Wednesday, 14 April 2021
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ANZ eyes deep learning to help make better decisions about risk

Although banks are by their very nature data-rich businesses, leveraging that wealth of information to make better decisions about the risk of lending to a particular customer is a formidable challenge.

Now, however, an ANZ proof of concept built in partnership with Nvidia and Monash University researchers has shown that deep learning techniques can be combined with customer data to better assess risk and to also do so on a more frequent basis than has previously been possible.

The proof of concept sought to use a neural network to predict which customers were likely to default on payments.

Identifying high risk loans is key to reducing the bank’s exposure and the need to maintain large asset reserves, ANZ’s head of retail risk Jason Humphrey today told the Nvidia AI Conference in Sydney.

Historically banks have used two key methods for assessing risk. The first is application scoring, which is a static score calculated at the point of a customer’s application for a product.

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