What was the challenge you sought to address with AI?
Nedbank embarked on several AI projects around the same time, all of which sought to improve core operational processes within our businesses. We had tried traditional approaches to these problems in the past, but given the complexity of the data behind them, these attempts were either unsuccessful or they had reached their ceiling in terms of optimization. AI has really given us the tools to break through that ceiling, enabling us to disrupt the ways we work and, at the end of the day, delight our customers.

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How are you using Watson at Nedbank?
Traditionally, Nedbank has reduced rates of online fraud by using rule-based decision systems. Every time a fraud was committed, new rules were added. Unfortunately, this created a large catch-all problem where responders would see many false alarms, which would encumber resources and divert attention away from actual fraud. By using machine learning, developed within the Watson Local development environment, we were able to drop the false positive rate from 60–80% to below 35%. Watson helped with easy collaboration for model development and simplified the deployment process by letting us build a production-ready API with ease.

What are some of the most important things you’ve learned about using AI
While you can go deep with AI quickly, it’s important to bring the people who will be affected along for the ride. It’s easy to want to land a new disruptive technology, expecting that people will immediately see the benefits and run towards the solution, but old habits are hard to break. It’s important that stakeholders are involved at all points in the journey.

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