NeuroChain — Complete Technology Documentation
A visual, step-by-step guide to understanding AI-Augmented Financial Settlement Infrastructure
What is NeuroChain?
Understanding the core concept in simple terms
🧠
"A Currency That Thinks"
NeuroChain is an AI-powered trust layer for India's financial system. Think of it as a shared intelligence network that connects 9,306 NBFCs (Non-Banking Financial Companies) so they can instantly detect fraud, verify borrowers, and process transactions — all in under 50 milliseconds.
Unlike Bitcoin or cryptocurrencies, NeuroChain is not speculative. It is enterprise financial infrastructure — like building the road network that banks and NBFCs drive on, rather than building the cars themselves.
🔍
Like a Credit Bureau, But Instant
Today, checking a borrower takes days and costs ₹15-50 per query. NeuroChain does it in <50ms, sharing fraud intelligence across all institutions in real-time.
🛡️
Like an Immune System
Just as your body detects harmful bacteria automatically, NeuroChain's AI detects fraudulent transactions, duplicate borrowers, and money laundering attempts — automatically, 24/7.
🌐
Like UPI, But for Trust
UPI connected payment apps. NeuroChain connects the intelligence layer — letting NBFCs share fraud data, verify identities, and trust each other's borrowers instantly.
9,306
Registered NBFCs in India
RBI, June 2024
₹16.58L Cr
NBFC Gross Payables
RBI FSR, March 2024
230M
Unbanked Adults in India
World Bank Findex, 2021
<50ms
Fraud Scoring Latency
NeuroChain SLA Target
The Problem We're Solving
Why India's NBFC sector urgently needs a shared trust infrastructure
Current State — Broken & Siloed
1
No Inter-NBFC Trust Network
9,000+ NBFCs operate in complete silos. A borrower blacklisted by one NBFC can immediately borrow from another. There is zero shared intelligence.
2
Expensive, Stale Credit Data
NBFCs pay ₹15-50 per credit query to CIBIL/Experian. At scale, this costs the sector ₹100Cr+ annually. Data is updated only monthly — far too slow.
3
Systemic Fraud Reporting Delays
Under RBI CRILC regulations, NBFCs must report fraud, but delays are systemic (RBI FSR 2024). Real-time fraud detection simply does not exist.
4
Missing AML in Smaller NBFCs
Upper Layer NBFCs have AML tools. The 9,000+ middle and base-layer NBFCs lack any real-time Anti-Money Laundering infrastructure.
5
Rural India Left Behind
Rural areas produce 47% of India's GDP but receive only 9% of banking credit (CRISIL MI&A, 2024). Fraud losses raise interest rates for the most vulnerable.
With NeuroChain — Connected & Intelligent
Shared Fraud Intelligence
Every NBFC sees fraud signals from the entire network. One detection protects all 9,306 institutions. The network grows smarter with every member.
Real-Time, Low-Cost Scoring
₹5-15 Lakh/year flat fee replaces per-query charges. Scores update in <60 seconds. Cost reduction of 80%+ versus traditional credit bureaus.
94%+ Fraud Detection Rate
AI-powered GNN (Graph Neural Network) models detect duplicate borrowers, velocity attacks, identity spoofing, and collusion patterns in real-time.
Built-in AML Infrastructure
Bloom filter watchlist matching, behavioral biometrics, and device fingerprinting — available to every NBFC, not just the large ones.
Financial Inclusion at Scale
Lower fraud = Lower interest rates = More credit access for 230M unbanked Indians. Each NBFC serves 50,000-500,000 customers.
How NeuroChain Works
The Two-Plane Architecture explained simply
The Core Design Principle
NeuroChain separates "thinking" from "executing". The AI brain works in the background, continuously learning about fraud patterns. The transaction engine reads these scores instantly — AI never slows down a transaction.
