How Google Cloud’s AML AI redefines the fight against money laundering
Google Cloud’s AML AI represents an advancement in the fight against money laundering. By replacing outdated transaction monitoring systems and embracing AI technology, financial institutions can now stay ahead of evolving financial crime risks, improve operational efficiency, ensure regulatory compliance, and deliver a superior customer experience.
In this Help Net Security interview, Anna Knizhnik, Director, Product Management, Cloud AI, Financial Services, at Google Cloud, explains how Google Cloud’s AML AI outperforms current systems, lowers operational costs, enhances governance, and improves the customer experience by reducing false positives and minimizing compliance verification checks.
Money laundering is becoming increasingly sophisticated. How does Google Cloud’s AML AI adapt to these evolving tactics?
Most incumbent AML products are overly reliant on manually defined rules which are inefficient for identifying suspicious activities. Money launderers can learn and work around these rules to avoid detection. Even in the most advanced implementations, current rule-based systems cannot keep pace with the dynamic nature and constant adaptability of bad actors. As a result, only a fraction of suspicious activity is reported. At the same time, a high percentage of rules-based alerts are false positives, wasting valuable investigator time.
AML AI replaces legacy transaction monitoring systems with an AI-powered product that significantly improves financial crime risk detection. Google Cloud’s AML AI provides a consolidated machine learning (ML)-generated customer risk score as an alternative to rules-based transaction alerting. The risk score is based on the bank’s data including transactional patterns, network behavior, and Know Your Customer (KYC) data to identify instances and groups of high-risk retail and commercial customers. The product can adapt to changes in underlying data, delivering ongoing and accurate results which increases overall program effectiveness and improves operational efficiency.
Why did Google Cloud decide to build a solution for AML now?
Our goal has always been to make Google Cloud the platform of choice for the financial services industry. We bring innovations that combine the best of Google Cloud technologies, data, infrastructure and AI and ML to organizations across the financial ecosystem to help solve their biggest challenges. Our AML AI product builds on this commitment, as we are already seeing the value that AI-powered technology can provide banks to manage risk and fraud, and improve operational efficiencies.
What differentiates AML AI from other anti money laundering products in the market?
We are taking an AI-first approach to help solve the challenges with transaction monitoring and better identify suspicious activity. AML AI brings together Google’s strength in AI and ML with deep financial services expertise, and it’s also underpinned by the resiliency and security of our cloud platform. Thus far, AML AI has significantly outperformed incumbent systems across Europe, Latin America, and Asia Pacific.
How has Google Cloud’s AML AI helped financial institutions improve their customer monitoring framework and detection capabilities?
We are working with a number of global financial institutions to help them more effectively and efficiently detect money laundering. For example, Google Cloud’s AML AI is the core of HSBC’s primary AML transaction monitoring system in its key markets. The scalability and high-performance computing power of Google Cloud enables HSBC to significantly reduce batch processing time for HSBC’s large customer base and drive advanced models to improve detection capability and deliver more accurate results. HSBC found AML AI identified 2-4x more suspicious activity, while reducing alert volumes by more than 60%.
Google Cloud’s AML AI is said to minimize the need for additional compliance verification checks. Can you discuss how this has improved the customer experience?
Rule-based transaction monitoring systems tend to generate a lot of false positive alerts. By increasing precision and significantly reducing false positives, AML AI minimizes the need to engage with customers for additional compliance verification checks.
For instance, HSBC told us that they don’t want to waste their customers’ time by reaching out for more information on what ultimately turns out to be a false positive alert on suspicious activity.
By using AML AI, HSBC saw an over 60% reduction in those Requests for Information (RFIs), in part because of the increase in the amount of data available to their investigators, and because AML AI increased precision and reduced the overall volume of alerts. This allowed HSBC’s front line staff to focus on investigating high quality alerts and not introduce unnecessary friction into customer experience by asking customers for information that actually isn’t relevant to a determination that the bank’s investigators need to make.
How does Google Cloud plan to incorporate generative AI foundations into the financial services industry, and what benefits do you anticipate this will bring?
AML AI can help customers reduce their operational costs while simultaneously improving the strength of their AML program. In the future, Google Cloud plans to provide generative AI foundations for the financial services industry with the goal of boosting employee productivity, for example, to reduce the time needed for an analyst to investigate potential suspicious activity.