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Cracking Machine Learning to speed up client service

Cracking Machine Learning to speed up client service

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Machine Learning has helped Nedbank Insurance speed up its client service

Nedbank Insurance, part of the Nedbank Group that employs over 31,000 people across South Africa, has utilised Machine Learning and Design Thinking to help speed up how it processes emails.

Challenge

Today, every business has an email address. And if you’re a financial services organisation with many business sections and over a million clients, that channel can get crowded.

Nedbank Insurance covers clients’ life and other insurance needs and receives thousands of emails about claims, policies, address changes, complaints, queries and potential new clients. Some emails are long; others are short. Many have attachments that must be categorised correctly to simplify processing for back-end teams. There are also unpredictable peaks when for example heavy storms cause inboxes to be bombarded.

Indranil Bandyopadhyay, Head of Business IT Enablement for Nedbank Insurance, suspected that an automated system could solve the problem and increase operational efficiency and client satisfaction.

“With the old system, a single person can, on average, process one email every 60 to 300 seconds (three minutes on average),” he said.

“That’s about 160 emails per dedicated resource a day. With email volumes growing, our backlogs were mounting, and we didn’t want this to influence our client service. We knew technology could help us create a more sustainable solution, but we needed the right partner.

“Nedbank Insurance had to try a couple of times to get this right. The challenge was that they [other companies] didn’t try to solve the real problem and looked towards their overseas counterparts to solve for this problem. Some of them went half-way and gave up.

“I also engaged with international vendors who mentioned they couldn’t solve the end to end problem. This made me feel we are really trying this for the first time in the world. I did not give up because I believed this could be solved.

“Grit and tenacity along with the right partner were the reasons we were successful with Synthesis.”

Solution

Following various methods to analyse email and attachment data, Synthesis built predictive Machine Learning (ML) models with natural language processing algorithms to understand the intent of the email. The models first learnt from the employees at a rapid pace, refining the process through feedback. Then, when the models performed reliably in terms of predicting where emails should go, they were routed automatically.

“I have been thinking about ML for the past three years,” said Bandyopadhyay.

“The benefits are huge. Especially in an admin orientated industry like Insurance.

“While RPA can deal with repetitive stuff, ML can be used where cognitive ability is needed to complete a task. The main challenge of ML and automation in general is the introduction of ‘positive feedback loop’. This has a leg for a process to go rogue and can have huge negative impact in a very short period of time.

“Sometimes, the amount of data to teach the ML algorithms is also huge challenge and the quality of data is also a major constraint.

“But what struck me is that Synthesis used Design Thinking to truly understand what the problem was. In a very short time they created a prototype, tested it with users and then adapted it. The solution they created revolved around genuine client satisfaction and not just completing a task, and that worked for us.

“Design Thinking assists in understanding the real problem and not the superficial issue. After asking 10 whys, one gets to the core of the problem.

“The issue for us was to find a solution to deal with a huge number of incoming emails. Our emails were in the Microsoft Inbox and the attachments needed downloaded on to a local PC.

“Once a human being understood what the emails were all about, they needed to go to our policy admin system and then categorised with the documents attached the documents.

“While we thought about using a ML/AI solution, Synthesis was quick to understand the real problem via DT. They quickly created a front end that pulled emails and attachments and, once the human being understood the content of the email, the front end automatically attached the document in the right category.

“This immediately gave 80% efficiency though no ML was used at this stage. This was a simple yet powerful solution that the users started using and got immediate benefit. This assisted in change management as well. At a later stage, we used an AI model to make sense of the emails and attachments and took the human element out of the value chain.”

Results

The new system has significantly reduced email processing time, on average from 720 seconds to 28 seconds.

“Clients’ experience will change immensely because now we can work on whatever our clients request immediately,” said Bandyopadhyay.

“During a peak, the machine can handle the volume of requests and now nothing hinders us from responding to client needs. This will greatly improve client experience and will support our commitment to serving clients.

“I am not surprised by how successful the solution has been because I slept, ate and drank this dream and was confident that this problem could be cracked. However, I was surprised to see the success rate from day one we went to production.

“Internally, there has been a very positive response. This solution is now showcased within the group and there are many interested parties that would like to use such a solution in this business.

In turn, Synthesis thinks Nedbank Insurance deserves recognition for its persistence.

“This is what it takes to pioneer advancements,” said Marais Neethling, Synthesis AI Evangelist.

“It will be great to support Nedbank Insurance as it continues its transformation journey and client satisfaction drive.

‘We have significant experience in using emerging technologies to help organisations transform, but we’ve found there’s something more important than technology – applying Design Thinking.

“We try to define an end-to-end system that is practical and includes all supporting processes.

“The challenge was interesting, and we had to work closely with stakeholders and create a plan with the Nedbank tech team.”

Nedbank Insurance now wants to leverage the solution across the business.

“Any electronic interaction is the next step,” said Bandyopadhyay.

‘All we need to do when someone calls in is to convert the voice into text – and route the call correctly. Artificial Intelligence and Machine Learning are the ideal ways for us to tap into the digital world truly to meet our clients’ needs. We are excited to delve further into the possibilities of this technology.”

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