Money laundering is turning "dirty" money "clean" by making it look like money from crimes actually came from legitimate sources (source: fbi.gov). Between 2 and 5% of global GDP, or up to $2 trillion, is laundered each year (source: UN Office on Drugs and Crime). Connected to activities ranging from drug and human trafficking to terrorist financing, these cash flows cost financial institutions up to hundreds of millions annually on anti money laundering technology and operations.
Google Cloud's Anti Money Laundering AI (AML AI) product is an API that scores AML risk. Use it to identify more risk, more defensibly, with fewer false positives and reduced time per review. This API:
- Generates monthly risk scores for retail and commercial banking customers
- Is designed to meet model governance requirements
- Is explainable to analysts, risk managers, auditors, and regulators
- Replaces or complements legacy transaction monitoring
- Can be extended with a customer's own supplementary risk indicators
Data it uses
AML AI uses no data other than what you provide. AML AI does not use Google data to enrich your datasets.
Accuracy and coverage depend on the quality and completeness of data you provide according to the AML AI schema and the volume and quality of customer exit or suspicious activity report (SAR) data to train on.
Incorporate AML AI into your AML process
AML AI trains on your core banking data, suspicious activity information, and other data in your Google Cloud environment. Use the API to produce risk scores and accompanying explainability output to support your alerting and investigation process.
Feed investigation data into AML AI to regularly update models and risk scores.
Supported financial products
The supported retail banking products include:
- Checking or current accounts
- Savings
- Credit cards
- Mortgages
- Personal loans
This does not include the following: brokerage, trading of any kind, cryptocurrency, or insurance.
The supported commercial banking products include:
- Cash accounts
- Loans
- Lines of credit
- Direct payments from customers
This does not include the following: capital markets, trade finance, or foreign exchange.
Risk typologies supported by AML AI
AML AI can identify money laundering risk across five core AML risk typologies related to transaction monitoring. With sufficient investigation and supplementary party data, it can cover more typologies.
Allowlisted customers can access additional AML AI documentation to support compliance and model risk governance processes.
Money laundering through high-risk jurisdictions and cross-border activities
In this risk typology, money launderers use countries or financial systems that have weak regulations or enforcement against money laundering as a means of obscuring the origin and ownership of funds by moving them through. High-risk jurisdictions are typically those with weak anti-money laundering (AML) laws, inadequate oversight of financial institutions, and a lack of cooperation with foreign authorities. Money launderers often use shell companies, trusts, and other legal entities incorporated in these jurisdictions to move and hide the proceeds of illegal activities. In these jurisdictions, money launderers can process transactions that would be flagged as suspicious in other countries. The high-risk jurisdiction list is dynamic as it is reviewed periodically by local regulatory or intergovernmental organizations.
Money laundering through domestic funneling and pass-through funds
In this risk typology, money launderers introduce (place) illegally-obtained funds into the financial system in a way that is difficult to trace in order to obscure the source of the funds. Funneling is the first step in the money laundering process, and it involves moving the illicit funds into the financial system so that they can be further laundered. Funneling can be done through various means, such as through shell companies, offshore accounts, cash-based businesses, or money mules.
Money laundering through shell companies and professional enablers
In this risk typology, money launderers utilize anonymous shell companies, which are companies that exist only on paper and have no actual business activities or assets, to move and conceal illicit proceeds. These companies can be used to create the illusion of legitimate business transactions, hiding the real source of the funds and making it difficult to trace. Shell companies can be used for a variety of money laundering activities, such as wire transfer of funds, investing in real estate, buying luxury items, or holding money in offshore bank accounts. They can be created through various ways in secrecy jurisdictions and are often used in combination with other money laundering techniques, such as funneling, layering, and structuring, to create a complex web of financial transactions that can be difficult to unravel.
Money laundering through structuring funds
In this risk typology, money launderers break down large transactions into smaller ones (structure) to evade detection by financial institutions and regulators. The goal of structuring is to avoid triggering reporting requirements for transactions above a certain threshold. Money launderers often make multiple small transactions in an effort to stay below this threshold, or they can use multiple individuals (that is, "smurfs") to conduct transactions on their behalf. Structuring can also include round-tripping, where the launderer breaks a large amount of cash down into smaller amounts, and then deposits the cash into multiple locations and accounts.
Money laundering through money muling
In this risk typology, money launderers use individuals, referred to as "money mules", to receive and/or transfer illicit proceeds on their behalf. Money mules can be unknowing participants or knowingly involved in the illegal activity. They can be asked to open bank accounts, receive and then transfer money, or make purchases with the illegal funds, in order to conceal the true source of the money and make it appear legitimate. The money mules, acting as intermediaries, can be used to make the transactions more difficult to trace.