Used Diesel Trucks For Sale In Nc, ANZ eyes deep learning to help make better decisions about risk–
While corporate databases will greatly benefit from this massive amount of data to make better decisions on customer credit risks, this is a big challenge.
ANZ proof of the concept developed with Nvidian and Monash University researchers has shown that in-depth learning methods can be combined with customer data to better assess risks more frequently than ever before.
The demonstration of the concept was to use the neural network to predict what users are likely to be paid for.
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Identifying risky loans is the key to maintaining the bank’s exposure and the need for large reserves, says Jason Humphrey’s Risk Diversification Heads Conference in New Zealand today.
Historically, banks have used two important risk assessment methods. The first is the application result, the statistical result that is calculated at the time a customer requests the product.
Humphrey stated that since the 1960’s the risk of getting feedback is not significantly different from the combined application of the mathematical data model to the customer and the information available to banks and the information provided by the credit institution. All of this information is used to get results that determine the probability of a presumed standard customer.
As a result of the use of the conference, the profit is 50% compared to the forecasts. “As soon as you see a buyer’s behavior as a product, it’s very far-reaching and it’s powerful to predict whether a customer will make such a payment product,” Humphrey says.
However, their limitations are behavioral disadvantages, and have not changed much since the 1980s and 1990s, he said. It is possible to limit the availability, accuracy and amount of data, and the frequency at which users can update or update their own templates.
Although scoring programs usually occur once, behavioral outcomes are always focused on events, Humphrey said. “These events often occur when a customer cycles,” he said at the conference. “When you get your opinion and you get the sum and sum up all the events of a month and you have a balance, it is usually at present that the bank revaluation is at risk for the buyer.”
The problem is that behaviors are usually compared to the last monthly balance. “Of course, frustration is that you do not know what’s behind it,” Humphrey said.
Two guests may end up paying the same salary for a month, but have been able to look at the timetable for transactions over the same period of time as the nature of their business has a much greater risk for the bank.
“To get it right, now at ANZ, we get more than 10 million events a day,” he said. “Events that are important to me for modeling, and we have never managed to work with the company.” Over the course of the year, 1.7 billion transactions from the point of view of consumers in terms of small business are only costs and over 300 million transactions. ”
Proof that profound learning has developed over five days and that the concept has been tested demonstrates how this information is used to make risk decisions.
The project evaluates the use of TensorFlow-type neural networks in clusters of computers, the Nvidia DGX-1 platform uses two Intel 20 cores, eight NVIDIA Tesla D100 and 512 GB of RAM.
Food Intake Data using five node processing clusters, each node has eight cores and 48 GB of RAM. The bank uses data from credit cards for 1000 accounts. the rest for the test.
“We have achieved excellent results,” Humphrey said, including an addition to the Gini coefficient used to assess the risk .78 – .82. The test model lasted only 30 minutes and about 200,000 accounts, he said.
“The ability to recover the balance and transactions is very exciting,” Humphrey said.
“The way the results work today is the beginning of a month, we are launching a risk at the end of the month, updated