A criminal undertaking cannot operate in broad daylight without people questioning its source of income. The alternative is to open a slew of fake bank accounts under phony profiles and link them to a dummy front business.
Dummy companies become a conduit to channel illegal profits that morph into legitimate income within the banking system. Money laundering is the process of legitimizing assets from criminal activities by obscuring the link between ill-gotten profits and their illicit sources.
Following the terrible human tragedy of September 11, 2001, two policy changes came in rapid succession. There was increasing pressure on Banks and financial institutions to clamp down on money laundering, terror financing, and the growing narcotics trade.
The most significant policy change was the near-unanimous global agreement to make Know Your Customer guidelines compulsory for identifying, blocking, and actioning fake accounts and suspicious transactions.
The International Monetary Fund (IMF) views money laundering as a growing threat to national financial networks that could derail economies worldwide. There was concern that money laundering grew in economies with weak banking systems functioning under opaque rules that encourage anonymity and concealment.
The collateral damage inflicted by money laundering could be seen in worsening law and order, weakening governance, and slower foreign investment inflows. Global money-laundering crime costs the world nearly $2 trillion annually, a figure exceeding the GDP of Canada, the world’s 9th largest economy.
KYC experts working with Passbase ID verification system insist that the way forward is to adopt the right technology to combat the threat of money laundering and terrorist financing.
The need of the hour is a robust platform with Anti Money Laundering (AML) software that can manage an extensive database and monitor suspicious activities through comprehensive data analytics.
How Technology Tackles The Scourge Of Money Laundering And Terrorist Financing
Valuable human resources need to focus on following complex money laundering channels with cross-border ramifications. Processing massive data is beyond human capability, so we turn to technology for AML solutions that are faster, cheaper, and more efficient.
Machine Learning (ML)
By its very definition, ML algorithms are constantly learning from existing data and analyzing evolving situations to formulate the most appropriate responses in real-time. ML automates risk management protocols and minimizes manual human intervention.
Machine Learning Is Valuable In Dovetailing Effective Anti Money Laundering Applications
ML identifies customers using biometrics and life-detection techniques to foil internet chatbot intervention, spoofing attacks, and fake image perpetuation.
ML deploys transactional analysis to identify and categorize customers with similar behavior patterns showing red alert scorings.
ML algorithms continuously scan vast databases to shortlist accounts that flout regulatory concerns needing urgent intervention.
Automated data reporting (ADR) churns out analytical reports that supervisors need for AML compliance and auditing purposes.
Blockchain Distributed Ledger Technology (DLT)
The Blockchain has an immutable ledger that records personal and transactional data, which is tamper-proof. DLT effectively weeds out fraudulent transactions because of a system of encryption, time stamping, and network consensus validation that each translation must go through.
In Blockchain smart contracts, customers can safely use Blockchain Distributed Ledger to store, access, and verify sensitive personal profiles and IDs. Banks, given selective access, can quickly match-authenticate-verify IDs of customers to approve or deny facilities.
Natural Language Processing (NLP) And Textual Analytics
Banking officials pour through vast volumes of incoming data to curate what’s important and discard what’s irrelevant. NLP, a branch of Artificial Intelligence (AI), does the same job using algorithms to decipher the human spoken language and written documents.
The NLP algorithm analyzes data in seconds and helps pursue Customer Identification and Due Diligence (CDD) to ensure AML compliance. It’s possible to convert text to speech and speech to text and distribute the data in the language of choice.
The NLP protocols help authenticate and verify customer identity and red flag AML non-compliant accounts.
Application Programming Interfaces (APIs)
The traditional in-person checking of individual user accounts isn’t practical, considering the thousands of customers going through onboarding mechanisms.
An API is an intermediate software that allows different applications and components of a network to communicate between themselves. APIs are bringing new AML solutions empowering the financial sector.
An API brings agility and scalability to networking systems, allowing more efficient capture and analysis of big data. APIs are at the forefront of KYC monitoring and AML compliance and one of the most cost-effective ways of combating money laundering and checking terrorism finance.
Benefits Of Engaging Biometrics In Anti-Money Laundering And Financial Crime Control
Only users passing legitimate authentication checks gain access to their accounts. Biometric face recognition has the sophistication to block deepfake images, masks, and photos that attempt to replicate a customer’s facial features.
Detecting Fraud At The Onboarding Stage
The biometric software allows the opening of accounts only after matching and cross-verifying facial biometrics with the documentary IDs of the user; this blocks impersonation, identity theft, and internet bot interventions.
Flagging High-Risk Transactions For Monitoring
By coding, the bank’s regulatory restrictions and money transfer threshold can prompt the software to red flag high-risk transactions occurring in suspicious accounts.
Identifying And Blocking Money Mule Activity In User Accounts
An unknown person can’t remotely operate a genuine customer’s account for money mule purposes. The biometric face and voice recognition mismatch between the customer and the fraudulent person’s profiles automatically red flags the risky transaction.
High-Tech Authentication Regimes Can Fight Economic Crime And Block Money Laundering
Money laundering legitimizes criminal profits derived through theft, extortion, embezzlement, drug trade, and terrorism. Financial networks and larger economies suffer distortion and instability as a result of money laundering.
Money launderers want access to global banking networks to take advantage of fast wire transfers to push money beyond the jurisdiction of local law enforcement.
The Achilles heel of the money launderer is the entry point into the financial system. The brilliance of human investigative power isn’t enough to stop financial malfeasance. Banks need cutting-edge authentication technology’s computing power, agility, and scalability to identify and block wrongdoers. Authentication technologies riding on Artificial Intelligence and deploying the analytical power of biometrics show the way by assuring banks that they deal with customers whose identities are beyond doubt and whose transactional activities are unquestionably legitimate.