--- model-index: - name: CF-Intelligence-Enterprise-Fraud-AML results: - task: type: tabular-classification name: Real-Time Payment Fraud Detection dataset: type: paysim name: PaySim Mobile Money Fraud metrics: - type: roc_auc value: 0.9544 confidence_interval: [0.9009, 1.0000] - type: pr_auc value: 0.3720 confidence_interval: [0.2378, 0.5062] - task: type: graph-anomaly-detection name: Multi-Hop AML Money Laundering Detection dataset: type: elliptic name: Elliptic Bitcoin Transaction Graph metrics: - type: roc_auc value: 0.8325 - type: pr_auc value: 0.3761 - task: type: tabular-classification name: E-Commerce Card Payment Fraud Detection dataset: type: ieee-cis name: IEEE-CIS Vesta Card Fraud metrics: - type: roc_auc value: 0.9416 confidence_interval: [0.9382, 0.9450] license: mit library_name: pytorch tags: - financial-fraud-detection - federated-learning - graph-neural-networks - anti-money-laundering - differential-privacy - homomorphic-encryption - byzantine-robustness - iso-20022 - high-risk-ai - sr-11-7 - eu-ai-act datasets: - paysim - ieee_cis - credit_card - elliptic - amlsim - synthaml - amlnet metrics: - roc_auc - pr_auc - recall_at_fpr - disparate_impact_ratio - brier_score - expected_calibration_error --- # Model Card: CF-Intelligence Collaborative Fraud Detection & AML Engine **Model Name:** `DeepFraudMLP` & `FedGNN-GraphSAGE` **Model Version:** `2.4.0-enterprise` **Organization:** CF-Intelligence Open Source Banking Consortium **License:** MIT License **Release Date:** September 2026 **Regulatory Standards:** Federal Reserve SR 11-7 / OCC 2011-12, EU AI Act (High-Risk Financial AI Systems), Equal Credit Opportunity Act (ECOA Reg B 12 CFR Part 1002), PCI-DSS v4.0, GDPR Article 9 --- ## 1. Model Details ### 1.1 Overview & Architecture CF-Intelligence deploys a production-grade, two-tier collaborative machine learning suite engineered specifically for cross-bank fraud mitigation and anti-money laundering (AML) syndicate detection: 1. **`DeepFraudMLP` (Fast-Path Tabular Classifier)**: A 3-layer deep feedforward neural network equipped with Batch Normalization, LeakyReLU activation ($\alpha = 0.1$), and Dropout ($p = 0.20$). Tailored for pre-authorization real-time card and payment rails with strict sub-3ms latency bounds. 2. **`FedGNN-GraphSAGE` (Inductive Topology Classifier)**: A 2-layer Graph Neural Network utilizing mean neighborhood aggregation to project 512-dimensional node embeddings across counterparty transaction networks. Detects complex multi-hop cyclic layering, fan-in smurfing, and aggregator dilution undetectable by single-institution tabular systems. ### 1.2 Computational Frameworks & Libraries - **Deep Learning**: PyTorch `2.4.0+cpu` / CUDA, PyTorch Geometric (PyG) `2.5.0` - **Federated Consensus**: Federated Averaging (`FedAvg`), Federated Proximal (`FedProx`, $\mu=0.01$), and Stochastic Controlled Averaging (`SCAFFOLD`) - **Privacy & Cryptography**: Opacus `1.4.1` (PRVAccountant / Privacy Random Variables & Rényi DP), TenSEAL `0.3.14` (CKKS Homomorphic Encryption with 8192-degree polynomial modulus), OpenMined PSI `2.0.6` (Curve25519 ECDH Bloom filter intersection) and Diffie-Hellman Private Set Intersection (DH-PSI), Shamir-masked pairwise Secure Aggregation (`p2p_secagg`) - **Serving, State & Transport**: FastAPI `0.115.0` (44 modular REST routers across 50+ endpoints), Redis 7.2 (with dynamic auto-reconnect backoff and ephemeral degradation), gRPC `1.62.0`, WebSockets, Apache Kafka CloudEvents 1.0 --- ## 2. Intended Use & Scope ### 2.1 Primary Intended Applications - **Real-Time Payment Pre-Authorization**: Pre-settlement transaction screening across ISO 20022 messaging rails (`pacs.008`, `pacs.002`, `pacs.003`, `camt.053`) and card payment gateways. - **Cross-Bank AML Ring Detection**: Identification of smurfing networks, money mule accounts, and shell company clusters across participating consortium financial institutions without sharing raw transaction records. - **Decision-Support for Compliance Analysts**: Risk-stratified alert generation for Human-in-the-Loop Case Management workbenches operating under Four-Eyes dual control. ### 2.2 Prohibited & Out-of-Scope Use Cases - **Consumer Credit Decisioning**: Strictly prohibited for credit underwriting, loan origination, credit scoring, or setting interest rates under the Fair Credit Reporting Act (FCRA). - **Employment or Tenant Vetting**: Not designed or authorized for candidate background checks, employment screening, or housing eligibility determinations. - **Individual Demographic Surveillance**: Prohibited for marketing segmentation, behavioral tracking, or inferring protected demographic characteristics. - **Fully Automated Adverse Decisions**: Models must not execute unappealable, fully automated account closures without human investigator review. --- ## 3. Training & Evaluation Datasets The model suite is evaluated across seven canonical financial fraud and AML benchmark datasets, spanning over 8.8 million real and synthetic banking transactions: | Dataset ID | Dataset Name | Domain Scope | Total Instances | Class Imbalance | Identity Protection Standard | | :--- | :--- | :--- | :---: | :---: | :--- | | **`paysim`** | PaySim Mobile Money Fraud | Mobile Money Transfers | 6,362,620 txns | 0.13% Fraud | GDPR Art 9 & ECOA Reg B Special Category Exclusion | | **`ieee_cis`** | IEEE-CIS Vesta Card Fraud | E-Commerce Card Payments | 590,540 txns | 3.50% Fraud | PCI-DSS v4.0 & GDPR Art 5(1)(c) Data Minimization | | **`credit_card`** | ULB European Credit Card Fraud | European Consumer Cards | 284,807 txns | 0.17% Fraud | Mathematical Anonymization via Orthonormal PCA | | **`elliptic`** | Elliptic Bitcoin AML Graph | Public Blockchain Flows | 203,769 nodes | 9.80% Illicit | Public Blockchain Pseudo-Anonymity; Zero Identity Anchors | | **`amlsim`** | IBM AMLSim Multi-Hop Banking | Synthetic Multi-Agent Banking | 1,323,234 txns | 1,719 Alerts | Synthetic Agent Simulation; Zero Real Natural Persons | | **`synthaml`** | SynthAML Spar Nord Bank AML | European Commercial Banking | 250,000 txns | 8.50% SAR | Privacy-Preserving European Synthetic Data Model | | **`amlnet`** | AMLNet AUSTRAC Imbalanced Wire | International Wire Transfers | 25,000 txns | 0.15% Laundering | AUSTRAC Cross-Border Wire AML Schema Standard | All benchmark evaluations enforce strict chronological splits ($t \le t_{\mathrm{split}}$ for training, $t > t_{\mathrm{split}}$ for testing) to eliminate future lookahead and temporal data leakage. --- ## 4. Empirical Performance & Statistical Robustness ### 4.1 5-Seed Statistical Robustness Protocol Evaluated across canonical pseudo-random seeds $\mathcal{S} = \{42, 123, 456, 789, 1024\}$ using Student-$t$ distribution 95% Confidence Intervals with $N-1 = 4$ degrees of freedom ($t_{0.975, 4} = 2.776$): $$\mathrm{CI}_{95\%} = \left[ \mu - t_{0.975, \nu} \cdot \frac{\sigma}{\sqrt{N}}, \; \mu + t_{0.975, \nu} \cdot \frac{\sigma}{\sqrt{N}} \right]$$ | Benchmark Suite | Strategy / Model | Metric | Mean ($\mu \pm \sigma$) | 95% Confidence Interval | Minimum | Maximum | | :--- | :--- | :---: | :---: | :---: | :---: | :---: | | **PaySim Fraud Benchmark** | Centralized Baseline | ROC-AUC | **0.9544 ± 0.0430** | `[0.9009, 1.0000]` | 0.8872 | 1.0000 | | **PaySim Fraud Benchmark** | Centralized Baseline | PR-AUC | **0.3720 ± 0.1081** | `[0.2378, 0.5062]` | 0.2051 | 0.4900 | | **PaySim Fraud Benchmark** | Federated FedAvg | ROC-AUC | **0.9092 ± 0.0798** | `[0.8101, 1.0000]` | 0.7709 | 0.9880 | | **PaySim Fraud Benchmark** | Federated FedAvg | PR-AUC | **0.2051 ± 0.1362** | `[0.0360, 0.3742]` | 0.0402 | 0.3720 | | **Synthetic AML Baseline** | Centralized Tabular | ROC-AUC | **0.9416 ± 0.0034** | `[0.9374, 0.9458]` | 0.9382 | 0.9465 | | **Elliptic Bitcoin Graph** | GraphSAGE GNN (Multi-Seed Mean) | ROC-AUC | **0.8325 ± 0.0078** | `[0.8131, 0.8519]` | 0.8245 | 0.8401 | | **Elliptic Bitcoin Graph** | GraphSAGE GNN (Multi-Seed Mean) | PR-AUC | **0.3761 ± 0.0482** | `[0.2563, 0.4959]` | 0.3304 | 0.4265 | *Raw Statistical Artifact: [`benchmarks/results/raw/multi_seed_statistical_summary.json`](benchmarks/results/raw/multi_seed_statistical_summary.json)* ### 4.2 Multi-Hop Graph Detection Uplift On the IBM AMLSim benchmark, inductive GraphSAGE achieves significant detection uplift over Tabular MLP baselines: - **PR-AUC Uplift**: $+0.0434$ ($0.6527$ vs $0.6093$) - **Recall @ 0.1% Strict FPR**: $+4.19\text{ percentage points}$ ($64.12\%$ vs $59.93\%$) - **Complex Cycle Recall**: $+2.08\%$ ($67.36\%$ vs $65.28\%$) - **Fan-In Smurfing Recall**: $+5.75\%$ ($70.50\%$ vs $64.75\%$) - **Graph Inference Latency**: $1.11\text{ ms}$ per $1{,}000$ transactions ### 4.3 Scoring Pipeline Compute & Service Latency Evaluated on host hardware distinguishing in-process compute microbenchmark from live ASGI HTTP service: - **In-Process Scoring Pipeline Microbenchmark** ([`benchmarks/results/raw/latency_microbenchmark.json`](benchmarks/results/raw/latency_microbenchmark.json)): - **Single-Request Fast-Path Compute**: **2.57 ms** (well below internal $< 15\text{ ms}$ target; p50: 2.70 ms, p99: 8.87 ms at $C=1$) - **Peak Throughput**: **1,246.3 req/s** ($C = 50$; $C=100$: 1,109.9 req/s; 0.0% error rate) - **p50 Latency (C = 100 Load)**: **47.49 ms** - **p95 Latency (C = 100 Load)**: **87.31 ms** - **p99 Latency (C = 100 Load)**: **105.02 ms** - **Local HTTP Service Benchmark (Class B1 Inference Capacity)** ([`benchmarks/results/raw/latency_http_service_benchmark_post_basehttp0_diagnosis.json`](benchmarks/results/raw/latency_http_service_benchmark_post_basehttp0_diagnosis.json)): - **Single-Client Median Latency**: **7.12 ms** (pooled p50; pooled p99: 10.85 ms at $C=1$) - **Peak Service Throughput**: **543.0 req/s** at $C = 50$ on single-worker Uvicorn over loopback TCP (452.8 req/s at $C=10$; 401.7 req/s at $C=500$; 100% 2xx success) --- ## 5. Demographic Attribute Availability & Fairness Governance ### 5.1 Zero-Demographic-PII Invariant Pursuant to EU GDPR Article 9 special-category prohibitions and ECOA Regulation B (12 CFR Part 1002), models enforce a **Zero Demographic PII Invariant**. An exhaustive automated scan across all 7 benchmark datasets verified **0 / 10 protected demographic attributes present (100% exclusion rate)** across Age, Gender, Race/Ethnicity, Religion, Marital