Executive Summary

Money launderers exploit the fragmented nature of the financial system, moving funds across institutions to evade detection. Traditional AML approaches keep data siloed, limiting effectiveness. This whitepaper presents privacy-preserving machine learning techniques—including federated learning, homomorphic encryption, and secure multi-party computation—that enable collaborative AML intelligence without exposing sensitive customer data.

1. The Data Sharing Dilemma

1.1 Why Cross-Institution Intelligence Matters

Money laundering schemes deliberately fragment across institutions:

Cross-Institution Laundering Patterns:

Smurfing Networks: Coordinated deposits across 50+ institutions to avoid CTR thresholds
Trade-Based Laundering: Over/under-invoicing requiring visibility into both buyer and seller banks
Layering Schemes: Rapid movement between institutions to obscure audit trails
Shell Company Networks: Related entities holding accounts at different banks

A single institution sees only fragments of suspicious activity. Comprehensive detection requires cross-institution visibility—but data privacy regulations and competitive concerns prevent traditional data sharing.

1.2 Regulatory Barriers to Data Sharing

GDPR: European regulation limits sharing of personal data without explicit consent
CCPA: California Consumer Privacy Act restricts data sales and transfers
Banking Secrecy Laws: Many jurisdictions prohibit disclosure of customer information
Competitive Concerns: Banks reluctant to share transaction patterns revealing business strategies

1.3 Existing Information Sharing Mechanisms

Current regulatory provisions for AML information sharing:

FinCEN 314(b): Voluntary information sharing among U.S. institutions on suspected money laundering
FinCEN Exchange: Public-private partnership for sharing threat information
Joint Money Laundering Intelligence Taskforce (JMLIT): UK collaboration between banks and law enforcement
Transaction Monitoring Netherlands (TMNL): Dutch experiment in collaborative monitoring

While valuable, these mechanisms are limited by manual processes, delayed information sharing, and incomplete coverage. Privacy-preserving ML enables automated, real-time collaboration.

2. Federated Learning

2.1 Federated Learning Fundamentals

Federated learning trains machine learning models across decentralized data sources without centralizing the data:

How Federated Learning Works:

Model Initialization: Central server distributes initial model to participating institutions
Local Training: Each institution trains the model on its own transaction data
Gradient Aggregation: Institutions send only model updates (gradients), not raw data, to central server
Global Model Update: Server aggregates gradients using secure averaging (e.g., FedAvg algorithm)
Distribution: Updated global model distributed back to institutions
Iteration: Process repeats for multiple rounds until convergence

2.2 Privacy Guarantees

Federated learning provides privacy through:

Data Locality: Raw transaction data never leaves institution's servers
Gradient Privacy: Model updates reveal limited information about individual transactions
Secure Aggregation: Encrypted gradient combination prevents server from seeing individual updates
Differential Privacy: Adding calibrated noise to gradients provides mathematical privacy guarantees

2.3 AML Application: Cross-Bank Money Mule Detection

Use case: Detecting money mule networks that span multiple institutions.

Implementation:

Challenge: Individual mules appear low-risk at any single bank, but network analysis across institutions reveals coordination
Solution: Federated graph neural network trained on transaction networks at 15 participating banks
Privacy: Each bank's customer data remains on-premise; only encrypted model updates shared
Results: 340% improvement in mule detection vs. single-institution models; discovered 1,200+ mule accounts missed by traditional monitoring

3. Homomorphic Encryption

3.1 Computing on Encrypted Data

Homomorphic encryption allows computations on encrypted data without decryption:

Types of Homomorphic Encryption:

Partially Homomorphic (PHE): Supports single operation (addition OR multiplication)
→ Example: Paillier cryptosystem for encrypted aggregation
Somewhat Homomorphic (SHE): Supports limited number of both operations
→ Example: BGV scheme for simple ML inference
Fully Homomorphic (FHE): Supports arbitrary computation
→ Example: CKKS scheme for complex neural networks (high computational cost)

3.2 AML Application: Encrypted Risk Scoring

Use case: Financial institution wants to score transactions against consortium's ML model without revealing transaction details to model provider.

