AML alert triage
Rank AML alerts by evidenced risk instead of static rules, so analysts spend their time on the cases actually worth escalating, with a documented rationale attached to every routing decision.
AI in finance
AI systems for banks, insurers and asset managers, built to run inside your compliance perimeter
Rexto designs and ships AI for financial firms: document intelligence, alert triage, and drafting copilots that sit inside your existing systems, deployed on infrastructure you control, with the audit trail your regulator will ask for.
The landscape
Where AI actually works in financial firms today
The systems reaching production in banks, insurers and asset managers this year are not autonomous decision-makers. They are extraction, triage, and drafting systems: pulling structured data out of loan files and KYC packets, ranking alerts by evidenced risk instead of static rules, and producing a first-pass credit memo or claims summary that a person edits rather than writes from scratch. Every one of them has a human checkpoint before the output reaches a client, a ledger, or a regulator. The pattern holds across banking, insurance and asset management alike: the underlying workflow does not change, only how much of the repetitive part of it a system now handles.
What doesn't work yet, at least not without a lot of caution, is full autonomy on anything with fiduciary or regulatory weight: a model approving a loan on its own, pricing an insurance risk with no underwriter in the loop, or making a trading call unsupervised. That's not a capability gap that will close with the next model release; it's a governance line most firms are right to hold, and the EU AI Act's high-risk classification for credit scoring and insurance risk-pricing exists precisely because a wrong call there has consequences a wrong product recommendation doesn't. The honest read: AI is doing the volume work that used to eat an analyst's week, not the judgment calls that justified hiring the analyst in the first place.
Extraction and drafting, not autonomous decisions
The production wins are in pulling data out of documents and producing a reviewable first draft, not in a model making the final call.
The back office, not the trading desk
Most working systems sit in operations, compliance and reporting, the high-volume, low-glamour work, rather than front-office decision-making.
Retrieval over your documents, not a general chatbot
The systems that hold up under review are grounded in your own policy wording, contracts and filings, not a general-purpose model answering from memory.
A human still signs off
Every system in this section keeps a person in the loop for anything that reaches a client, a ledger, or a regulator. That's a design choice, not a limitation we're working around.
Use-case map
What AI actually does inside a financial firm
Risk & compliance
The highest alert volume and the review process already exists: the natural place to start.
KYC/KYB onboarding automation
Extract and cross-check onboarding documents against sanctions and beneficial-ownership data automatically, cutting onboarding time without skipping a check a regulator would ask about later.
Solvency II reporting automation
Assemble Solvency II submissions directly from actuarial and policy systems, with full lineage back to the originating record instead of a spreadsheet nobody can fully explain.
Suitability & MiFID II monitoring
Flag advice or portfolio changes that fall outside a client's documented risk profile before they reach the client, not after a compliance sample catches it months later.
Operations
The highest-volume, most repetitive work in a financial firm, and the least visible to clients, which makes it the safest place to automate first.
Claims triage & FNOL automation
Route first-notice-of-loss submissions by severity and complexity so straightforward claims reach fast-track handling and complex ones reach a senior adjuster immediately, instead of sitting in a shared queue.
Core-banking data integration
Connect core-banking, ledger and case-management systems into a single consistent data layer, so every system built on top of it, AI or otherwise, works from the same facts.
Month-end close automation
Automate the reconciliations and variance checks that consume the last week of every close cycle, flagging genuine exceptions instead of requiring a full manual review of every line.
Reconciliation automation
Match transactions across settlement, ledger and processor records automatically, surfacing only the breaks a human actually needs to investigate.
Client-facing
Where firms are most cautious, for good reason, and where a policy-grounded assistant with a hard escalation boundary earns trust instead of losing it.
Contact-centre deflection
Handle routine account and product questions through an assistant grounded in your actual policy documents, escalating anything outside its confidence threshold to a human with full context attached.
Client reporting automation
Generate portfolio commentary and performance narratives from the data already in your systems, giving relationship managers a reviewed first draft instead of a blank page every quarter.
Policy wording Q&A copilots
Let agents and policyholders ask plain questions about coverage and get an answer sourced directly from the policy wording, with the clause cited rather than a generic summary.
Developer & merchant support copilots
Support integration and dispute questions from merchants and developers with an assistant embedded in your existing support tooling, backed by an engineer who can extend it as new edge cases turn up.
Finance & accounting
Drafting and analysis work that has always required a person to start from a blank template. Now it starts from a reviewed draft instead.
Credit memo drafting
Draft first-pass credit memos from financial statements, covenant data and prior exposure history, giving underwriters a structured starting point they edit rather than a blank document they write from scratch.
Workpaper review copilots
Surface exceptions and inconsistencies across audit workpapers automatically, so a reviewer's time goes to judgment calls instead of line-by-line cross-referencing.
