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 17 focused on Decision Optimization, I currently work as a […]
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 optimize your decisions mathematically. But what will you be able to […]
Neal Silbert of DataRobot had an interesting post on the DataRobot blog last week – Outcome-Based Claims Assignment: The “New Grail” of Insurance. He talks about using ML and AI to improve the assignment of claims adjusters to commercial claims. As he says “why make exceptions [to ensure the best assignment] when you can make […]
The International Institute for Analytics (I’m a faculty member) recently hosted me for a webinar on Digital Decisioning: Driving Business Value from Advanced Analytics, Machine Learning and AI. In the webinar I discussed the opportunities and challenges of Machine Learning (ML) and Artificial Intelligence (AI), introduced Digital Decisioning – it’s value, principles and how to […]
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 to a “pay and chase” approach to eliminating it. This has a poor success rate. Much better […]
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 a foreword for my new book Digital Decisioning: […]
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 machine learning models that can be, will be, trusted is regarded as […]
Many organizations are investing in artificial intelligence (AI) initiatives these days. However, many are also lumping all of the uses of AI into the same program. This is a terrible idea because AI technologies are being used for two very different business outcomes. AI success relies on a clear focus on business outcomes rather than […]
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.