AI in Financial Services
Month-End Shouldn't Mean Retyping Numbers Out of PDFs.
A due diligence firm cut report time in half. A finance company moved its monthly revenue forecast from a hand-built process to a live system. We build around the systems your team already uses. Start with a 30-minute call with Rob and a senior engineer.
Most first builds live in 6 to 16 weeks · Since 2010 · 100+ people in the US · SOC 2 Type II
A note from Rob
Most finance leaders I talk with aren't short on AI ideas. They're short on time, and they want to know exactly who can see their data.
If you're not sure where to start, tell me which report or review your team rebuilds by hand every month. We can start there.
A senior engineer and I will tell you plainly whether it's worth building, what it would take, and what your security team will want to see.
Rob Lubeck
Chief Revenue Officer, RTS Labs
Financial Firms We've Built For
Reports or reviews your team still rebuilds by hand?
Why It Matters
Hours of Assembly. Five Minutes of Decision.
In most finance teams, a skilled analyst spends hours pulling numbers, reading documents and re-keying data. Then the actual call takes five minutes. AI does the assembly. Your people keep the decision.
Where Most Teams Are Stuck
- A pilot that demoed well and never shipped
- Analysts re-keying numbers between systems
- Answers buried in PDFs, statements and shared drives
- Security review that stalls every AI project
Where RTS Labs Takes You
- One workflow live in production, inside your stack
- Analyst hours moved from assembly to judgment
- Every output traced to its source document
- Access controls and audit logs built to pass your security review
Sound Familiar?
The Problems We're Called In to Solve.
If one of these sounds like your team, it’s a good place to start.
“Our AI pilot never made it to production.”
The demo impressed everyone. Then it stalled with no integration path, no owner and no guardrails. Months later, nothing has shipped.
“My team lives in spreadsheets.”
Reconciliation, reporting and review are still manual. Smart people spend their days copying numbers instead of making decisions.
“Our data is too siloed for AI.”
Financial data sits in core systems, warehouses, documents and email. No two systems agree on the same number.
“We waste hours searching for answers.”
The answer is in a policy, a contract or a filing. Finding it means pinging three people and reading 40 pages.
“Security reviews stall every AI project.”
Every AI conversation hits the same question: can we show an examiner what it did? Usually that’s where the project stops.
“Month-end takes over the whole team.”
Close, reconciliation and the reporting pack eat days every month, and the commentary gets rewritten from scratch each time.
Recognize two or more?
Start with the one that costs most.
Thirty minutes with a senior engineer. We’ll tell you which workflow has the clearest payback and what it takes to fix.
Where We Apply It
Use Cases We Ship Into Production.
Specific financial workflows, each one built into the systems you already run. Filter by the part of the business you want to move.
Document Intelligence
AI Document Intelligence
Pain
Critical data sits in PDFs, statements and scans, read and re-keyed by hand.
We Build
Extraction pipelines that turn documents into structured, searchable data.
Impact
Hours of reading become seconds. Every value links back to its source page.
For: COO · Head of Operations
Document Intelligence
Loan & Deal Package Review
Pain
Analysts review loan packages and diligence files page by page, on deadline.
We Build
A review assistant that pulls terms, flags covenants and exceptions, and drafts the summary.
Impact
Faster, more consistent reviews, with a record of what was checked.
For: Head of Credit · Deal Teams
Document Intelligence
Compliance & Policy Copilot
Pain
Teams spend hours hunting through policies and regulatory text for one answer.
We Build
An assistant that answers from your own documents, with citations.
Impact
Answers in seconds. Every one sourced, so compliance can check it.
For: Chief Compliance Officer
Risk & Fraud
Fraud & Anomaly Detection
Pain
Rules engines bury analysts in false positives and still miss new patterns.
We Build
Models that score transactions against your history and flag anomalies for your analysts.
Impact
Fewer false positives, faster triage, and a reason logged for every flag.
