CUBBIN: Ultimately due to the emergence of a number of technologies the role of machine learning may change, as the products, use and flow of data also changes.
At the moment though, machine learning is starting to deliver value for us and our customers, with an improvement in processing times of up to 40 per cent, increased consistency of quality, and increased operational efficiencies. And as you said, we are just in pilot stage for blockchain.
GRANT: Yeah, I agree. The problem we have with robots is they aren’t intelligent enough to react to different scenarios unless they are taught how to handle it.
Ultimately the machine is only as smart as the rules that get coded into it and applied to the processing of paper documents, while blockchain provides new opportunities for products – and how they are used to change the world of trade finance.
Without the human, the subject matter expert, to code the rules that guide the actions of the machine, it is of no value to anyone. That’s your quality driver.
And obviously the amount of coding and maintenance on these robots should be kept at check. If there’s code written for basically every scenario, it may soon become counter-productive.
CUBBIN: It’s very similar to humans. You can teach a human the rules and teach them how to do something. Then when you give them the experience, their knowledge and capability increases – and quality and efficiency improves further.
At ANZ, when we started our first machine-learning use case – for sanctions screening – we were getting what’s called ‘capture and accuracy rates’ in the mid, high 80s, in percentage terms. Over time, as we pumped more transactional data through that machine, these metrics increased as the machine ‘learned’. The difference is complimented with human-augmented workflow to ensure overall quality is maintained.
The machine capture and accuracy rate now for us is around 95 per cent. It’s almost perfect and the variance in performance is actually better than a human ‘maker’.
And for all the use cases we’ve implemented, the operational efficiencies are substantial. The machine could achieve operational efficiencies of up to 30 per cent in some cases, if not higher.
But we’re not just doing it for that purpose. There are also several customer benefits from deploying machine learning, including from more consistent quality, as well as faster processing times. Additionally, the operational risk is also reduced and our ability to capture and use data is also enhanced through the automation.
Ultimately when we deploy all our use cases, customers will see an increasing improvement in turnaround times, allowing us to enhance our service proposition and gain a competitive advantage.
And our teams at ANZ are also embracing the use of new technologies, seeing the benefits and becoming ‘champions’ in identifying, and helping to develop, and deploy new machine learning use cases.
GRANT: I think that what’s important to point out is that ANZ’s progress around this space is right at the very front of the global queue in certain cases. I think that would be fair to say.
But there are plenty of other banks who are working on this. Ours is probably still immature in the grand scheme of things but more mature than many others, including banks which are significantly larger trade banks than we are.
Shane White is content manager & Arun Kayal is AD, Communications, Institutional at ANZ
This article was originally published on ANZ’s Institutional website
Credit: Google News
