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Posts Tagged: Machine Learning

COVID-19: Drive the future of work (Part 4)

Following up on part 1, part 2, and part 3, McKinsey's Jump-starting resilient and reimagined operations discussed the impact of COVID-19 on the future of work. We are already seeing radical changes in how people work, especially with remote collaboration and other...

What the Future Holds for Decision Optimization

A Guest Post by Neill Crossley, ACIB James, thank you for the opportunity to guest blog in your series on Decision Optimization. First to introduce myself….. I’m a veteran of 35 years in Retail Financial Services, 25 of which working in Decision Management, the last...

New Analytic Approaches in Decision Optimization

There’s a lot you can do right now to optimize your decisions. You can model your decision-making to understand it better, experiment to gather data about what works and for whom. You can engage in continuous improvement, making small changes regularly. And you can...

Fraud, AI and Digital Decisioning

Automating decisions about transactions lets them be handled in real-time, providing better customer service. If that decision fails to detect fraudulent transactions, this lets fraud into the system. Once a fraudulent transaction has been allowed, you are committed...

Three Steps to Boost Your Deployment Score

Three Steps to Boost Your Deployment Score

As we approach the end of the year, our friends over at the International Institute for Analytics (IIA) are re-tweeting some of their best content. One post in particular caught our eye - What’s Your Deployment Score? This is a great piece by Tom Davenport (who wrote...

Machine Learning, Trust and Stephen Covey

Machine Learning, Trust and Stephen Covey

Trust is a big deal when it comes to machine learning. “Black box” algorithms, concerns about bias and a sense that data scientists may know everything about the data but nothing about the business all undermine trust in machine learning models. Indeed, building...

What is an Acceptable Analytic Failure?

What is an Acceptable Analytic Failure?

Many speakers on predictive analytics, machine learning (ML) and AI talk about the need to allow data science teams to fail. Without failure, without a willingness to fail sometimes, it’s very hard to build a successful data science program. This is true and often a barrier for companies that find it hard to accept that not all analytics initiatives succeed.

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