Open Source · Apache 2.0 · Federated Graph Protocol

Federated Risk Typology & Signal Registry

The open-source registry for federated financial fraud and AML typologies and detection signals. Version, distribute, and validate production-grade models across distributed financial institutions and digital asset networks.

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Typologies

Models

1,386

Pulls

Nodes

Browse Signals

Fraud typologies and detection patterns across the federated network.

16 typologies

Signal	Slug	Upvotes	Pulls	Submissions
Crypto Mixing / Tumbling Obfuscating crypto transaction trails through mixing services	crypto_mixing	38	33	56
Cuckoo Smurfing Exploiting legitimate remittance channels to co-mingle illicit funds	cuckoo_smurfing	15	25	52
Cycle Detection Circular transaction flows indicating coordinated laundering rings	cycle	32	36	55
Dormant Account Activation Long-inactive accounts suddenly receiving high-volume transactions	dormant_activation	36	66	58
Fan-in (Mule Collector) Many accounts funneling funds into a single mule account	fan_in_mule	22	36	48
Fan-out (Burst) Single account rapidly dispersing funds to many recipients	fan_out_burst	38	78	40
Funnel Account Single account aggregating deposits from dispersed geographic locations	funnel_account	27	32	50
Layering Chain Sequential hops through intermediary accounts to obscure fund origin	layering_chain	20	46	49
Nesting / Payable-Through Respondent bank clients accessing correspondent banking indirectly	nesting	16	25	48
Peel Chain Large crypto amount peeled off in small increments across wallets	peel_chain	22	42	47
Race conditions Golang is a l	golang	3	7	5
Rapid Movement Funds moved through multiple accounts within hours to avoid detection	rapid_movement	12	60	41
Round-Tripping Funds sent abroad and returned disguised as foreign investment	round_tripping	44	31	46
Shell Company Network Funds routed through entities with no real business operations	shell_company	31	140	51
Smurfing (Structuring) Coordinated small transactions structured below reporting thresholds	smurfing	28	43	41
Trade-Based Laundering Over/under-invoicing of goods and services to move value across borders	trade_based	26	22	41

Browse Models

Production-grade models registered across the federated graph.

8 models

fgi-autoencoder-anomaly

Production

Variational autoencoder for anomaly detection via reconstruction error on transaction embeddings

Autoencoder pytorch v3

55 pulls 98

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fgi-gat-multihead

Production

Multi-head attention GAT with 8 attention heads for heterogeneous transaction graphs

GAT pytorch v3

64 pulls 81

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fgi-gcn-spectral

Production

Spectral GCN with Chebyshev polynomial approximation for transaction graph convolution

GCN tensorflow v4

58 pulls 92

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fgi-graphsage-inductive

Production

GraphSAGE inductive model for unseen node classification in expanding transaction graphs

GraphSAGE pytorch v3

84 pulls 82

View

fgi-risk-gnn

Production

Federated Graph Intelligence risk scoring GNN — 128-dim node embeddings with 2-layer message passing

GNN pytorch v3

138 pulls 99

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fgi-temporal-gat

Production

Temporal Graph Attention Network — time-aware attention over transaction sequences

Temporal GNN pytorch v5

97 pulls 86

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fgi-txn-transformer

Production

Transaction-level Transformer encoder for sequential fraud pattern detection

Transformer pytorch v5

94 pulls 103

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fgi-xgb-features

Production

XGBoost classifier on graph-derived feature vectors — degree centrality, PageRank, clustering coefficient

XGBoost xgboost v5

74 pulls 87

View

Browse Nodes

Regulatory jurisdictions participating in the federated graph network.

7 regions

US / FinCEN

us_fincen

United States — Financial Crimes Enforcement Network jurisdiction

EU / AMLD

eu_amld

European Union — Anti-Money Laundering Directive jurisdiction

UK / FCA

uk_fca

United Kingdom — Financial Conduct Authority jurisdiction

APAC / FATF

apac_fatf

Asia-Pacific — FATF-aligned regulatory framework

LATAM

latam

Latin America — Regional AML/CFT framework

MENA

mena

Middle East & North Africa — Regional regulatory framework

Africa

africa

Sub-Saharan Africa — Regional AML/CFT framework

Infrastructure for Federated Payments Fraud Intelligence

Everything you need to version, validate, and distribute typologies across the federated graph.

Federated Aggregation

Coordinate typology updates across distributed graph nodes with cryptographically versioned weights.

Immutable Signal Registry

SHA-verified, append-only storage for typology artifacts with granular RBAC and audit trails.

Edge-Ready Distribution

Hot-swap production typologies at the edge. Sub-second propagation across all participating nodes.

Continuous Validation

Automated drift detection and quality gates on every submission before promotion to production.

Quickstart

Get up and running with the official federated-graph Python library.

Install the library

pip install federated-graph

Optional extras: [grpc] [redis] [scoring] [all]

Configure and initialize a model

import torch
from torch_geometric.data import Data
from federated_graph import FGIConfig
from federated_graph.models import FGIModel

# Create configuration
config = FGIConfig()

# Initialize model
model = FGIModel(in_channels=10, config=config.model)

Run a forward pass on graph data

# Prepare graph data
x = torch.randn(100, 10)
edge_index = torch.randint(0, 100, (2, 500))

# Forward pass returns embeddings and risk scores
embeddings, entity_scores, txn_scores = model(x, edge_index)

Start federated training

from federated_graph.federation import FederationServer, FederationClient

server = FederationServer(
    num_rounds=config.federation.num_rounds,
    min_clients=config.federation.min_clients,
)

client = FederationClient(
    client_id="client_1",
    model=model,
    config=config,
)

Full documentation at pypi.org/project/federated-graph

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