For: Head of Fraud · Risk
Risk & Fraud
Risk Scoring & AI-Assisted Underwriting
Pain
Underwriting decisions are slow, inconsistent and hard to explain.
We Build
Predictive scoring on your own data, with the reasons shown for every decision.
Impact
Faster decisions on consistent criteria your team and examiners can follow.
For: Chief Risk Officer · Chief Credit Officer
Risk & Fraud
KYC & Onboarding Automation
Pain
Onboarding stalls on manual document checks, data entry and ID verification.
We Build
KYC workflows that extract, validate and route, with people handling the exceptions.
Impact
Shorter time to onboard and cleaner records, with the same controls.
For: Head of Operations · COO
Forecasting & Reporting
Cash Flow & Liquidity Forecasting
Pain
Forecasts get rebuilt by hand every cycle, and nobody can trace the numbers.
We Build
A forecasting pipeline that pulls operating data and updates projections continuously.
Impact
A live view of cash and liquidity that explains every number.
For: CFO · Treasury
Forecasting & Reporting
Investor & Board Reporting
Pain
Every reporting cycle means re-pulling data and rewriting the same narrative.
We Build
Automation that drafts board-ready packs and commentary from your source systems.
Impact
Your team reviews and approves. Nobody retypes.
For: CFO · Family Office COO
Modernization
Spreadsheet-to-Platform Modernization
Pain
A critical process runs on a spreadsheet only one person understands.
We Build
A real application with proper data, access controls and an audit trail.
Impact
It no longer depends on one person, and every change is logged.
For: CIO · CTO
Modernization
Internal Knowledge Search
Pain
Answers live in contracts, emails, reports and wikis nobody can search across.
We Build
One permission-aware search across your own content.
Impact
Staff find the answer in seconds, with the source attached and access respected.
For: Chief Data Officer
Modernization
Core Banking AI Layer
Pain
AI ideas die at the integration layer because the core can’t expose its data.
We Build
An AI and integration layer over your existing core: APIs, pipelines and assistants.
Impact
AI in production without replacing the system of record.
For: CIO · Chief Digital Officer
Document Intelligence
AI Document Intelligence
Pain
Critical data sits in PDFs, statements and scans, read and re-keyed by hand.
We Build
Extraction pipelines that turn documents into structured, searchable data.
Impact
Hours of reading become seconds. Every value links back to its source page.
For: COO · Head of Operations
Document Intelligence
Loan & Deal Package Review
Pain
Analysts review loan packages and diligence files page by page, on deadline.
We Build
A review assistant that pulls terms, flags covenants and exceptions, and drafts the summary.
Impact
Faster, more consistent reviews, with a record of what was checked.
For: Head of Credit · Deal Teams
Document Intelligence
Compliance & Policy Copilot
Pain
Teams spend hours hunting through policies and regulatory text for one answer.
We Build
An assistant that answers from your own documents, with citations.
Impact
Answers in seconds. Every one sourced, so compliance can check it.
For: Chief Compliance Officer
Risk & Fraud
Fraud & Anomaly Detection
Pain
Rules engines bury analysts in false positives and still miss new patterns.
We Build
Models that score transactions against your history and flag anomalies for your analysts.
Impact
Fewer false positives, faster triage, and a reason logged for every flag.
For: Head of Fraud · Risk
Risk & Fraud
Risk Scoring & AI-Assisted Underwriting
Pain
Underwriting decisions are slow, inconsistent and hard to explain.
We Build
Predictive scoring on your own data, with the reasons shown for every decision.
Impact
Faster decisions on consistent criteria your team and examiners can follow.
For: Chief Risk Officer · Chief Credit Officer
Risk & Fraud
KYC & Onboarding Automation
Pain
Onboarding stalls on manual document checks, data entry and ID verification.
We Build
KYC workflows that extract, validate and route, with people handling the exceptions.
Impact
Shorter time to onboard and cleaner records, with the same controls.