TRANSACTION PLANE
Deterministic C++ Core
Layer 1
AABFT Consensus
Validator selection, Byzantine fault tolerance
< 1.5s finality
Layer 2
Object-Centric DAG
Parallel execution, owned-object fast path
< 200ms owned
Layer 3
Fast Rule Engine
Bloom filter AML, rate limiter, Ed25519 signatures
Sub-ms checks
Layer 4
WASM Smart Contracts
Deterministic execution, loan contracts, insurance
wasmtime
SCORE TABLES
O(1)
Read
Read
60s
TTL
TTL
AI INTELLIGENCE PLANE
Asynchronous · Never Blocks Transactions
Model 1
GNN Fraud Detection
Graph Neural Networks analyze borrower relationship patterns
PyTorch
Model 2
RL Reputation Engine
Reinforcement Learning scores NBFC trustworthiness
ZeroMQ
Model 3
Behavioral Biometrics
Device fingerprint, typing pattern, touch pressure analysis
GPU Workers
Safety
Fail-Safe Design
If AI plane fails entirely, consensus continues on stake-weighted fallback
60s TTL
Technology Architecture
What powers NeuroChain under the hood
⚙️
C++17 Transaction Engine
Lock-free, zero heap allocation in hot path
Sub-millisecond per operation
Ed25519 cryptographic signatures via Rust FFI
BLS12-381 aggregate signatures
🧠
Python AI Pipeline
PyTorch GNN models for fraud graph analysis
ZeroMQ ring buffer for async message passing
GPU inference workers (A10G / A100)
Model promotion via governance-gated protocol
📡
Enterprise Integration
REST API — single endpoint, no blockchain knowledge needed
ISO 20022 compliant messaging format
AWS Mumbai Region — DPDPA 2023 data localization
Go-based API gateway with rate limiting
🔒
Security & Compliance
VAPT (Vulnerability Assessment) before every pilot
DPDPA 2023 compliant from Day 1
No real PII stored — behavioral + device data only
RBI Sandbox-first regulatory approach
How AI Powers Fraud Validation
The intelligent scoring system that protects every transaction
Three AI Models Working Together
01
Graph Neural Network (GNN)
Fraud Pattern Detection
Analyzes the web of relationships between borrowers, wallets, and NBFCs. Detects invisible patterns like: same person borrowing from 5 NBFCs using slightly different names, sudden spike in loan applications from one device, or coordinated fraud rings across institutions.
Accuracy: 94%+Async (pre-computed)
02
Reinforcement Learning (RL)
Reputation Engine
Continuously scores each NBFC node's trustworthiness based on: transaction volume and quality, fraud rate history, uptime reliability, and consensus participation. Higher reputation = more influence in validator selection.
Accuracy: Dynamic60s refresh
03
Behavioral Biometrics
Identity Verification
Analyzes HOW a person interacts with their device — typing speed, touch pressure, swipe patterns, screen tilt. Even if someone steals credentials, their behavioral pattern won't match. Catches identity spoofing and synthetic identities.
Accuracy: 88%+Real-time capture
Combined Risk Score Formula
Risk Score = (GNN Score × 0.50) + (Behavioral Score × 0.30) + (AML Bloom Filter × 0.20)
0-30
LOW RISK
Auto-approved. Normal transaction.
30-60
MEDIUM
Enhanced monitoring. Flagged for review.
60-80
HIGH RISK
Transaction held. Manual review required.
80-100
CRITICAL
Transaction blocked. AML alert triggered.
Complete Transaction Workflow
Step-by-step: from NBFC API call to fraud-scored settlement
1
NBFC Sends Transaction
An NBFC sends a single REST API call with transaction metadata: amount, wallet ID, device fingerprint, timestamp, and geolocation. No blockchain knowledge needed.
POST /v1/score · REST API0ms
2
Layer 3: Fast Rule Engine
Instant pre-checks: Ed25519 signature verification, Bloom filter AML watchlist scan, rate limiter check, and risk score lookup from pre-computed tables.
C++ · Bloom Filter · Ed25519<1ms
3
AI Score Table Lookup
The transaction engine reads the borrower's pre-computed risk score from the shared score table. This is an O(1) hashmap read — instant. The AI calculated this score in the background.
In-memory HashMap · O(1) Read<0.1ms
4
Layer 2: DAG Routing
Transaction is routed through the Object-Centric DAG. 85% of transactions take the OWNED-OBJECT fast path (no consensus needed). Only 15% need full BFT consensus.
DAG Engine · Parallel Execution<200ms (owned)
5
Layer 1: AABFT Consensus
For shared-object transactions: validators are selected using weighted reservoir sampling (based on AI Reputation Scores). They vote approve/reject. Byzantine fault tolerant — works even if <33% of nodes are malicious.
AABFT · BFT Voting · n/3 Tolerance<1.5s
6
Score Response Returned
The NBFC receives: risk_score (0-100), aml_flag (boolean), confidence (0.0-1.0), and latency_ms — all within 50ms for the fraud score. The NBFC decides to approve or reject the loan.
