AI for Financial Services
Fraud, lending, and compliance support with models that keep your data in house.
A short call to see if this is a fit. No pressure, no slides.
Use cases
Fraud check support
An agent flags suspicious transactions, gathers the surrounding context, and routes the close calls to a human reviewer with a summary. It speeds up triage without making the final call alone.
Loan processing
An agent reads application documents, pulls the required fields, checks them against your rules, and assembles a clean file for an officer to decide on.
Compliance support
An agent retrieves the relevant policy, checks a document or transaction against it, and writes up what it found, with citations to the source rule.
Statement and filing parsing
Statements and filings arrive in many formats. An agent extracts the figures you need and writes them into your system, with a source reference for each.
Where AI fits in financial services
Finance runs on documents, rules, and review queues. An agent can read the documents, apply the rules, and prepare the file, so your reviewers spend their time on the decisions that matter.
The constraint is data: much of it cannot go to a public model. So the design is private first.
Keeping data in house
We run open-weight models on hardware you control. When the system needs answers grounded in your own documents, we use private RAG with citations on every claim. When AI goes into an app you already run, AI integration puts the model behind a clean API layer, and standards like the Model Context Protocol keep the wiring auditable.
Keeping a human in the loop
For fraud and lending, the agent prepares, the person decides. We design the workflow so the final call, and the accountability, stays with a human.
How we deliver
Start with a short readiness audit to pick the workflow with the clearest payback and the lowest risk.
Honest cost note
Private infrastructure is more expensive than a hosted API. For regulated data that is the correct trade. For low-risk internal tasks with no sensitive data, a hosted model can be cheaper, and we will point that out.
FAQ
See the questions above on decision-making and data residency.
Frequently asked questions
Can AI make the final lending or fraud decision?
We design it not to. The agent gathers context, checks rules, and prepares the file. A person makes the decision and owns it. That keeps you on the right side of fair-lending and accountability rules.
How do you handle data that cannot leave our network?
We run open-weight models on hardware you control and use private RAG so answers stay grounded in your own documents. Nothing is sent to a public model.
Solutions for this industry
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See if AI fits your team
A short call to see if this is a fit. No pressure, no slides.
Book a scoping call