Rent-roll data extraction
Extract rent-roll and covenant data from lease documents and servicer reports into a structured feed, so portfolio monitoring runs off current numbers instead of a quarterly manual refresh.
Real-time fraud detection
Detect anomalous transaction patterns using a model tuned on your own historical fraud cases, built to minimize the false positives that make every large processor's dispute queue miserable.
Private AI
Why regulated firms deploy in-perimeter, not against a public API
Most LLM products are built around a public API: your data leaves your network, goes to a third-party model provider, and comes back with an answer. For anything touching account data, KYC documents, credit files or non-public deal information, that is not a starting point your compliance or security team will sign off on, no matter how good the model is.
Three regulatory frameworks make the in-perimeter case concrete rather than theoretical. The EU AI Act classifies credit scoring and life/health insurance risk-pricing as high-risk under Annex III, which brings documentation, human-oversight and audit-trail obligations regardless of where the model runs. Running it inside your own infrastructure makes those obligations far easier to satisfy than retrofitting them after a public-API pilot. DORA treats a hosted model API as a third-party ICT dependency like any other vendor, subject to the same operational-resilience and concentration-risk scrutiny as your core-banking provider. And GDPR raises a cross-border transfer question the moment client PII or account data crosses to a model endpoint outside the EU, a question most data protection officers would rather not have to answer.
This is not a blanket argument for keeping everything in-perimeter. Some workloads genuinely benefit from a frontier model behind a well-scoped, logged API call; others need nothing more than a smaller open-weight model running on hardware you already own. Deciding which is which, backed by an evaluation on your own data rather than a benchmark leaderboard, is the first deliverable of an engagement, not an afterthought. Either way, the deployment sits on infrastructure your security team already monitors, not a new perimeter it has to learn to defend.
EU AI Act
Credit scoring and life/health insurance risk-pricing sit inside Annex III as high-risk; that classification brings documentation, human-oversight and audit-trail obligations that are far easier to build in from day one than retrofit after a supervisory review flags the gap.
DORA
A hosted model API is a third-party ICT dependency like any other vendor, subject to the same operational-resilience and concentration-risk scrutiny under DORA. Running the model inside your own infrastructure removes that dependency from the register entirely.
GDPR
Sending client PII or account data to a model endpoint outside the EU raises a cross-border transfer question your DPO will ask before go-live. In-perimeter deployment keeps the data, and the processing, inside the jurisdiction it already sits in.
Existing model risk governance
Most firms already have an internal model risk framework built for pricing and credit models. An in-perimeter AI system is built to live inside that same framework: logged, versioned, and reviewable, rather than becoming a shadow system nobody signed off on.
How to start
A first system in production, not a slide deck
01Start with an audit, not a build
A two-to-three-week AI strategy and readiness audit tells you which use cases your data can actually support, and which regulatory tier each one falls into, before anyone commits budget to a build.
02Choose how deep we embed
From an advisory engagement to a forward-deployed engineer working inside your team, the approach page lays out each engagement model and how it scopes and prices.
03Ship the first system on your infrastructure
The first production system runs inside your VPC or on-prem, with the access controls and logging your compliance team already requires from everything else you run.
Frequently asked questions
Is this still hype, or is there a working system we could point to?
Document intelligence, alert triage and drafting copilots are in production at financial firms today. This is not speculative. What's still maturing is fully autonomous decisioning; we scope engagements around what a model can reliably do now, not a roadmap promise.
How long before a first system reaches production?
A scoped first system, KYC document intelligence or AML alert triage for example, typically reaches a production pilot in 8-12 weeks, including the compliance documentation your review board will want to see.
Do we need to hire an in-house AI team first?
No. Most clients start with an embedded team under one of our engagement models and only build internal capability as the program grows; the systems and documentation are handed over so your team can operate them without us long-term. Where a client does want to grow an in-house team alongside us, that hand-over plan is scoped from the first engagement, not bolted on once the pilot succeeds.
Does this expose us to EU AI Act high-risk obligations we're not ready for?
Some candidate use cases do: credit scoring and life/health insurance risk-pricing are Annex III high-risk, for instance. We map every use case against the actual classification during the audit, so you know the compliance burden before you commit rather than discovering it after.
Can this integrate with legacy core-banking or policy administration systems?
Yes. This is usually the harder half of the work. We build the integration layer into your existing core-banking, claims or policy admin systems rather than asking you to migrate to a new platform first.
What does a realistic first-year outcome look like?
Typically one or two systems in production, handling a defined slice of a high-volume workflow such as an alert queue, a document intake process, or a reporting pipeline. The second and third systems get scoped from what the first one proved out, not a sitewide AI rollout in year one.
Talk to us about your first AI system
A 30-minute call to scope what a first system would look like against your own data, your own systems, and the compliance tier it falls into. No generic deck, just a straight read on what is worth building first.
Book a 30-min intro call