For: Head of Operations · COO
Forecasting & Reporting
Cash Flow & Liquidity Forecasting
Pain
Forecasts get rebuilt by hand every cycle, and nobody can trace the numbers.
We Build
A forecasting pipeline that pulls operating data and updates projections continuously.
Impact
A live view of cash and liquidity that explains every number.
For: CFO · Treasury
Forecasting & Reporting
Investor & Board Reporting
Pain
Every reporting cycle means re-pulling data and rewriting the same narrative.
We Build
Automation that drafts board-ready packs and commentary from your source systems.
Impact
Your team reviews and approves. Nobody retypes.
For: CFO · Family Office COO
Modernization
Spreadsheet-to-Platform Modernization
Pain
A critical process runs on a spreadsheet only one person understands.
We Build
A real application with proper data, access controls and an audit trail.
Impact
It no longer depends on one person, and every change is logged.
For: CIO · CTO
Modernization
Internal Knowledge Search
Pain
Answers live in contracts, emails, reports and wikis nobody can search across.
We Build
One permission-aware search across your own content.
Impact
Staff find the answer in seconds, with the source attached and access respected.
For: Chief Data Officer
Modernization
Core Banking AI Layer
Pain
AI ideas die at the integration layer because the core can’t expose its data.
We Build
An AI and integration layer over your existing core: APIs, pipelines and assistants.
Impact
AI in production without replacing the system of record.
For: CIO · Chief Digital Officer
Proof
What It Looks Like When It Ships.
Two financial workflows in production, with the numbers.
Forecasting · Finance Company
Revenue Forecasting, Rebuilt as a Live System
A growing finance company rebuilt its revenue forecast by hand every month. It was slow and hard to check. We built a forecasting pipeline that pulls their operating data, updates projections continuously and shows the reason behind every number.
Due Diligence · Financial Due Diligence Firm
Due Diligence Reports in Half the Time
Analysts at a financial due diligence firm spent six-plus hours per report on manual extraction. We built a platform that pulls financial metrics, compliance flags and risk indicators from source documents, with a citation behind every extraction.
“We cut report generation time by 50% and tripled our client capacity per analyst without adding headcount.”
Leadership team, financial due diligence firm
More From Financial Services
Firm-Wide AI Program · Financial Advisory Firm
13 business groups assessed. About 300 people trained on Claude. Eight initiatives scoped and costed in about eight weeks, then two ongoing engineering engagements.
Deal Platform · Investment Banking
An agent drafts partner pre-call memos from deal documents. Bankers build their own AI steps without engineers. Live in about 90 days, with the code and AWS account owned by the client.
Deal Intake · Private Credit Fund
An assistant reads incoming deal packages and drafts the summary for the investment team. Built as a proof of concept.
Document Search · Investment Management Firm
Plain-English search across about 5 TB of legal, contract and finance files. Teams find a clause, compare versions and turn contract terms into checklists.
Want this on your own workflow?
Start with a 30-minute call.
Thirty minutes with a senior engineer on the workflow costing you the most.
How We Partner
From Strategy to Production in Four Practical Steps.
We find the one workflow worth building, prove the data supports it, put it in production, then measure and scale.
Find the Highest-Value Workflow
A fixed-fee, two-week Discovery Sprint. We map opportunities against data readiness and ROI, then pick the one to build first.
Two weeks, fixed fee
Validate Data and Systems
Before we build, we confirm the data, integrations and security model can support a production system.
No build until the data checks out
Build Phase 1 Into Production
We build it into your core systems, warehouse and review queues, with evaluation and monitoring from day one.
Live in weeks
Measure, Improve and Scale
We track the metric we agreed on, tune the system, and hand off with documentation and training. You own it.
Your team runs it
Reference Architecture
The Reference Architecture Behind It.
Most systems we ship into a financial firm sit on the same seven layers. Each layer carries a control. The controls are what get a system through security review.
