IOOF taps AI to perform customer processes 400x faster

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IOOF taps AI to perform customer processes 400x faster

Partners with Deakin Uni.

ASX-listed investment house IOOF has joined forces with Deakin University’s Applied Artificial Intelligence Institute to drastically decrease the time taken to process client correspondence.

The project added natural language processing techniques to IOOF’s in-house client administrative system in a bid to streamline how the company handled the hundreds of email queries flooding into its business centres each day.

Damien O’Donnell, head of platform technology and architecture at IOOF, said the company is undergoing a number of changes to bolster its customer focus, which its annual report said would focus on using technology as a differentiator between incumbent fund managers like itself and emerging competitors.

“Manual work classification of incoming correspondence by a person takes, on average, two minutes per item,” O’Donnell said.

“The AI solution takes around 300 milliseconds per item – so it can perform 400 classifications in that same period.”

Professor Rajesh Vasa, head of translational research and development at the institute, said the new system analyses incoming correspondence and assigns each item to a work category, where support teams can process the item and respond to the customer.

“Work requests come in from many different sources, including email, phone and text message, and staff have the task of manually identifying these for appropriate actioning,” he said.

“The volume of requests and the repetitive nature of the work makes this an ideal job for a machine, but it is critical that the machine can read and understand the content and context of each work request.

“In this project we apply natural language processing (NLP) techniques while retaining and empowering customer service staff to do their jobs better.”

O’Donnell added that, importantly, the AI solution was accurate around 90 percent of the time across some of the company’s highest volume demand areas.

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