Google Cloud Launches "Anti Money Laundering AI" Service After Successful Trial With HSBC

Google Cloud illustration. (photo; dock. google cloud)

JAKARTA - Google Cloud recently announced the launch of the "Anti Money Laundering AI" (AMLAI) service after successfully undergoing trials with London-based financial services group HSBC.

AMLAI uses machine learning to create risk profiles, monitor transactions, and analyze data.

"AI-based transaction monitoring replaces a manually specified regulatory-based approach and leverages financial institution data power to train advanced machine learning models (MLs) to provide comprehensive views on risk scores," wrote a blog post from Google Cloud.

In practice, Google Cloud claims that their pilot partner, HSBC, has seen a two to four-fold increase in the number of positive warnings and a 60% drop in false warnings.

The cost of this service will vary depending on the number of customers served daily with the AML system and risk assessment, as well as how many customers are included in the training data set used to develop the model.

The launch of AMLAI marks the advancement of Google and Google Cloud's ambitions in fintech. While the current main trend in AI is the development of generative AI products such as Google's Bard chatbot, the company has secretly made its presence felt as a fintech developer and banking service provider.

Moreover, during the COVID-19 pandemic, Google quickly launched a salary protection program loan processing tool. Over the years, the company has been trying alternative payment solutions, such as Google Pay's widely used services and Google-backed debit cards equipped with near-field communications (NFC) connectivity.

Google's further involvement in the AML sector could be a positive sign for this growing industry. According to an analysis from BlueWeave Consulting, the global AML market size is estimated at around US$3 billion (Rp45.1 trillion) in 2022 and is expected to reach nearly US$8 billion (Rp141.5 trillion) towards the end of the decade.

Projected growth-barrier factors include the emergence of non-traditional payments, persistent regulatory changes, and a slow increase in the number of money laundering cases worldwide.