HSBC Embraces Google Cloud For Big Data Analytics And Money Laundering Detection

HSBC has turned to Google to take its big data and analytics into the cloud, while also embracing machine learning to make batter sense of that data and detect money laundering.

At Google Next 2017 in London, Dr David Knott, chief architect at HSBC, explained that the bank has worked with Google’s cloud division to be ready to put its big data systems securely into the cloud.

Knott highlighted that by doing this, HSBC will gain access to the multitude of machine learning capabilities Google offers within its cloud services, to improve the analysis of massive amounts of data, notably when is comes to detecting money laundering activity.

“We’re working with Google to build machine learning models to increase our ability to detect genuine cases of financial crime and money laundering and to reduce the false positives so we can really focus on catching the bad guys,” explained Knott.

Climbing to the cloud

With 37 million customers in more than 70 countries, HSBC is no stranger to big data. However, the amounts have grown massively, from 56 petabytes (PB) in 2014 to 93PB in 2017.

As such, HSBC needs a new way to better handle and analyse its data.

Knott said the bank started out using traditional sequential (SQL) databases but struggled to do analytics on such a setup so started using data warehousing systems to better crunch big data.

But such a setup struggled with the growing amount of data at scale and to handle unstructured data. So the bank turned to Hadoop, embracing the open source big data system; but this came with its own challenges.

“We’ve experienced mixed success; it turns out that putting that stack together, integrating it, making it work, making it operate at scale, industrialising it, is pretty hard. So [while] we’ve derived some great insight, we’ve found the data difficult to manage,” said Knott.

Yet finding a system to take care of this was no small ask. Knott explained that HSBC wanted to have the performance and data integrity of SQL, the analytics capabilities of data warehousing,  the speed and scale of the Hadoop ecosystem, including a cloud-first strategy for big data, analytics and machine learning.

“We want to access the new wave of data technologies which [are] what’s powering machine learning,” he said. “What we don’t want is the headache of managing all the massive infrastructure and all the technologies that sit underneath those capabilities ourselves.”

Enter the Google Cloud Platform.

With data analytics, open source, and machine learning at the core of the Google Cloud Platform, as well as its significant cloud footprint, derived from the infrastructure Google developed to support its own data services, the Google cloud offered a near magic-bullet for HSBC.

After working with Google for four to six months, HSBC now has a system ready to go live. Knott highlighted that the simplicity of embracing the Google Cloud Platform, a missive the search giant is pushing heavily through its lock in-free, API rich, and competitively priced platform, would have allowed the bank to move faster were it not for the need to rigorously adhere to the finance sector’s rigorous security, privacy and compliance rules.

“We’ve effectively spent the time to secure for ourselves a safe route to the cloud,” he said, noting that future cloud migrations will be faster as a result.

While concerns over cyber security and data compliance may pour cold water on cloud-first ambitions, if a major global bank like HSBC can make significant in roads into the cloud, there is no reason for other to not follow suite.

And for Google, having HSBC on its books builds a solid case to prove to big businesses that its cloud is ‘enterprise ready’ and can more than take on the likes of Amazon Web Services and Microsoft Azure.

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