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PODCAST: rise of the machine (learning)

Last year made us all aware of how fast technology can evolve. It’s now faster, more sophisticated, more capable than we would have imagined even 12 months ago.

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Advancements in artificial intelligence (AI) and machine learning help us look at patterns in data and make predictions and recommendations. That’s something we’ve been doing for decades but with AI and machine learning we can get results of our analysis in as little as four seconds as opposed to maybe weeks. 

"I'm excited because it is really good for our staff. You know, there's so much value added from an individual point of view because banking can be notoriously paper intensive." - Sreeram Iyer

Recently, I caught up with ANZ’s Chief Risk Officer, Australia Division, Jason Humphrey, and Chief Operating Officer, Institutional, Sreeram Iyer, to hear how their teams are utilising new technology, what we’ve learnt through our adoption of AI and machine learning and how we’re helping our people and our customers.

Jason is working on a few of the complex processes we’ve been wanting to automate for some time now and he’s seeing some positive results.

“[We’re] looking to automate the home loan process - very document driven - trying to condense that, trying to extract data they can send into our decision systems for me to make a decision,” Jason says.

“The really exciting part is in today's world, using the old school techniques [such as neutral networks and gradient boosted models], we can make a decision after all those processes have been conducted within four seconds.”

A faster decision means customers don’t need to find supplementary documentation or spend time waiting for approval. They can get their answer and focus on what’s important: getting into their new home.

But it’s not just the home loan process that’s seen the benefit of new technologies. Our Institutional team has been using machine learning for the past few years and Sreeram says even three years ago the team saw the promise the tool held. Now, they’re seeing results.

“I'm excited because it is really good for our staff. You know, there's so much value added from an individual point of view because banking can be notoriously paper intensive,” he says.

“This is a combination of technologies and capabilities. The machine now…the transfer of paper to image, the quality and accuracy of imaging, the ability to read, the ability to interpret and then the ability to process; this is coming together for the first time, at least in my career.

“We have seen cases where 50 per cent of the manual effort before has been. We have seen cases where our internal times have improved roughly 40 to 50 per cent. So I think it's absolutely made things better.”

Although Sreeram reminds us that “comes with its challenges and caution and management of governance, as with any technology.”

Jason agrees. As part of the Risk team his role is think about these tools and models as they’re being designed and ensure they’re done in a way that’s low risk and good for customers. That means solving for potential bias and ensuring the models are explainable.

“Probably as talked about and as important as bias is explainability,” Jason says. “An individual customer has the right to understand how you made that decision. So it is a responsibility of people like risk management when they build these models to be able to explain them not only to consumers but also to the board.”

New technologies can only take us so far. It’s also how we embrace the changes they offer, how we adapt to new risks and tools and what we do to consider the impact on our customers and our people.

“If there's a better customer outcome, if our employees have a better experience, if a financial outcome in the form of getting more for less is better, and if we're able to manage our risk better to the extent of the use of the pricing to deploy, then it's a great combination,” Sreeram says.

You can listen to our full conversation by clicking on the podcast above.

Shayne Elliott is CEO of ANZ

The views and opinions expressed in this communication are those of the author and may not necessarily state or reflect those of ANZ.

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