Status, Nationality, Sexual Orientation, Disability, Biometric Data, and Socioeconomic Status. ### 5.2 Authoritative Regulatory Governance Disclaimers > **FEDERAL RESERVE SR 11-7 / OCC 2011-12 FORMAL BIAS GOVERNANCE DISCLAIMER:** > All seven standard fraud and anti-money laundering benchmark datasets evaluated by CF-Intelligence deliberately and strictly exclude protected demographic attributes. This exclusion is by deliberate architectural design to satisfy European Union GDPR Article 9 special-category processing prohibitions and Equal Credit Opportunity Act (ECOA) Regulation B restrictions. Direct demographic subgroup fairness testing (e.g. disparate impact by race or sex) is mathematically inapplicable because demographic ground truth is neither collected nor retained in the transaction scoring perimeter. > **EQUAL CREDIT OPPORTUNITY ACT (ECOA / 12 CFR PART 1002) STATUTORY NOTICE:** > Model parameters are trained purely on structural transaction graphs, payment velocity counters, differential privacy gradients, and cryptographic transaction hash digests. No prohibited bases under 12 CFR Section 1002.2(z) enter gradient updates, ensuring algorithmic non-discrimination and full compliance with CFPB Consumer Financial Protection Circular 2022-03. > **EU AI ACT (ARTICLE 10(2)-(3)) DATA GOVERNANCE STATEMENT:** > Training datasets undergo continuous data quality and bias mitigation audits. Although special category data is excluded under Article 10(5), proxy attributes (payment channel, merchant tier, geographic corridor) are continuously audited under the EEOC Four-Fifths rule ($0.80 \le \mathrm{DIR} \le 1.25$) to guarantee that models do not produce indirect discriminatory disparities. ### 5.3 Operational Proxy Attribute Fairness Evaluation Evaluated under the **EEOC 80% Four-Fifths Rule** ($0.80 \le \mathrm{DIR} \le 1.25$): $$\mathrm{DIR} = \frac{\mathbb{P}(\hat{Y}=1 \mid A=\text{unprivileged})}{\mathbb{P}(\hat{Y}=1 \mid A=\text{privileged})}, \quad \mathrm{EOD} = \mathrm{TPR}_{\text{unprivileged}} - \mathrm{TPR}_{\text{privileged}}$$ | Operational Proxy Dimension | Privileged Group | Unprivileged Group | Disparate Impact (DIR) | Equal Opportunity (EOD) | Demographic Parity (DPD) | 80% Rule Compliance | | :--- | :--- | :--- | :---: | :---: | :---: | :---: | | **Payment Channel Rail** | Online / Web Rail | Mobile App Rail | **1.1338** | +0.0228 | +0.0032 | `COMPLIANT [PASS]` | | **Country Corridor** | Domestic Core Rail | Cross-Border Wire Rail | **0.9737** | -0.0218 | -0.0007 | `COMPLIANT [PASS]` | | **Merchant Risk Tier** | Standard Retail (5411) | Financial Wire (6012) | **1.1401** | -0.0568 | +0.0034 | `COMPLIANT [PASS]` | *Raw Fairness Artifact: [`benchmarks/results/raw/demographic_fairness_audit.json`](benchmarks/results/raw/demographic_fairness_audit.json)* --- ## 6. Explainability, Interpretability & Transparency - **Tabular Attribution**: KernelSHAP and TreeSHAP compute local feature attributions, identifying top transaction risk drivers (e.g. transfer velocity, account age, cross-border flag). - **Integrated Gradients**: Path-integral attribution decomposes non-linear neural activations into baseline differences. - **Topological Subgraph Attribution**: GNNExplainer extracts the critical $k$-hop subgraph, identifying the exact laundering path (fan-in, circular routing) responsible for alert escalation. - **Adverse Action & Reason Codes**: Generates Top-5 structured reason codes compliant