The Problem
Current AML systems rely on outdated rule-based detection and fragmented databases. This creates two major issues: excessive false positives and, most importantly, no compliant way to share vital information between institutions, limiting cross-network insight.
Siloed Data
Institutions can’t detect cross-network patterns or coordinated frauds.
False Positives
Over 90% of alerts are false or redundant, draining compliance capacity.
Blocked Collaboration
Vital intelligence cannot be shared between banks due to privacy and compliance barriers.
False Positives vs. True Positives
Data for illustrative purposes only.
MFS Growth Trend
This rapid growth, while positive, increases AML/CFT risk exposure as digital channels can be exploited for illicit fund movement.
The Solution
A shared, AI-driven blockchain infrastructure that solves the core problem of information sharing. It enables all financial institutions and regulators to identify threats collectively and instantly in a fully compliant, anonymized manner.
AI Analytics
Machine learning models detect anomalies and reduce false positives.
Permissioned Blockchain
A secure distributed ledger for authorized nodes to share verified, non-PID data.
Zero-Knowledge Proofs
Cryptographic proofs that ensure data remains private and compliant during analysis.
Technology Stack
Built using scalable, modular frameworks integrating Hyperledger Fabric, TensorFlow Federated, and ZK-SNARK verification layers.
We propose Hyperledger Fabric because it is not a public blockchain. It is a private, permissioned network where only authorized participants (BFIU, BB, banks) can operate nodes. Its "channel" architecture allows for granular privacy, enabling FIs to share data only with the regulator and not with each other, ensuring competitive neutrality. Its high throughput is essential for a national-level system.
The AI layer moves beyond static rules (e.g., "flag all transactions > $10k") to dynamic, behavioral analysis. By using TensorFlow/PyTorch, we can deploy unsupervised learning to find new anomalies and graph analytics (network mapping) to uncover the complex, low-value collusion (like Hundi networks) that is currently invisible to single banks.
Banks cannot and will not share raw customer data or PII, as it violates regulations like the DPA. This layer makes compliant collaboration possible. We use Federated Learning, where the AI model is trained on private data *locally* at each bank. Only the model updates (the "learnings") are sent to the central network, never the underlying data itself. Zero-Knowledge Proofs (ZKPs) add another layer, allowing a bank to *prove* a transaction is compliant **without revealing any of its details.**
A monolithic system is not viable. A Kubernetes (K8s) containerized architecture is essential for a national-level platform. It allows for modularity (e.g., updating the AI engine without touching the DLT) and resilience. If one component fails, K8s automatically restarts it, ensuring the high-availability required for critical financial infrastructure.
How It Works
A step-by-step look at the data and intelligence flow, from transaction to enforcement.
1. Customer Interaction
A customer onboards or transacts at their FI (Bank, MFS). The FI's local system processes it. For KYC, a Verifiable Credential (dID) is created.
2. Data Anonymized & Recorded
The FI pseudonymizes the data (e.g., hashing transaction details) and records it on the permissioned blockchain. Crucially, no PII or raw customer data is shared between banks, ensuring regulatory compliance.
3. AI Engine Analyzes Patterns
The central AI engine analyzes the entire network's anonymized data. It spots complex, cross-institutional patterns (like Hundi networks) that are invisible to any single bank.
4. Shared Alert Generation
When a high-confidence threat is detected, the AI generates a single, high-quality alert. This alert is sent *simultaneously* to the BFIU and all FIs involved in the suspicious activity.
5. Collaborative Action
Regulators and FIs can now collaborate on the *same alert* in real-time, using the blockchain as the common source of truth, leading to faster investigation and enforcement.
Roadmap
A phased, three-step implementation to ensure stability and build consensus.
Phase 1 (Months 1-9)
Establish legal consortium. Launch regulatory sandbox with 3-5 volunteer FIs to test the shared KYC utility.
Phase 2 (Months 10-24)
Deploy the AI/ML layer for the pilot group. Monitor KPIs for false positive reduction and detect new typologies.
Phase 3 (Months 25-48)
Phased national onboarding of all FIs. Expand to new use cases like Trade-Based Money Laundering (TBML).
Governance & Risk
A robust framework built on public-private partnership and strict data controls.
Policy Alignment
Ensures full compliance with the Money Laundering Prevention Act, 2012, and the Anti-Terrorism Act, 2009. Designed to be compatible with the upcoming Data Protection Act (DPA).
Data Governance
Establishes a clear consortium-based model. Defines data ownership (customer data remains with the FI), access rights (BFIU gets analytical access, not raw PII), and liability.
PPP Model
A Public-Private Partnership where a consortium of FIs, MFS providers, and regulators (led by BB/BFIU) jointly fund and operate the platform as a shared national utility.
Risk Mitigation
Risks are managed through independent security audits of all smart contracts, phased sandbox testing to prevent system-wide failures, and tiered rollouts.
Global Context
Learning from successful national-level DLT initiatives.
UAE KYC Blockchain
This platform unified KYC data from multiple banks, dramatically cutting onboarding times.
Lesson: A strong regulatory push combined with a focus on a single, high-pain-point (KYC) creates a clear ROI that drives initial adoption.
Singapore Project Ubin
A multi-year exploration of DLT for cross-border payments.
Lesson: A phased, sandbox-first approach is critical. It allows regulators and FIs to build technical expertise, test legal implications, and build confidence before a national rollout.