India is increasingly vulnerable to money laundering, fraud, and illicit financial flows, driven by the rapid rise in digital payments, the growing popularity of cryptocurrencies, and the broad adoption of fintech services. Traditional AML tools, such as manual KYC, rule-based monitoring, and paper trails, cannot keep pace with the velocity, complexity, and scale of modern transactions. By combining artificial intelligence (AI) with blockchain, regulators and firms can build RegTech solutions that reduce compliance costs, enhance transparency, and accelerate the detection of wrongdoing. This article examines how AI and blockchain can strengthen India’s AML compliance, explores the legal and regulatory context, reviews case studies, and offers a roadmap for implementation.
Current Indian Legal and Regulatory Context
India regulates AML under the Prevention of Money Laundering Act, 2002 (PMLA), which requires banks, financial institutions, intermediaries, and other “reporting entities” to verify customer identity, maintain transaction records, and report suspicious transactions to the Financial Intelligence Unit‐India (FIU-IND).
In March 2023, the Government notified that Virtual Digital Asset (VDA) service providers, including crypto exchanges, wallet providers, and intermediaries dealing in virtual digital assets, would become “reporting entities” under PMLA. They must register with FIU-IND, perform customer KYC, maintain record‐keeping, have compliance officers, and file suspicious transaction reports. Shortly thereafter, FIU-IND issued AML & CFT Guidelines specifically for VDA reporting entities, prescribing KYC standards, record-retention rules, and the use of transaction-monitoring tools, including blockchain analytics and sanctions screening.
Authorities have already enforced these rules. For instance, Binance was fined about ₹18.82 crore (~US$2.2 million) for failing to register as a reporting entity and for failing to maintain records or report suspicious transactions. CoinDCX also registered as a reporting entity ahead of regulatory requirements.
Thus, Indian regulators now treat specific crypto and VDA activities as part of the regulated financial system. This legal shift sets the stage for AI-blockchain RegTech to support compliance and enforcement.
AI, Blockchain, and Synergy
Role of AI
AI can analyse large volumes of data in real time to detect anomalies, cluster suspicious transaction patterns, and assign dynamic risk scores to customers. Machine learning (ML) models can adapt to new laundering techniques. Natural Language Processing (NLP) tools can scan news articles, adverse media, sanctions lists, and social media to flag negative information about clients. These tools reduce false positives, allowing compliance teams to focus on substantive threats.
Role of Blockchain
Blockchain provides an immutable, time‐stamped ledger of transactions. When institutions utilise permissioned blockchains, they can share data (for example, KYC records or transaction trails) in a manner that remains tamper-proof and audit-friendly. Smart contracts can enforce compliance rules (for example, triggering alerts or automatically reporting when thresholds are exceeded), reduce manual interventions, and ensure that records cannot be altered after the fact.
Synergy: Why Combined Use Delivers More
Using AI alone without ensuring data integrity leaves systems vulnerable to manipulation, deletion, or fraud. Blockchain alone may provide strong audit trails, but without intelligent filtering and detection, regulators and compliance units will be overwhelmed. When AI models draw on data stored securely and immutably on a blockchain, institutions can detect suspicious activity more accurately, trace funds more efficiently, and ensure that enforcement has reliable evidence to support their actions.
Implementation Opportunities in India
India has many pain points that AI-blockchain RegTech can address.
1. KYC/E-KYC Utilities
India already uses digital identity platforms (like Aadhaar) and digital KYC for many financial services. It can build a shared, permissioned blockchain ledger for verified identity attributes. Reporting entities (banks, crypto exchanges, fintech firms) can access it with customer consent. AI risk engines can overlay identity verification with risk scoring (e.g., whether someone is a politically exposed person, has adverse media mentions, or is from a high-risk geographic location).
2. Crypto/VDA Monitoring
Since being notified under the PMLA in 2023, VDA service providers are required to monitor and report suspicious transactions involving virtual assets. AI tools can analyse transaction graphs, detect mixing or tumblers, and link pseudonymous addresses using heuristics. Blockchain analytics tools (used elsewhere globally) help trace the source and destination of virtual assets. Immutable chain records help FIU-IND or other enforcement bodies rely on reliable trail data.
3. Real-Time Payment System Oversight (UPI, etc.)
UPI and other instant payment systems facilitate a large number of small transactions. Launderers may attempt to layer or smurf (split large transactions) to conceal their activities. AI tools can flag suspicious patterns such as rapid transfers through shell accounts or bursts of transactions over a short interval. Blockchain or ledger technologies (even if internal) can store logs in a tamper-resistant form, thereby providing a strong record trail for investigations.
