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FinSage

AI-Powered Financial Intelligence

Our flagship financial-intelligence platform. FinSage uses AI modules to turn complex financial data into practical, actionable insight, built on a custom, security-first architecture.

AI
Fintech
Analytics
Security

overview

What it is

An AI-powered AML compliance platform for banks and compliance teams. Users upload financial documents; FinSage extracts the transactions, scores AML risk across transaction-pattern and behavioral categories, and produces regulation-cited, traceable findings. It adds Entity Intelligence (KYB and UBO discovery via live business registries), a Wolfsberg CBDDQ V1.4 onboarding form engine, and a conversational AI chat over loaded project data. It is built on FastAPI (Python 3.13) and React 19, with Google Gemini 2.5 Flash as the primary AI provider. FinSage provides analysis and evidence — it does not file SARs and is not regulatory compliance on its own.

capabilities

Key features

Custom-Engineered Architecture

Built from the ground up with a security-first design rather than assembled from off-the-shelf parts.

AI-Powered Analytics

AI modules transform complex financial data into practical, actionable insight.

Real-Time Intelligence

Automated reporting and anomaly detection surface what matters as it happens.

Enterprise-Grade Security

Security is a foundation of the architecture, not an afterthought bolted on later.

why it matters

Benefits

Structured, regulation-cited AML findings drawn from uploaded documents — with evidence citations to support SAR preparation, instead of manual reading.
A module plugin architecture lets new checks be added without touching the existing analysis pipelines.
A unified EU AML framework treats AMLD4/5/6 and AMLR as cumulative layers.
Parallel live-registry entity enrichment, with a per-registry timeout, for KYB and UBO discovery.
An immutable, WORM-style audit trail with full AI run-log traceability.

in practice

Use cases

An analyst uploads statements and runs a parallel finance, behavioral, and entity scan into a consolidated scorecard with citations.

A compliance officer runs a Wolfsberg CBDDQ onboarding with a reviewer workflow.

An external applicant uses the public onboarding portal (in development).

An auditor reviews the immutable AI run log behind a finding.

An analyst asks the in-project AI chat about ratio trends across the loaded data.

the story

Problem, approach, outcome

Problem: Compliance teams drown in manual AML review of financial documents.
Approach: An AI-powered AML platform that extracts transactions and scores risk across pattern + behavioral categories, with KYB/UBO entity intelligence and a Wolfsberg CBDDQ form engine.
Outcome: Structured, regulation-cited AML findings from uploaded documents — traceable and audit-ready.

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Let's talk

Interested in FinSage, or want to see how it could fit your work? Email is the best way to reach TRAGenX — send us a note and we'll get back to you.

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