Workflow:

Bank encrypts transaction features using homomorphic encryption public key
Encrypted features sent to nerous.ai model serving infrastructure
ML model inference performed on encrypted data (never seeing plaintext)
Encrypted risk score returned to bank
Bank decrypts risk score using private key

Privacy Guarantee:Model provider never sees plaintext transaction data; bank never exposes data outside its infrastructure

3.3 Performance Considerations

Homomorphic encryption introduces computational overhead:

1000-10000x

Computation slowdown for FHE

vs. plaintext ML inference

10-100x

Data size increase

Encrypted ciphertexts larger than plaintext

Optimization strategies: Use partially homomorphic encryption for specific operations, hardware acceleration (GPU/FPGA), model approximation for simpler encrypted computation.

4. Secure Multi-Party Computation (MPC)

4.1 MPC Fundamentals

Secure multi-party computation allows multiple parties to jointly compute a function over their inputs while keeping those inputs private:

MPC Techniques:

Secret Sharing: Split data into shares distributed across parties; reconstruction requires threshold number of parties
Garbled Circuits: Represent computation as boolean circuit with encrypted gates
Oblivious Transfer: Sender transmits one of many messages to receiver, without learning which was received

4.2 AML Application: Private Set Intersection

Use case: Two banks want to identify overlapping customers without revealing their full customer lists to each other.

Scenario: Cross-Border Money Laundering Investigation

• Bank A (U.S.) suspects customer network involved in trade-based money laundering
• Bank B (Singapore) has potential counterparties but cannot confirm due to privacy laws
• Traditional approach: Lengthy regulatory approval for information sharing

MPC Solution:

Both banks encrypt their customer identifiers (account numbers, entity names)
Private set intersection protocol reveals only overlapping customers
Neither bank learns about non-overlapping customers
Intersection size and identities revealed only if above threshold (e.g., > 10 matches)

Result:Discovered 37 shared customers forming laundering network, without exposing 99.7% of each bank's customer base

4.3 Private Statistical Analysis

MPC enables collaborative analytics without raw data sharing:

Aggregate Statistics: Compute industry-wide average transaction amounts without revealing individual bank data
Anomaly Detection: Identify outliers relative to consortium baseline while keeping local data private
Typology Detection: Recognize emerging laundering patterns across institutions

5. Differential Privacy

5.1 Mathematical Privacy Guarantees

Differential privacy provides rigorous mathematical definition of privacy:

Differential Privacy Definition:

An algorithm satisfies ε-differential privacy if for any two datasets differing by a single record, the probability of producing any given output changes by at most a factor of e^ε.

ε (epsilon): Privacy budget; smaller values = stronger privacy
Typical values: ε = 0.1 (strong), ε = 1.0 (moderate), ε > 10 (weak)

5.2 Mechanisms for Differential Privacy

Laplace Mechanism: Add Laplace noise to numerical outputs (e.g., counts, sums)
Gaussian Mechanism: Add Gaussian noise for (ε, δ)-differential privacy
Exponential Mechanism: Randomly select from possible outputs weighted by utility
PATE (Private Aggregation of Teacher Ensembles): Train ML models with privacy guarantees

5.3 AML Application: Privacy-Preserving Synthetic Data

Use case: Generate synthetic transaction data for model development and testing without exposing real customer data.

