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

1,222

Pulls

Nodes

Browse Signals

Fraud typologies and detection patterns across the federated network.

19 typologies

Signal	Slug	Upvotes	Pulls	Submissions
Cross-Border Wire Stacking Rapid sequential wire transfers across multiple jurisdictions to outpace investigative response	wire_stacking	3	19	31
Crypto Mixing / Tumbling Obfuscating crypto transaction trails through mixing services	crypto_mixing	44	30	62
Cuckoo Smurfing Exploiting legitimate remittance channels to co-mingle illicit funds	cuckoo_smurfing	17	20	61
Cycle Detection Circular transaction flows indicating coordinated laundering rings	cycle	37	32	62
Dormant Account Activation Long-inactive accounts suddenly receiving high-volume transactions	dormant_activation	39	49	66
Fan-in (Mule Collector) Many accounts funneling funds into a single mule account	fan_in_mule	29	29	57
Fan-out (Burst) Single account rapidly dispersing funds to many recipients	fan_out_burst	41	57	57
Funnel Account Single account aggregating deposits from dispersed geographic locations	funnel_account	32	26	65
Invoice Factoring Fraud Fraudulent or duplicate invoices used to obtain financing against fictitious receivables	invoice_factoring_fraud	2	15	22
Layering Chain Sequential hops through intermediary accounts to obscure fund origin	layering_chain	23	35	57
Mule Recruitment Network Social-engineered recruitment rings coordinating multiple money mules	mule_recruitment_network	1	13	21
Nesting / Payable-Through Respondent bank clients accessing correspondent banking indirectly	nesting	20	20	58
Peel Chain Large crypto amount peeled off in small increments across wallets	peel_chain	26	31	58
Rapid Movement Funds moved through multiple accounts within hours to avoid detection	rapid_movement	15	44	51
Round-Tripping Funds sent abroad and returned disguised as foreign investment	round_tripping	45	23	54
Shell Company Network Funds routed through entities with no real business operations	shell_company	32	95	58
Smurfing (Structuring) Coordinated small transactions structured below reporting thresholds	smurfing	33	31	48
Synthetic Identity Fraud Fabricated identities combining real and fake PII to open and operate accounts	synthetic_identity	1	13	19
Trade-Based Laundering Over/under-invoicing of goods and services to move value across borders	trade_based	30	21	48

Browse Models

Production-grade models registered across the federated graph.

11 models

fgi-autoencoder-anomaly

Production

Variational autoencoder for anomaly detection via reconstruction error on transaction embeddings

Autoencoder pytorch v3

46 pulls 115

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fgi-gat-multihead

Production

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

GAT pytorch v3

56 pulls 99

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fgi-gcn-spectral

Production

Spectral GCN with Chebyshev polynomial approximation for transaction graph convolution

GCN tensorflow v4

50 pulls 111

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fgi-graphsage-inductive

Production

GraphSAGE inductive model for unseen node classification in expanding transaction graphs

GraphSAGE pytorch v3

74 pulls 105

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fgi-hetero-gnn

Production

Heterogeneous graph neural network modeling distinct node/edge types across account, merchant, and device layers

hetero_gnn pytorch v1

23 pulls 25

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fgi-onnx-edge

Production

ONNX-exported lightweight GNN for edge/on-device inference at partner institutions

GNN onnx v1

22 pulls 25

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fgi-rf-ensemble

Production

Random forest ensemble on engineered graph/behavioral features — baseline benchmark model

ensemble sklearn v1

19 pulls 23

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fgi-risk-gnn

Production

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

GNN pytorch v3

111 pulls 126

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fgi-temporal-gat

Production

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

Temporal GNN pytorch v5

79 pulls 102

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fgi-txn-transformer

Production

Transaction-level Transformer encoder for sequential fraud pattern detection

Transformer pytorch v5

76 pulls 114

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fgi-xgb-features

Production

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

XGBoost xgboost v5

63 pulls 110

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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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