4. Trade-Based Money Laundering Detection
As trade flows cross borders, over- or under-invoicing and fictitious trade invoices facilitate money laundering. Blockchain for trade finance (for example, bills of lading, letters of credit) can ensure document authenticity and maintain shared provenance. AI can compare declared values, shipping patterns, and counterparties against global data to detect anomalies (e.g., unusually low freight charges, repeated use of third-party brokers).
5. Synthetic Data for Training and Testing AI Models
Institutions can utilise generative AI to generate synthetic blockchain transaction datasets that mimic real-world activity, allowing for safe model training without exposing private data. Researchers in India have begun exploring how generative models can help simulate financial crisis scenarios or transaction behaviour for AML compliance stress testing.
Benefits
The integration of AI and blockchain in AML compliance delivers substantial benefits for India’s financial ecosystem. AI-driven analytics enhance the accuracy and speed of suspicious transaction detection, reducing false positives and freeing compliance teams to focus on high-risk cases.
Blockchain’s immutable, transparent ledger enables real-time, auditable transaction trails, significantly improving interagency coordination among the Financial Intelligence Unit–India (FIU-IND), the Reserve Bank of India (RBI), and law enforcement agencies.
Together, these technologies reduce compliance costs for banks and fintech companies by automating Know Your Customer (KYC) and transaction monitoring while maintaining a higher standard of due diligence. For example, Dubai’s Virtual Assets Regulatory Authority (VARA), through its updated Rulebook 2.0, mandates blockchain-based record-keeping for virtual asset service providers. India can adapt a model like this to strengthen its AML regime.
Challenges and Risks
Despite its promise, the integration of AI and blockchain in AML compliance also presents significant challenges and risks for India. AI systems require large, high-quality datasets to function effectively; however, Indian financial institutions often maintain fragmented or inconsistent customer data, increasing the risk of biased or inaccurate outputs. Overreliance on algorithmic decision-making without human oversight may lead to the wrongful flagging of legitimate customers, raising concerns under the Supreme Court’s privacy jurisprudence in K.S. Puttaswamy v. Union of India.
Blockchain, while transparent and tamper-resistant, also poses regulatory and technical hurdles. Public ledgers may expose sensitive transaction details if not properly encrypted, and permissioned chains may still be vulnerable to insider collusion. Compliance teams must also grapple with the absence of explicit statutory recognition for blockchain records in India’s Evidence Act, which complicates their admissibility in court.
Finally, integrating AI-blockchain systems demands substantial upfront investment, skilled personnel, and cross-sector coordination, which smaller banks and fintech firms may find prohibitive.
Proposed Roadmap for Implementation in India
To move from potential to practice, India should follow a structured roadmap.
- Establish a National AML and VDA Compliance Blockchain Utility: The government, through FIU-IND or the RBI, could develop a permissioned blockchain to store verified KYC data, transaction alert logs, and compliance event records. Reporting entities could access it under strict governance.
- Mandate AI-based Transaction Monitoring and Risk Engines: The regulators should issue guidelines or mandates for large reporting entities to deploy AI/ML risk engines. Model governance (documentation, approval, periodic validation) should become a regulatory expectation.
- Expand Regulatory Sandboxes: India’s regulatory authorities (RBI, SEBI, FIU-IND) should incorporate AI and blockchain AML pilots within sandbox frameworks, allowing tools to be tested in controlled, supervised environments before scaling.
- Public-Private Partnerships and Capacity Building: The government can collaborate with fintech companies, AI startups, and universities to co-develop tools. They should invest in training and workshops for compliance officers and in creating open-standard datasets (anonymised/synthetic) to support tool development.
- Legal and Regulatory Reforms: Laws must be adapted to explicitly permit secure data sharing among regulated entities, protect privacy, define liability for model failures, and clarify what constitutes admissible evidence (e.g., blockchain records, AI-generated flags) in court proceedings.
- Ensure Accountability and Transparency: Reporting entities must maintain audit logs, explainable AI models, and governance structures with internal controls. Regulators must periodically inspect and audit these systems to ensure their effectiveness and reliability.
Conclusion
India now stands at a crucial juncture. As finance digitalises, virtual assets proliferate, and money laundering threats evolve, relying solely on legacy AML tools will leave significant gaps. AI and blockchain together provide the tools needed: intelligent, automated detection built on top of immutable, auditable data. India has taken steps to bring VDA providers under the PMLA and penalise non-compliance. Still, the full implementation of AI-blockchain synergy will require regulatory will, legal clarity, capacity building, and collaboration.
India can draw lessons from regulators like VARA in Dubai, but it must design its own path-one that protects individual rights, maintains data privacy, ensures explainability, and scales sustainably. If it succeeds, India can not only enforce AML more effectively but also build a globally competitive RegTech ecosystem that inspires trust, innovation, and financial security.