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Inside Macquarie Bank’s data transformation journey

Australia’s Macquarie Bank has moved all its data and analytics to the cloud and is applying machine learning to detect fraud and improve customer experience

As a rapidly growing digital bank, Macquarie Bank needed a strong data foundation to enable it to be more agile in scaling up its business while meeting new regulatory requirements.

That foundation is now a key part of the bank’s digital transformation strategy, which was conceived as early as 2013, to modernise all aspects of its infrastructure, environments and applications, which include a new core banking platform.

“There were three key guiding principles that underpinned that transformation strategy, including a relentless focus on client and employee experience, strong risk management and simplification,” said Ashwin Sinha, chief data officer of Macquarie’s banking and financial services group.

With data being a key enabler of Macquarie’s transformation efforts and fundamental to every decision and initiative across its business, the bank has invested significantly in moving all its data and analytics to a cloud-based data architecture.

“All of our data is housed on cloud and is being drawn upon for a variety of regulatory and analytical projects,” said Sinha. “Because of the efficiencies we’ve enabled, we’ve been able to reduce the cost of running data platforms by 50% and we have applied machine learning to areas such as fraud prevention, client service and default management.”

As an organisation that is analytically minded and data-driven, Macquarie Bank is particular when it comes to delivering client experiences and being proactive in managing risks, said Sinha.

“We achieve this data-driven culture through things like our data governance process for critical data elements, analytical dashboards for decision-making and the use of machine learning to solve complex problems,” he added.

During management meetings, for example, the bank’s senior executives rely on Tableau dashboards to drive discussions based on data that reveals business performance, opportunities and risks that need to be addressed.

“A lot of this has been achieved by continuously operating and delivering a variety of projects using an agile approach and a cross-functional team from different parts of the business,” Sinha added.

For now, he said the bank does not intend to monetise any client data – as the data is owned by clients – and that its primary focus, through the use of data, is to improve client experience or better manage different types of risks.

Being forward-thinking about the use of cloud, digital platforms and data, Macquarie Bank has been selective with talent – and getting talent well-versed in areas such as data science and data engineering has been challenging.

To overcome these challenges, Sinha said the bank has focused on prioritising tasks, assessing the value of each feature it builds and optimising its continuous integration/continuous delivery (CI/CD) processes.

Read more about data analytics in APAC

  • Healthcare providers are harnessing data analytics to improve clinical and operational outcomes even as they continue to face challenges in data aggregation and data protection.
  • Informatica has consolidated its operations in four key Asia-Pacific markets in a move that will enable it to better meet the demand for cloud-based data management software.
  • Teradata has been positioning itself to capture the region’s cloud opportunities through not only R&D, but also efforts to support enterprises in their cloud journey.
  • Snowflake set foot in APAC just three years ago and has started to gain traction among large enterprises in Singapore, India and Southeast Asia.

On the lessons the bank has learned on its data transformation journey, Sinha said that with technological approaches and tools in the data realm constantly changing, it was important for data architectures to be flexible and able to adapt to changes while meeting customer demands.

He also urged organisations to question if certain data assets were needed, and the kinds of decisions the data would support. “Otherwise, you could create a lot of unnecessary assets.”

Banks have been one of the keenest adopters of big data analytics and will remain so as they look to capitalise on financial, transactional and customer data.

IDC research shows that spending on big data technology in banking and financial services in Asia-Pacific is expected to grow at a compound annual growth rate of 15.6% from 2019 to 2024.

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