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Fraud & AML Triage
Detection models and agentic triage for fraud and anti-money-laundering alerts, where the false-positive rate is the whole problem.
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AI for financial services:
explainable by design.
Reduce manual reconciliation, support risk analysis and build auditable financial data pipelines — with every figure traceable to its source, and a named person disposing of every material decision.
Last reviewed 4 September 2026 · Engineering guidance, not legal or regulatory advice.
Where it breaks today
01
Data arrives as documents
Term sheets, SWIFT messages, invoices and regulatory filings arrive faster than analysts can process them, and most of what they contain never reaches a system in structured form.
02
Systems that do not talk
Spreadsheets, core banking mainframes and regulatory reporting software were not built to share state, so people carry data between them by hand.
03
Hours spent moving data
Analysts spend an enormous number of hours re-keying and matching figures between systems — expensive, slow, and the kind of work where errors are structural rather than occasional.
OUR APPROACH
Less manual reconciliation, with the decision kept
We build orchestration layers that sit above your existing infrastructure. Agents extract, interpret and match unstructured financial data — PDF term sheets, SWIFT messages, freight invoices — with a validation step your team owns, so a probabilistic output does not reach a ledger unchecked.
The same rule holds across every financial workload we take on. An alert is assembled and structured, but an analyst disposes of it. A credit file is extracted, but the decision and its explanation are produced in terms a declined customer can act on. A regulatory figure is drafted, but its lineage runs back to the source record in both directions. The system changes how long the work takes and how good the evidence is; it does not change who is accountable.
HOW IT DEPLOYS
PHASE 01
Map the ledger
We audit each reconciliation touchpoint across your core banking stack and classify workloads by risk, volume and regulatory exposure.
PHASE 02
Deploy inside the perimeter
Extraction and reconciliation agents go live inside your compliance perimeter, wired to your existing mainframes and reporting tools through validated, reviewed interfaces.
PHASE 03
Audit continuously
Every agent action is logged with a replayable trail — the evidence an examiner will ask for, and the time your team gets back.
What we build
01
Low-Touch Invoice Processing
Replace fragile OCR templates with multimodal models that read complex, multi-page invoices across formats and languages, with exceptions routed to a person rather than discarded.
SEE FINANCIAL AUTOMATION →02
AI Risk Analysts
Agents that monitor portfolios, ingest licensed alternative data, and draft structured risk summaries for an analyst to own — with every element linked to the record it came from.
SEE DATA ANALYSTS →03
Auditable Data Pipelines
On an on-premise or private-cloud deployment, PII and financial records have no route to a public endpoint. Built to GDPR-aligned controls, with access scoped by role and every material decision carrying a replayable audit trail.
SEE SECURE PIPELINES →04
Legacy Mainframe Wrapping
Modernise COBOL backends without a rewrite by engineering AI-native interfaces over existing databases, with reads and writes passing through validated, reviewed boundaries.
SEE LEGACY WRAPPING →Controls we implement
GDPR-ALIGNED CONTROLS
PII INSIDE YOUR PERIMETER
REPLAYABLE AUDIT TRAIL
FOUR-EYES ON MATERIAL DECISIONS
NO TRAINING ON YOUR DATA
ENCRYPTED IN TRANSIT AND AT REST
These are engineering controls we build into the architecture, not certifications. QQuantum.ai holds no security certifications, and the audits and attestations your sector requires remain yours to run — see our trust centre.
Financial services use cases
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FIN.02
Credit Decisioning
Model-assisted lending decisions under adverse-action and explainability obligations.
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Regulatory Reporting
Assembling, reconciling and evidencing regulatory submissions with every figure traceable to its source.
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Questions
Questions about Financial Services.
How does AI automation work for financial reconciliation?
Agents ingest unstructured financial documents — PDF term sheets, SWIFT messages, freight invoices — and extract, normalise and match line items against your core ledger. Unlike template-based OCR, multimodal models read document semantics, so new formats and languages are typically handled without reconfiguration. Every match is logged with a replayable audit trail, and anything below a confidence threshold goes to a person rather than to the ledger.
How do you handle security and GDPR for banks?
By architecture rather than by assurance. On an on-premise or sovereign-cloud deployment, PII and financial records have no route to a public endpoint and are not sent to a provider that could train on them. Access is scoped by role, every material decision leaves a replayable trail, and data is encrypted in transit and at rest. Where a hosted model is used, we record the provider's retention and training terms in the architecture document, at the time of writing, and you should confirm them against the provider's current terms. What we do not bring is assurance: QQuantum.ai holds no security certifications, so the evidence we produce supports your audit rather than substituting for it.
Can AI agents integrate with legacy COBOL mainframes?
Yes. Instead of a rewrite, we engineer AI-native interfaces that wrap existing COBOL backends and core banking databases. Agents read from, and write to, legacy systems through validated, reviewed interfaces with a schema check on every write, so workflows modernise while the underlying mainframe stays untouched.
What return should we expect?
We will not quote you a percentage, because any number we have not measured on your process is marketing. What we will do is measure the current cost of a specific workflow — reconciliation breaks, alert triage, document intake — build the case on your own figures, and tell you if it does not clear. The cost model page sets out the method, including the lines usually left out.
Keep the decision.
Automate the assembly.
Start with an architecture review of one workflow — reconciliation, alert triage or document intake — measured on your own figures.
30-minute technical call · no deck
