Money laundering is the act of processing illicit money , which is generated by any illegal or criminal activity i.e (drugs trafficking, terrorist funds, embezzlement, corruption, or gambling), into the legitimate business. The money from the criminal activity is considered dirty, and the process “launders” to make it look clean.moreover is a virus in financial sector.
Example of money laundering
When the drug dealer wishes to buy a new car and has earned money illegally from selling drugs, the dealer needs to launder the cash in order to have it appear legitimate. The drug dealer owns a small laundromat, which is highly cash-intensive. The cash from the drug deal is mingled with the laundromat's money and then taken to a bank for deposit. Drawing a check from the laundromat's account, the dealer can then buy the car without suspicion.
Stages of money laundering
Placement: In the first stage, money enters the banking system. This stage is termed placement.
Layering: The second phase involves mixing the funds. It is important to mix the funds from illegal sources with legal.
Integration: It is relatively very difficult to detect money laundering at this stage. In the third stage, money flows back to the beneficiary.
Methods of money laundering
Structuring: This is a method of placing cash deposits that involves dividing larger deposits into smaller ones. This is used to avoid anti-money laundering reporting requirements, as well as to purchase bearer instruments like demand drafts and then deposit those in small amounts of cash. Smurfing is another variation of Structuring.
Cash intensive business: Criminals use the cash-intensive nature of businesses to launder money. The criminal deposits cash proceeds of crime into bank accounts belonging to these businesses and then withdraws that money by withdrawing money from legitimate business accounts. It is difficult for authorities to trace and recover the illicit funds because they are deposited into legitimate bank accounts.
Gambling: Gambling on cricket:In cricket matches, one can bet on the outcome of the match as well as a single over. Gamblers choose cricket matches to place their bets because they offer many possible outcomes.
Sell companies and funds: is when dummy companies (‘shells’) are created by money launderers to hide illegal funds and avoid paying taxes. The relative anonymity around declaring the true identity- beneficial corporate ownership- of such entities makes this possible. For an example of how Canada is countering the use of shell companies to launder money and avoid tax.
Round Tripping. Here, crafty accountancy methods transfer funds to offshore enterprises or jurisdictions with weaker AML controls, before being returned as direct foreign investment to circumvent tax obligations. In Europe, the seismic fall of fintech giant Wirecard is an example of this money laundering method. You can read more about the scandal here.
Bank Capture. This is when the money launderers themselves gain control of a financial institution. They then move funds around without scrutiny and can transact with other, above-board banks to legitimise their funds. This might present severe financial risk to partnering legitimate institutions, which can be held liable.
Casinos. Criminals use casinos to launder money by swapping illegally acquired cash for gambling chips, playing the tables or machines for a short period, then cashing the chips for a cheque or receipt. The cash is then claimed as a return from gambling (other forms of gambling are also employed to launder money using similar techniques). To see how Australia is clamping down on the use of casinos to launder money.
Voluntary Disclosure Schemes: These are the tax amnesty schemes for the tax defaulters. If the defaulter declares his hidden wealth, the government offers to take no action against the default. With this in mind, it is a limited-time opportunity for taxpayers to pay taxes on evaded income, in exchange for forgiving liability. For example, those that legalise unreported assets and cash in tax havens.
HOW AI IS STOPPING MONEY LAUNDERING
Anti-money laundering (AML) and know your customer (KYC) consistence may be changed by man-made brainpower (artificial intelligence).Artificial intelligence frameworks may likewise mine immense measures of information through KYC check organisations for risk-important data for hostile to tax evasion reasons, making recognizing high-risk clients simpler.
Artificial intelligence is useful while finishing tedious exercises since it saves time, exertion, and assets that can be diverted to higher-esteem client assignments. Normal language handling and AI (ML) are two artificial intelligence advancements that, when joined, can create jump mechanisation prospects in areas of client life cycle the board (CLM) that are as of now work escalated, tedious, and lessen the chances of error.
AI and KYC Process
Artificial Intelligence (AI) has the potential to play a significant role in the KYC process, thanks to KYC labelling companies and services. The technology can assist in streamlining the regulatory and compliance process, minimise fraud to some extent, remove human mistakes, automate repetitive operations, and save time and money. The following are some, but not all, examples of how AI may help with the KYC process:
Image processing
Image processing aids in scanning evidence papers in real time and enhances the automated decision-making process.
Pattern recognition
Checking for discrepancies in submitted documents in real-time and returning near-instantaneous results.
Behaviour Analysis
Collecting data from a chatbot, for example, and performing behaviour analysis on that data can help identify a customer’s potential risks and make the appropriate decisions, such as automatically re-running a KYC on the customer or alerting the business with all possible risks discovered so they can act accordingly.
Link analysis
Link analysis examining various sorts of items and transactions using AI analytical models and discovering linkages and relationships between them would make the KYC process more effective and rapid.
OCR
Using an intelligent data extraction engine and auto-filling KYC paperwork, customers may be onboarded more quickly.
Best KYC Labelling Companies
Several corporations and organisations employ Artificial Intelligence (AI) in the KYC process to meet their goals, increase efficiency, and reduce overall costs.
By merging data from several sources, analysing typical money transfer patterns, and learning to discern between routine and suspect activity, this technology is ideal for getting a complete picture of a client.
You need high-quality training data to develop a high-quality AI model. We supply annotated or labelled datasets for machine learning and computer vision at Cogito, allowing you to build a high-quality AI model.
