Hello NVIDIA community,
I am excited to share the first public technical overview of FinC2E — a specialised domain-adaptive financial AI engine deployed entirely inside the NVIDIA Private Registry, designed for enterprise-grade banking, sovereign-risk, and credit-analysis workflows.
FinC2E is not a general LLM.
It is a domain-constrained cognition & execution engine with deterministic structures, fully aligned to real financial governance frameworks (FSAP, ICAAP, EBA GL, Basel logic, sovereign-risk methodologies, SME/Corporate scoring systems, committee documentation, and audit-trace expectations).
🔧 Technical Architecture
Stack used:
NVIDIA Private Registry (model hosting & versioning)
NIM architecture for secure per-client model instances
Containerised runtime for high-throughput inference
LLM fine-tuning on structured JSONL datasets (risk, statements, templates)
Pipeline orchestration for deterministic output formatting
Audit-ready deterministic mode for financial governance
Human Processing Unit (HPU) orchestration for human-in-the-loop decisions
The model processes a blend of:
✔ Unstructured financial documents
✔ Structured inputs (balance sheet, liquidity metrics, qualitative scores)
✔ Scenario settings (stress tests, shocks)
✔ Document stacks for committees and supervisors
⚙️ Performance
On a single NVIDIA GPU:
Tousants financial analyses per hour
Deterministic, auditable outputs
Zero data exposure (entire workflow runs inside Private Registry)
Compatible with NIM deployment for multi-client isolation
A recent demo case:
FinC2E produced a full sovereign-risk assessment for Bosnia and Herzegovina (IMF EFF scenario) — including rating, bucket mapping, narrative, and stress commentary — in seconds.
Traditionally, the same report would require:
multiple analysts
2–5 days of preparation
multiple iterations & committees
manual structuring work
FinC2E compresses this workflow into single-pass inference.
📊 What FinC2E Outputs
Sovereign risk reports
Corporate / SME credit assessments
Committee-ready documentation
Stress test simulations
FSAP-aligned analytical narratives
Rating/bucket assignment
Governance-ready PDF / DOCX summaries
Structured JSON outputs for downstream systems
🧠 Why This Matters
Financial institutions globally face an expanding analytical load that cannot be matched by human teams alone.
FinC2E creates a machine-speed analytical layer, while preserving human command authority through a structured orchestration framework (HPU – Human Processing Unit):
Real-Time Tracking & Boarding
Effort-Based Credentialing
Human-Machine Quantum Convergence
This moves financial decision-making from manual, fragmented and slow → to continuous, consistent and explainable.
🧪 Pilot Program (Limited)
We are opening 5 technical pilot slots for:
Banks
Development funds
Ministries & sovereign finance units
Asset management & investment firms
Pilot includes:
Private Registry deployment
Model fine-tuning on internal templates
NIM instance integration
Secure, isolated inference channel
Benchmarking on real document stacks
📬 Contact
If anyone from the NVIDIA community (engineers, researchers, enterprise teams) wants to explore collaboration, benchmarking, or discuss deployment models — feel free to reach out.
📩 Email: info@bpm.ba
🔗 LinkedIn: https://www.linkedin.com/in/edin-vučelj-bpm-mainbrain/
Happy to provide a deeper technical brief or benchmark log.
— Edin Vučelj
Founder · BPM RED Academy
Creator of HumAI & FinC2E Engines
NVIDIA Private Registry Developer