Trucking Authority For Sale, ANZ eyes deep learning to help make better decisions about risk

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Trucking Authority For Sale, ANZ eyes deep learning to help make better decisions about risk-While banks are basically intelligent information with strong knowledge, using this knowledge to determine a user’s credit decisions is a huge task.

However, the ANZ concept, developed in co-operation with Nvidia and Monash University researchers, has demonstrated that in-depth learning methods can be combined with customer data to evaluate risks and make them more often than before.

The proof of the concept was the use of the neural network for a forecast that customers may not pay.

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Identifying risky loans is the key to reducing exposure to banks and the need to maintain large reserves, says the main risk of ANZ distribution, Jason Humphrey, conferences at Sydney Nvidia AI.

Previously, banks used two important risk assessment methods. The first app rating is a static estimate that is calculated at the customer’s request. Humphrey said that from the age of sixties the risk assessment did not change substantially from a mathematical model that combines the data. which are included in the client’s application and information that the bank also has information provided by the credit institution. All of this information is used to produce results that represent the standard customer default, he said.

Behavior allows a 50% prediction of the results of the application, he said at the conference. “When behavior detects that a user can pay for a product, it will be a great future and very important when determining whether the customer is still paying for the product,” Humphrey said.

Scoring behavior has, however, boundaries and has not changed significantly in the 80s and 90s, he said. In general, the availability, accuracy and volume of data, as well as the frequency at which users can estimate or update models, are generally limited.

Although app evaluations usually only occur once, behavioral evaluations have always been managed events, Humphrey said. When you create an account and get the sum, and list all the events a month, and you have the balance, it is usually at this time, the risks in the banking sector to look at the client side. “,

The problem is that the behavioral decision generally compares the monthly closure rates. “Frustration is obvious, you do not know what’s behind it,” Humphrey said.

Two customers can spend one month on the same balance sheet or report on the time and the nature of the events over the same period can demonstrate that the bank’s risk is significantly higher.

“To give this context, we currently have more than 10 million events per day at ANZ,” he said. “Events that are important to me in modeling will never be able to create partnerships with Nvidia.” During the current year, 1.7 billion transactions for the consumer are only costs and over 300 million transactions from a small business perspective. ”

Designed and tested for five days, the evolving perception of in-depth learning demonstrates how this information can be used to make risk decisions.

The project uses a proactive nerve network formed on a number of TensorFlow DGX-1 platforms with Nvidia Intel processors with two kernels of 20, eight GPUs Nvidia Tesla P100 and 512 gigabytes of RAM.

The data is made to use a spark wave with five nodes and each node that works with eight processors and 48 GB of RAM. The bank used data from 1000 credit card accounts. the rest of the tests.

“We had great results,” said Humphrey, including the Gini coefficient used to assess the risk of 0.78-0.82. A model test of about 200,000 accounts lasted only 30 minutes, he said.

“The ability to handle accounts and events is very exciting,” Humphrey says.

“The way things are done today at the beginning of the month and at the end of the month, the risk that needs to be updated

When you start a daily log, you’ll suddenly see the latest results that change behavior somewhat … You can follow the risk shift dynamically.

“The key to risk management, banking and finance is that speeding up problem solving can reduce the loss, detriment or aggravation of customer experience.”

Additional information: ANZ provides support for digital images