#AML, also known as Anti-Money Laundering, process to prevent money launderers and terrorists engage in money laundering activities. AML screening following the compliance process to track the various techniques that disguise funds obtained from illegal activities including drug trafficking, corruption, tax evasion and fraud.
The reality is in the massive amounts of new legislation moving through national and international bodies. Massive inroads have already been made into the privacy edifice by successive raids against drug trafficking, money laundering, corruption, tax evasion, and, as no one will need reminding, terrorism.
How to categorise different types of fraud
When we talk about detecting #fraud, first thing we need to know is how to categorise different types of fraud. Indeed, fraud is so common that it can be categorized in countless ways. But fundamentally, every type of fraud is either organizational or individual. Here are some key characteristics of each.
1. Against individuals
This is when a single person is targeted by a fraudster — including identity theft, phishing scams and “advance-fee” schemes. Perhaps one of the most noteworthy and devastating individual frauds is the Ponzi scheme.
2. Internal organizational fraud
Sometimes called “occupational fraud,” this is when an employee, manager or executive of an organization deceives the organization itself. Think embezzlement, cheating on taxes, and lying to investors and shareholders.
3. External organizational fraud
This includes fraud committed against an organization from the outside, such as vendors who lie about the work they did, demand bribes from employees and rig costs. But customers sometimes defraud organizations, such as when they submit bad checks or try to return knock-off or stolen products. And increasingly, technology threatens organizations with theft of intellectual property or customer information.
How does AML work in Hong Kong?
In Hong Kong, the Hong Kong Monetary Authority (HKMA) is closely monitoring the AML status, in order to maintain the high-level reputation as an International Financial Centre (IFC). While compliance on AML also happens in different sectors, not only do banks shall be aligned with the ordinance, industry such as fforeign exchange/remittance stores or jewellery retail industry, are also key stakeholders in terms of the AML self-regulation, in response to the guideline from corresponding department.
As one of the members of the the Financial Action Task Force (FATF), Hong Kong is striving for meeting the international standards that aim to prevent these illegal activities and the harm they cause to society. In accordance with the provisions of the guideline from the regulator, authorized institutions (AIs) should establish and maintain effective policies, procedures and controls to ensure compliance with the relevant regulations and legislation on terrorist financing, financial sanctions and proliferation financing.
What are the regulations on AML in Hong Kong?
The Hong Kong Exchanges (HKEX) has set out a brief summary of the principal legislation in Hong Kong that is concerned with money laundering and terrorist financing.
Among other things, the AMLO imposes requirement relating to client due diligence and record-keeping and provides regulatory authorities with the powers to supervise compliance with the requirements under the AMLO. In addition, the regulatory authorities are empowered to (i) ensure that proper safeguards exist to prevent contravention of specified provisions in the AMLO; and (ii) mitigate money laundering and terrorist financing risk.
Among other things, the DTROP contains provisions for the investigation of assets suspected to be derived from drug trafficking activities, the freezing of assets on arrest and the confiscation of the proceeds from drug trafficking activities. It is an offense under the DTROP if a person deals with any property knowing or having reasonable grounds to believe it to represent the proceeds of drug trafficking.
The OSCO empowers officers of the Hong Kong Police Force and the Hong Kong Customs and Exercise Department to investigate organised crime and triad activities, and it gives the courts jurisdiction to confiscate the proceeds of organised and serious crimes, to issue restraint orders and charging orders in relation to the proceeds of organised and serious crimes, to issue restraint orders and charging orders in relation to the property of defendants of specified offences.
The UNATMO provides that it would be a criminal offense to: (i) provide or collect funds (by any means, directly or indirectly) with the intention or knowledge that the funds will be used to commit, in whole or in part, one or more terrorist acts; or (ii) make any funds or financial (or related) services available, directly or indirectly, to or for the benefit of a person knowing that, or being reckless as to whether, such person is a terrorist or terrorist associate.
How to do AML with AI?
Typically, many AI AML automation solutions are following the multistage approach. They begin with data descriptions and then progress to smart transaction-evaluation approaches.
In contrast to conventional machine learning approaches, deep learning methods can learn feature representations from raw data. In deep learning techniques, multiple layers of representation are learned from a raw data input layer, by using nonlinear manipulations on each representation learning level.
Deep learning has outperformed many conventional machine learning approaches on designed features in various AI tasks, including natural language understanding, image recognition, and speech recognition (LeCun et al. 2015).
How is the market trend on AI AML automation?
What’s even better than a proof from a data pack. Let’s take a look into a report from an industry survey.
According to market research in 2021, 25% of respondents have already integrated AI and machine learning into their AML solutions, with over half actively evaluating their implementation. Remarkably, 50% of those evaluating expressed their intention to integrate these technologies within the next year. This data demonstrates the industry's recognition of the power of AI in combating financial crimes efficiently and effectively.
Furthermore, the research highlights a significant shift towards public cloud adoption in AML. While only 4% of respondents were using public cloud-based AML software and services in 2019, a sweeping 50% are planning to leverage this technology within the next two years. Embracing the cloud offers scalability, agility, and cost-efficiency, enabling financial institutions to enhance their AML capabilities.
Notably, the research reveals a substantial increase in the application of "Machine learning for segmentation" as a business driver, rising from 13% in 2018 to an impressive 47% in 2019. This shift signifies the industry's understanding of the value that machine learning brings to AML processes, enabling more accurate risk segmentation and targeted compliance efforts.
At iFinGate, we're at the forefront of this AML revolution, delivering AI-driven solutions and cloud-based platforms that empower financial institutions to stay ahead of evolving threats and regulatory requirements.
How to do AML AI automation on transaction monitoring?
Transaction monitoring plays a crucial role in Anti-Money Laundering (AML) efforts and is of paramount importance in detecting and preventing illicit activities within financial systems. According to scholars (Winston Maxwell, Astrid Bertrand, Xavier Vamparys), below let us make a simple explanation on transaction monitoring for AML in a few steps.
Step 1: Automated review system
While doing transaction monitoring, every incoming transaction is first screened by an automated review system that uses the information gathered on the client profile through KYC and client due diligence (CDD), the transaction details, information concerning watch-listed countries and other triggers, such as key words, for alerts. Other data sources such as market activities, trade-based data or even social media and news feed may be used, subject to limits imposed by data protection laws.
Step 2: Alert confirmation & reporting on suspicious transaction
This automated review system will decide whether to generate an alert for a given transaction, either based on deterministic ‘if-then’ rules or based on more sophisticated AI models. All the generated alerts then follow a two- or three-step review by compliance experts who decide either to escalate the alert to the next review level or to close it.
Step 3: Financial investigation
Alerts that go through all review steps are then consolidated into SARs (Suspicious Activity Reports) that are forwarded to the FIUs (Financial Investigation Units) for investigation. Banks often terminate the accounts of customers subject to an SAR, but are not allowed to inform the customer that an SAR has been generated. The FIUs that receive the SARs generally provide no feedback about individual SARs, and investigate only a small portion of the SARs they receive.
Here are just some key steps to get you a brief idea on how AML AI automation works. AI automation has already emerged as a transformative force in the realm of Anti-Money Laundering (AML). By implementing robust transaction monitoring systems, financial institutions can contribute to the global fight against financial crimes and protect the integrity of the financial system. Although providing detailed solutions to AML process is way more complicated, you are more than welcome to REACH US now for more thoughts and insight on more solutions that fit your needs.