JSON Response · SLA: P99 < 50ms<50ms total
🧠 Meanwhile, in the Background (AI Plane)
While transactions are processed in milliseconds, the AI Intelligence Plane works asynchronously — never blocking any transaction:
Every 60 seconds: GNN model re-scores all active wallets based on new transaction patterns and publishes updated scores to the shared table.
Every consensus round: RL engine updates NBFC reputation scores based on their voting behavior, fraud rates, and uptime.
Continuously: Behavioral biometrics models train on new device interaction data, improving identity verification accuracy over time.
Benefits for Every Stakeholder
How NeuroChain creates value across the entire financial ecosystem
For Government & RBI
Real-time fraud visibility across 9,306 NBFCs — no more reporting delays
Automated CRILC compliance: fraud flagging happens at transaction time
AML/KYC infrastructure for the 9,000+ NBFCs that currently lack it
Development impact: credit access for 230M unbanked Indians
Digital India alignment: AI-native financial infrastructure
Tax revenue increase through reduced shadow economy transactions
For Banks & NBFCs
Fraud losses reduced by 50%+ through shared intelligence network
Credit bureau cost reduction: ₹5-15L/year flat vs ₹15-50 per query
Real-time borrower verification instead of monthly-updated data
Network effect: each new NBFC member makes the system smarter for all
No technology burden: single REST API call, no blockchain knowledge needed
ISO 20022 compliant — ready for international banking integration
For Consumers & Borrowers
Lower interest rates: reduced fraud = lower risk premium on loans
Faster loan approvals: AI scoring in <50ms vs days of manual review
Privacy-first: only behavioral + device data used, no PII stored
Protection from identity theft via behavioral biometrics
Access to credit for rural borrowers currently excluded from the system
Transparent scoring: borrowers can see their trust score improve over time
For the Private Sector & Investors
$72.5M total capital plan with clear milestone-based funding phases
Year 5 ARR target: ₹157 Crore (~$18.9M) — EBITDA positive
7 diversified revenue streams from SaaS to government infrastructure
Massive TAM: ₹16.58L Crore NBFC sector + emerging market expansion
Network effect moat: more members = better AI = more members
Global vision: India → ASEAN → Middle East → Africa expansion path
Government & Regulatory Compliance
Built to operate within India's regulatory framework from Day 1
🏦
RBI Compliance
Sandbox-first approach: no token/crypto until RBI approval
Phase 1 is pure enterprise SaaS — no payment instrument involved
CRILC fraud reporting automation
Not a payment aggregator — legal opinion pre-cleared
🔐
DPDPA 2023
Data localization: AWS Mumbai / Azure India Central
No PII storage — behavioral & device data only
Consent-based data processing
Right to erasure supported
📋
Financial Standards
ISO 20022 message format compliance
VAPT security audit before every NBFC pilot
Ed25519 cryptographic signature verification
Audit trail for all consensus decisions
Regulatory Strategy Timeline
Months 1-12: Enterprise SaaS mode. No regulatory approval required. Pure fraud scoring API.
Months 12-18: RBI Sandbox application filed with 12+ months of NBFC pilot data as evidence.
Months 18-30: Sandbox testing under RBI supervision. NeuroCoin (NRC) only launched post-approval.
Month 30+: Full regulatory clearance. Public network launch. Protocol emission schedule begins.
Why NeuroChain is an Industry Benchmark
How this technology redefines financial transaction infrastructure globally
| Capability | Traditional System | NeuroChain | Improvement |
|---|---|---|---|
| Fraud Detection | Rule-based, manual review | AI-powered GNN + behavioral | 94%+ vs ~60% detection |
| Scoring Latency | 24-48 hours | <50ms real-time | 1,000,000× faster |
| Data Freshness | Monthly updates | <60 second refresh | Near real-time |
| Inter-NBFC Intelligence | None (complete silos) | Shared consortium network | Network effect |
| AML Coverage | Large NBFCs only | All 9,306 NBFCs | 100% coverage |
| Per-Query Cost | ₹15-50 per check | Flat ₹5-15L/year unlimited | 80%+ cost savings |
| Consensus Finality | N/A (centralized) | <1.5s BFT consensus | Decentralized trust |
| Byzantine Tolerance | Single point of failure | Tolerates <33% malicious | Military-grade resilience |
The Vision
The AI Settlement Layer for Emerging Market Economies
India (650M digital users) → ASEAN ($1T digital payments) → Middle East (UAE/Saudi sandbox) → Africa ($1.68T mobile money) — serving 4 billion emerging market consumers with AI-native trust infrastructure.