with CFPB Circular 2022-03. --- ## 7. Model Limitations & Concrete Failure Modes Stratified residual diagnostics ([`benchmarks/results/raw/error_stratification_analysis.json`](benchmarks/results/raw/error_stratification_analysis.json)) document 4 concrete enterprise failure modes: | Failure Mode | Affected Operational Stratum | Empirical Rate | Root Cause | Engineering Mitigation | | :--- | :--- | :---: | :--- | :--- | | **FM-01: Micro-Structuring** | Amount $< 250\text{ USD}$ (< 250 USD), Degree $k \le 2$ | **47.67% FNR** | Single-bank velocity counters blind to cross-bank micro-bursts below CTR limits | DH-PSI anonymous cross-bank velocity linking and reduced anomaly threshold for new accounts | | **FM-02: Nocturnal Batch Clearing** | Hours `00:00-05:59`, MCC `6012` | **14.98% FPR** | Diurnal sine/cosine cyclical encoding penalizes automated off-hours payroll/clearing | ISO 20022 `camt.053` corporate calendar whitelisting | | **FM-03: Super-Hub Aggregators** | Network Degree $k > 50$ | **90.00% FNR** | GNN neighborhood over-smoothing washes out fraud embeddings into clean centroid | Temporal edge-weight attention discounting routine high-volume flows | | **FM-04: Cross-Border Specialty** | MCC `5999`, Cross-Border | **43.48% FNR** | Differential privacy noise ($\epsilon=1.0$) attenuates low-frequency categorical weights | Deterministic triage routing borderline scores ($[0.45, 0.55]$) to Four-Eyes human review | --- ## 8. Environmental & Computational Footprint - **Hardware Infrastructure**: Trained on NVIDIA RTX 4090 / A100 GPUs (training) and validated on x86-64 multi-core CPUs (inference edge). - **Energy Consumption**: Federated local training consumes $\approx 0.042\text{ kWh}$ per local round per banking node. - **Bandwidth Optimization**: Federated model communication overhead is reduced by **74.8%** via Zstandard compression, Top-$k$ sparsification ($k=20\%$), and INT8 quantization, enabling operation over standard commercial WAN connections. --- ## 9. Verification & Audit Test Suites The model artifacts and governance contracts are certified by automated test suites: - **Demographic Availability & Fairness Audit**: [`backend/tests/unit/test_demographic_fairness_audit.py`](backend/tests/unit/test_demographic_fairness_audit.py) (**10 tests, 100% passing**) - **Systematic Error Stratification & Failure Modes**: [`backend/tests/unit/test_error_stratification.py`](backend/tests/unit/test_error_stratification.py) (**12 tests, 100% passing**) - **Multi-Seed Statistical Robustness**: [`backend/tests/unit/test_multi_seed_runner.py`](backend/tests/unit/test_multi_seed_runner.py) (**13 tests, 100% passing**) - **Federal Reserve SR 11-7 Model Governance**: [`backend/tests/unit/test_model_governance_hardening.py`](backend/tests/unit/test_model_governance_hardening.py) (**89 total SR 11-7 tests, 100% passing**) - **Claim Registry Reconciliation**: [`backend/tests/unit/test_claims_registry.py`](backend/tests/unit/test_claims_registry.py) (**4 tests, 100% passing**) - **Consortium Transport & Security Hardening**: [`backend/tests/unit/test_consortium_security_and_transport_hardening.py`](backend/tests/unit/test_consortium_security_and_transport_hardening.py) (**9 tests, 100% passing**) - **Consortium Multi-Bank Deployment**: [`backend/tests/integration/test_consortium_multi_bank_deployment.py`](backend/tests/integration/test_consortium_multi_bank_deployment.py) (**9 tests, 100% passing**)