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Artificial intelligence and data automation are becoming essential tools for financial services risk teams.
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Look through the fog of fintech risks to silver-lined cloud
03 September 2022
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11 minute read
Cyber security threats have become more prolific, more sophisticated and increasingly difficult to detect. At the same time, the geopolitical situation that often underlies these threats remains incredibly volatile.
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Add to this increasingly complex regulatory demands, new competitors getting to market faster and the need to have systems that stand up to these challenges and it is clear: risk management in financial services has never been more demanding.
Financial services businesses have always needed to hone their sense of risk but today that requirement is even higher. They need cutting-edge risk management teams, capable of using new technologies to identify, analyse and respond to these threats and challenges in real time.
Tools like artificial intelligence and machine learning, and the cloud infrastructure that enable them, are set to become indispensable as the risk landscape changes.
Fintech entrants increase the pace
For years, the financial services industry was dominated by the same established brands. Trusted names with a rich history. This legacy comes with baggage, however.
Companies built over decades have complicated, ageing systems. This can make it difficult to compete with start-up fintechs, which have the benefit of designing their data architecture from scratch with best-in-class systems, allowing them to make speed their greatest asset.
To compete with established names, they create new products such as loans or advice in a fraction of the time. Often, what used to take weeks of paperwork and consultation can now be achieved in a matter of days, or even hours.
For example, Macquarie’s Banking and Financial Services Group uses Google Cloud to provide personalised products for customers, such as home loan origination. Its cloud strategy and automation of its data centre reduced provisioning times from months to minutes.
This has raised the bar for established financial services businesses. That speed has irrevocably changed customer expectations. But risk teams need to provide the same level of astute supervision and protection as they always have. The risk of regulatory blowback and reputational damage is as high as ever. The pressure has just been dialled up.
To match this speed, financial services businesses need the right data architecture, data processes and data governance practices in place.
These businesses also need to ensure they have the right controls in place and that means knowing what their control landscape is, which risks they’re addressing, and systematically automating as many of these controls as possible.
All too often, business processes get automated and the core control is all manual.
Real-time data analytics and automation are key
Keeping up with the faster pace demanded of financial services isn’t the only hurdle these businesses need to get over. They’re also facing ongoing waves of regulatory change.
Quickly adapting to these regulatory requirements requires risk teams to have the right data processes and technology in place. It’s essential these systems — perhaps part of a wide-ranging digital transformation — are set up correctly from the start.
Real-time data analytics and automation allow risk teams to assess situations created by new regulation quickly and accurately, and this means they can help financial services adapt.
Bear in mind, these systems can be difficult and expensive to implement, as well as difficult and expensive to operate on an ongoing basis. There isn’t a lot of room for error.
But the potential benefits far outweigh the challenges, with a system that has the capacity and capability to respond to regulatory changes easily, with many complex, time-consuming tasks automated, and richer, deeper insights into the threats the business faces suddenly at the fingertips of the team charged with protecting it and its customers.
Better use of cloud technology can see this become a case of building once, and then using across multiple scenarios. By automating reporting tasks, such as home loan reporting, these teams can focus their efforts on the riskiest assets and investments versus taking the same approach to all opportunities.
A good example is neobank Up. A partnership between Bendigo and Adelaide Bank and software development firm Ferocia, Up uses a custom app tester and workflow based on Binary Authorization on Google Kubernetes Engine to deploy patches to protect the digital bank against malware.
Business as usual or true innovation?
Risk leaders and technology leaders need to make a decision sooner than later.
Are they going to continue along the business-as-usual path and spin up ad hoc projects to respond to threats?
Or are they going to fundamentally rethink their approach and take advantage of technology that can transform their organisations and their risk management functions?
While this process is complicated and requires a significant investment, the nature of the threats facing financial services is becoming increasingly sophisticated.
The regulatory burden is also growing, and competitors are pressuring established firms to do things faster than they ever imagined.
Meeting these challenges requires technology and cultural change.
Matt Pancino is director and industry solutions practice lead for Asia Pacific, Google Cloud.
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