What are Capital Asset Pricing Models (CAPM)?
A capital asset pricing model, known as CAPM, outlines the relationship between systematic risk and the expected return of the asset, explaining …
Money laundering detection is the process by which financial institutions (and other entities) identify and prevent the movement of illicit funds.
The process involves:
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.
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.
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:
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:
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.
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.
AI and ML-powered systems identify suspicious transactions based on predetermined criteria.
These invariably relate to:
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:
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.
Enhanced due diligence is a core part of a money laundering investigation.
As part of EDD, analysts may:
In this phase, banks may share information, pool resources and coordinate actions obtain more clarity on the type and extent of fraudulent transactions.
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:
A capital asset pricing model, known as CAPM, outlines the relationship between systematic risk and the expected return of the asset, explaining …
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Eftsure provides continuous control monitoring to protect your eft payments. Our multi-factor verification approach protects your organisation from financial loss due to cybercrime, fraud and error.