What is money laundering detection?

The process involves:

• Monitoring financial transactions for suspicious activities.
• Implementation of compliance measures such as know your customer (KYC), and
• Reporting anomalies and money laundering schemes to the appropriate authorities.

In an era of globalised financial systems and technological advancement, the methods used by money launderers have become more effective, sophisticated and widespread.

An estimated $2 trillion in dirty money is laundered by banks every year and is linked to various types of criminal activity such as insider trading, bribery, embezzlement, drug trafficking and terrorism.

The key components of money laundering detection

Money laundering detection comprises various policies, procedures and technologies that work together to monitor and analyse financial transactions.

Here is a brief look at the key components.

Transaction monitoring programs

For Australian businesses, transaction monitoring programs are a crucial part of compliance with the Anti-Money Laundering and Counter-Terrorism Financing (AML/CTF) Act 2006.

The Act requires each business to document how it monitors customer transactions and the processes it follows to identify suspicious transactions.

Monitoring program legalisation also requires companies to self-determine their money laundering risk.

To do this, they should consider:

• The types of customers they have – particularly if some occupy prominent public positions in government or organisations (otherwise known as politically exposed persons or PEPs).
• The types of designated services they provide. Services at risk of money laundering include the financial services, gambling, bullion and digital currency exchange sectors.
• How the services are provided (for example, face-to-face or online), and
• The countries they operate (or do business) in.

Know your customer (KYC) procedures

KYC procedures help financial institutions verify client identities, understand the nature of their activities and define appropriate risk levels.

This is another part of AML compliance that requires businesses to document their customer identification procedures. Such procedures are based on the type of customer in question and their associated risk level.

Other processes that need to be detailed include:

• How the business verifies the identity of consumers and real entities (companies or organisations).
• How the business responds to data discrepancies during verification. For example, when the name of an applicant in their passport does not match the name they provided.
• The risk-based controls and systems to be used if more information needs to be collected before verification.
• Simplified verification procedures for certain companies, such as domestic Australian companies and majority-owned subsidiaries of Australian companies listed on the ASX, and
• Simplified verification procedures for trusts, such as managed investment schemes and government superannuation funds.

Technology

As the prevalence of digital payments continues to increase, so does the diversity and complexity of financial crimes related to these types of transactions.

While traditional AML methods were resource-intensive, new technology powered by artificial intelligence (AI) and machine learning (ML) now does most of the heavy lifting.

More payments mean more data to analyse, but happily, ML can now detect patterns in vast datasets and flag potentially suspicious activity for review.

Other tools monitor transactions continuously such that threats can be detected before they have the chance to materialise.

Artificial intelligence is well suited to the optimisation of routine, rule-based tasks, such as those that are central to customer onboarding processes and enhanced due diligence (EDD).

With the rise of cryptocurrency, specialised tools have also been developed to trace transactions on blockchain networks and detect illicit activities related to digital assets.

The money laundering detection process

While the specifics vary from one bank or jurisdiction to the next, the process of detecting money laundering tends to follow a four-step process.

Step 1 – Detection

AI and ML-powered systems identify suspicious transactions based on predetermined criteria.

These invariably relate to:

• Unusually large, frequent or complex transactions.
• Transactions that utilise shell companies and off-shore accounts in known tax havens.
• Transactions that involve high-risk regions or countries.
• Transactions that involve sanctioned persons or organisations.
• Transactions that are split to avoid threshold transaction report (TTR) obligations.
• Unexpected transaction patterns that appear to serve no legitimate purpose, and
• Transactions that are inconsistent with a customer’s prior behaviour or risk profile.

Rule-based systems

Rule-based systems then alert compliance officers to the presence of potentially fraudulent transactions. In the case of unusually large transactions, any deposit that exceeds $10,000 may be marked for review.

Other rule-based systems sent alerts based on:

• Pattern detection – a customer may make multiple transactions of $9,900 to stay under the $10,000 threshold and avoid bank submission of a TTR to AUSTRAC.
• Behavioural analytics – as touched on above, AI and ML compare current transactions to a customer’s baseline behaviour and look for anomalies.
• Geographic monitoring – where transactions that involve high-risk countries are automatically flagged.

Step 2 – Review

Alerts are then distributed to compliance analysts or investigators within the bank’s AML, financial crime risk management (FCRM) or financial crime compliance (FCC) department.

Analysts conduct an initial review to determine if the alert is a false positive or warrants further investigation. They review the customer’s profile, account history, KYC information and expected transaction patterns.

Step 3 – Investigation

Enhanced due diligence is a core part of a money laundering investigation.

As part of EDD, analysts may:

• Request additional documentation such as invoices, contracts or other explanations for the transaction.
• Verify the legitimacy of the origin of the funds (particularly for large amounts), and
• Ascertain who owns or controls the account in question (for businesses with complex ownership structures).

In this phase, banks may share information, pool resources and coordinate actions obtain more clarity on the type and extent of fraudulent transactions.

Step 4 – Documentation, auditing and reporting

In step four, banks keep detailed records of suspicious transactions, the actions they took and any interactions with the customer.

Documentation may serve as evidence if the case proceeds to court, but it also supports audits (internal or external) that identify areas where future AML procedures could be improved.

Banks also report to (or collaborate with) various bodies such as AUSTRAC, the Australian Federal Police (AFP) or ASIC in cases where money laundering overlaps with securities fraud or market manipulation.

Summary:

• Money laundering detection is the process of identifying and preventing the concealment of illegally obtained funds.
• The key components of money laundering detection are transaction monitoring, KYC procedures and technology. Each works to help businesses identify potential threats and comply with AML regulations.
• The money laundering detection process has four key steps. In the detection phase, banks use rule-based systems to flag suspicious transactions. These are then reviewed and investigated, with processes documented and results shared with other banks and regulatory bodies.