Source Systems and Ingestion
Connectors into core banking, loan origination, CRM and document stores, plus the email and file shares where half the real data lives.
ControlRead-only access by default. Every extraction logged at the source.
Data Foundation
Normalization, entity resolution and lineage, so a customer is one customer across every system.
ControlLineage from raw record to reported number.
Retrieval and Context
Search over your documents, tagged so the model retrieves the right material for the right user.
ControlPermission-aware retrieval. Nobody sees a document they aren't cleared for.
Models and Orchestration
Extraction, routing and agent workflows matched to the task, instead of one model doing everything.
ControlAnswers cite their sources. Low-confidence answers go to a person.
Evaluation and Monitoring
Accuracy measured against a labeled set before launch and after every change, with drift alerts.
ControlNo change reaches production without passing the test suite.
Governance, Security and Audit
Role-based access, encryption in transit and at rest, and logs of every input, output and decision.
ControlAn exportable record of what the system did and why.
Delivery Surface
Results land in the tools your team already uses: the CRM, the loan platform, the review queue.
ControlA person approves anything with financial or regulatory consequence.
Which layers need the most work depends on what you have today. For most financial firms, it’s the first two.
Why RTS Labs
The Team That Recommends It Is the Team That Builds It.
How we compare with the options you’re probably weighing: big consulting firms, offshore dev shops and AI prototype vendors.
See It in Action
Watch How We Ship AI Into Production.
A short look at how we take AI from pilot to production: the data work, the guardrails and the handoff.
Why Financial Leaders Trust Us to Build
Credibility You Can Check Before You Commit.
The facts a vendor review asks for.
Building Since 2010
We’ve shipped production software since 2010. Our AI work runs on the same delivery habits.
200,000+ Hours of AI Work
Delivered for clients in financial services, insurance, logistics, real estate and more.
100+ People, All in the US
The engineers on your project are RTS employees based in the US.
SOC 2 Type II
SOC 2 Type II attestation held by Rising Tides Solutions, LLC, the company behind RTS Labs. Report available under NDA.
Runs in Your Cloud
We can deploy inside your own AWS or Azure account, and you decide which model services can see your data.
You Own What We Build
The custom code and infrastructure we build transfer to you at handoff, in your repositories.
Tested Before Every Release
We measure accuracy against a labeled set before launch and after every change.
Measured From Day One
We agree on the number we’re moving before we start, and report against it the whole way.
Anthropic Claude Partner
RTS Labs is a member of Anthropic’s Claude Partner Network.
Who You'll Work With
Meet the Delivery Team.
Everyone who touches your data is an RTS employee based in the US, and the engineers who scope the work are the ones who build it. Our home base is Glen Allen, Virginia, just outside Richmond.
Director of Client Delivery
Team Lead, Software Engineer
Team Lead, Software Engineer
Named a 2026 Top Workplace by the Richmond Times-Dispatch, based on what our own people say about working here.
Our Clients' Words
Testimonials
“They were instrumental in standing up our data warehouse, laying a solid foundation for our data strategy and enabling smarter, faster decision-making.”
Tracy Elliott
Chesapeake Bank
“Their team quickly understood our business, asked insightful questions, and designed solutions that not only met our needs but empowered us to self-service as we grow.”
Melissa Carlucci
COO, The London Company
Questions, Answered Straight
Before You Talk to Us.
What is AI consulting for financial services?
It’s help choosing, building and running AI inside a bank, lender, wealth manager or fintech. A good partner finds the workflow where AI pays back, checks that the data can support it, builds it into your existing systems with the right controls, and measures the result. At RTS Labs, one senior team takes it from the first call to production.
How is AI used in financial services today?
Mostly to remove manual assembly work. Common uses include extracting data from statements and filings, reviewing loan and deal packages, answering policy questions with citations, scoring fraud and credit risk, automating KYC checks, forecasting cash and revenue, and drafting board and investor reports. Generative AI handles the reading and drafting. Predictive models handle scoring and forecasting. Most firms need both.
