
- 300+
- 03
DropMock gave marketers cheaper, video-capable ad mockups PlaceIt didn't have
DropMock combined image and video ad mockups in one credit-based platform, undercutting PlaceIt's pricing and adding model-based scenes and video mockups it didn't have at the time.
How generative AI for finance is priced
We price by project, not by the hour. Cost depends on document variety, integration requirements, accuracy validation depth, and compliance controls. Start with the focused document analysis tool, then expand into multi-document analysis, report generation, and compliance checking once the first phase is proven.
- Focused document analysis tool
- LLM extraction, a document upload interface, structured output, and a human review workflow.
- Full financial AI platform
- Multi-document analysis, report generation, compliance checking, and core system integration.
How pricing works
Fixed scope, compliance built in.
A written scope, a timeline, and a number, agreed before development starts. Start with document analysis, then expand into the full platform.
Start with the focused document analysis tool. Expand into multi-document analysis, report generation, and compliance checking once the first phase is proven.
Starting investment
Starts at $10000
We price by project, not by the hour. Post-launch support is included in every project. After a 30-minute call, you get a written build, buy, repair, or wait recommendation plus first-phase direction. Free, whether or not you build with us.
No hourly billing
Once we scope your first phase, that price is locked in writing. No hourly billing, no surprise invoices as the platform grows.
Compliance built in
Your regulatory requirements are scoped during discovery, not retrofitted before launch. Private LLM deployments with data processing agreements, and audit trails for every AI-assisted financial decision from day one.
Work with us
Tell us where the work is stuck.
Bring the rough workflow, half-built product, or messy brief. We will map the smallest useful first move, then send scope, timeline, and price in plain English.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
Common 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 under an appropriate data processing agreement, we apply 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.
Accuracy depends on your document types. We run validation pipelines: LLM extraction, confidence scoring, a human review queue for low-confidence extractions, and accuracy reporting over time. 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 build in the measurement from day one so you can track and improve accuracy systematically.
In regulated domains like mortgages, the real challenge is not speed. It is proving reliability and auditability at scale. Hallucination risk in financial contexts is a real buying concern. Start with document processing: the use case where the model does the reading and a person does the deciding.
Track how freed-up capacity converts into revenue or cost savings. Total cost is token consumption plus data readiness plus governance plus retraining plus compliance, not just the license. Augmentation, not replacement: generative AI handles repetitive data-heavy tasks while humans keep relationship management and strategic advisory.
Where is the data coming from, and what is the compliance engine? In banking these are opening questions, not closing ones. Ask how your data is protected: private LLM deployments, robust data masking, and synthesized data for testing so real PII never reaches the core model. For credit scoring and other regulated decisions, ask for the fairness evidence rather than assuming the tooling away.
Ask how the vendor proves reliability and auditability at scale, not just speed. Ask where the data comes from and what the compliance engine is. Ask what accuracy standards are achievable for financial document analysis, and what breaks. Ask for the fairness evidence on model bias and fair lending. And ask about NDAs and data processing agreements before any discovery conversation.
We price by project, not by the hour, and we scope every project before pricing it. Cost depends on document variety, integration requirements, accuracy validation depth, and compliance controls.
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 during discovery 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.