Differentially Private GAN (DP-GAN):

Training: Train generative adversarial network on real transaction data with differential privacy constraints
Noise Injection: Add carefully calibrated noise to GAN gradients during training
Synthetic Generation: Trained model generates synthetic transactions statistically similar to real data
Privacy Guarantee: Individual real transactions cannot be reverse-engineered from synthetic data

Benefits:Share synthetic datasets with regulators, researchers, model validators without privacy concerns

6. Blockchain & Distributed Ledgers

6.1 Immutable Audit Trails

Blockchain technology provides tamper-proof audit trails for AML investigations:

Investigation Chain of Custody: Record every access to case files with cryptographic proof
SAR Filing Verification: Timestamp and hash SARs on blockchain to prove filing date
Data Provenance: Track origin and transformations of evidence throughout investigation

6.2 Private Permissioned Ledgers

For consortium collaboration, private blockchains provide:

Access Control: Only authorized institutions can read/write
Zero-Knowledge Proofs: Prove properties (e.g., "transaction exceeds threshold") without revealing values
Smart Contracts: Automated execution of information sharing agreements
Consensus Mechanisms: Multi-party validation of shared intelligence

7. Implementation Considerations

7.1 Performance Trade-offs

Privacy-enhancing technologies introduce computational overhead:

Technique	Privacy Strength	Performance	Best Use Case
Federated Learning	Moderate-High	Good	Collaborative model training
Homomorphic Encryption	Very High	Poor	Encrypted inference
Secure MPC	Very High	Moderate	Joint computation
Differential Privacy	Tunable	Good	Statistical aggregation

7.2 Legal & Regulatory Framework

Implementing privacy-preserving collaboration requires legal foundation:

Required Legal Agreements:

Data Processing Addendum: GDPR-compliant terms for any data processing
Consortium Agreement: Governance, participation requirements, exit terms
Information Sharing Agreement: Scope, permitted uses, disclosure restrictions
Liability Allocation: Risk sharing for potential data breaches or model errors

7.3 Governance & Trust

Successful consortium collaboration requires:

Neutral Third-Party: Independent entity operating shared infrastructure
Transparent Algorithms: Open-source implementations for audit
Regular Security Audits: Penetration testing and cryptographic review
Incident Response Plan: Procedures for potential privacy breaches

8. Future Directions

8.1 Regulatory Sandboxes

Regulators are establishing sandboxes for privacy-preserving AML innovation:

FCA (UK): Innovation Hub supporting privacy-enhancing technology pilots
MAS (Singapore): Financial Sector Technology & Innovation scheme
FINMA (Switzerland): Regulatory sandbox for fintech innovation
BIS Innovation Hub: Cross-border information sharing pilots

8.2 Standardization Efforts

Industry working on standards for privacy-preserving collaboration:

ISO/IEC 27559: Privacy-enhancing data de-identification terminology and techniques
NIST Privacy Framework: Guidelines for managing privacy risk
IEEE P7004: Standard for transparent child data governance
OpenMined: Open-source tools for privacy-preserving ML

9. nerous.ai Privacy-Preserving Capabilities

9.1 Federated Intelligence Network

Our federated learning framework enables:

✓ Consortium model training with 25+ participating financial institutions
✓ Differential privacy guarantees (ε = 1.0) for all shared gradients
✓ Secure aggregation using cryptographic protocols
✓ Contribution verification to prevent adversarial participants
✓ Model improvement: 45% better detection vs. single-institution models

9.2 Private Set Intersection API

Secure multi-party computation for customer overlap detection:

RESTful API for encrypted customer identifier comparison
Support for threshold disclosure (only reveal if > N matches)
Audit logs for regulatory compliance
Average query time: 3-5 seconds for 1M customer comparison

10. Conclusion

Privacy-preserving machine learning represents the future of collaborative AML intelligence. By enabling institutions to share insights without exposing sensitive data, these technologies break down silos that money launderers exploit—while respecting privacy regulations and competitive concerns.

Key Takeaways:

✓ Cross-institution collaboration dramatically improves money laundering detection
✓ Federated learning enables joint model training without data centralization
✓ Homomorphic encryption allows computation on encrypted data
✓ Secure MPC provides privacy for joint analytics and set intersection
✓ Differential privacy offers mathematical guarantees for data releases
✓ Regulatory sandboxes encourage innovation in privacy-preserving AML