Which financial workflows pay back fastest?
The ones where a skilled person spends hours gathering information before a quick decision. Document-heavy review, reconciliation and reporting usually pay back first, because the data already exists and the time saved is easy to measure. One financial due diligence firm cut report time by 50% and tripled the number of clients each analyst can carry.
Can AI help with fraud detection and risk management?
Yes. A model scores each transaction against your own history and flags anomalies for review, with the goal of fewer false positives than your rules engine. Before anything goes live, we compare it with your current rules on your historical transactions and measure false positives, fraud caught and review workload. Your analysts still make the call, and every flag records why it fired.
Can AI automate financial reporting and the month-end close?
AI can automate much of the close. It can pull data, reconcile accounts, build the reporting pack and draft the commentary, including variance explanations. People review and approve instead of retyping. It’s most valuable when reporting still depends on spreadsheets and manual reconciliation across several systems.
Does it integrate with our core banking, loan and CRM systems?
Yes. We connect to core banking, loan origination and servicing, CRMs, ERPs, data warehouses and document stores using whatever each system supports: APIs, database connections or scheduled file feeds. We check every data flow during discovery, before you commit to a build, because AI only pays off when it runs inside the systems your team already uses.
Is our data good enough for AI?
You don’t need perfect data to start. You need access to the right data. Financial data is usually spread across core systems, warehouses, documents and email, and the systems rarely agree. We check what you have during discovery, and if the foundation needs work, our data engineering team does it first.
How long does it take, and how is it priced?
Every engagement starts with a fixed-fee, two-week Discovery Sprint, so you know the scope and price before you commit to a build. Builds are then priced as fixed-scope projects with milestones. Most first builds go live in 6 to 16 weeks, depending on data and integrations. The two examples on this page took 6 and 8 weeks. We agree on the metric up front and report against it.
How do you keep AI safe and auditable in a regulated firm?
We build the controls in from the start: scoped data access, role-based permissions, encryption, a log of every input and output, and a person approving anything with financial or regulatory consequence. Systems can run inside your own cloud account, and we agree with you which model services, if any, can see client data. The full set of controls is on our AI agent governance page.
Who owns the code and the data?
You own the custom code and infrastructure we build, in your repositories and your cloud account. During discovery we agree with you which model services, if any, can see your data. Third-party models stay under their providers’ terms. At handoff you get documentation and training so your team can run it, and you’re never locked in to us to keep it running.
Should we build custom AI or buy off-the-shelf fintech software?
It depends on the workflow. Off-the-shelf tools work well for standard tasks. Custom AI fits when the work is specific to your firm or the data spans several systems. Most firms land on a mix: keep the platforms that work and build AI around the gaps.
What happens after launch?
After launch, you decide who runs it. Some clients run the system themselves. Others keep us on a monthly support retainer or add an embedded AI team to build the next workflow. Running costs are mostly cloud and model usage in your own account, and we size them with you during the build.
How is RTS Labs different from a big consulting firm or an offshore shop?
The senior engineers who scope your project build it, connect it to your production systems with controls and monitoring, and stay through launch and after. Everyone on the work is an RTS employee based in the US. We’ve built production software since 2010 and delivered more than 200,000 hours of AI work.
Which firms specialize in AI architecture for financial services?
Three kinds of firm do this work. Global consultancies such as Accenture and Deloitte bring scale at enterprise pricing. Platform integrators tie the work to one core banking or wealth system. Independent engineering firms like RTS Labs design and build the architecture directly. Whoever you pick, ask them to walk you through an architecture they’ve actually shipped.
Let's Talk
Bring Us Your Toughest Financial Workflow.
Thirty minutes with a senior engineer. You’ll know whether AI can move the number and what it takes to ship it.
- A senior engineer on the call
- A read on feasibility, data and effort
- Which workflow to start with, and why