Is Your AI Project About Customer Experience or About Business Decisions?

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 […]

Algorithms and Regulations: Tips for Success

Cathy O’Neill wrote an interesting piece on regulating automated decision making recently. I am not going to argue about whether use of algorithms should or should not be regulated because I think it is inevitable that they will be. The question is how companies should respond to these regulatory efforts as they are rolled out […]

Using Technology to Reduce Operating Costs in Insurance

In February, we published a blog post on “Using Technology to Add Value in Insurance”. That post, referenced Matt Josefowticz’s article – Technology May be the Answer for Insurers, but What Was the Question?, in which he states that there are only three levers of value in insurance: 1. Sell More, 2. Manage Risk Better, and 3. Cost Less […]

Using Technology to Better Manage Risk in Insurance

In February, we published a blog post on “Using Technology to Add Value in Insurance”. In that post, I referenced Matt Josefowticz’s article – Technology May be the Answer for Insurers, but What Was the Question?, in which he states there are only three levers of value in insurance: Sell More, Manage Risk Better (aka underwriting and adjusting), […]

Using Technology to Grow Relationship Value in Insurance

In February, we published a blog post on “Using Technology to Add Value in Insurance.” In that post, I referenced Matt Josefowticz’s recent article – Technology May be the Answer for Insurers, but What Was the Question?, in which he argues that there are only three levers of value in insurance: 1. Sell More 2. Manage Risk Better (aka […]

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.

Tips for Successful Data Science Implementation in Insurance

Nancy Casbarro and Deb Zawisa of Novarico recently published a new paper on Data Science in Insurance: Expansion and Key Issues subscription required) that was summarized in this nice little article on Dig-in  3 challenges facing insurers in data science implementation. These three challenges – getting business buy in, attracting talent, and strategic alignment are exactly […]

Gartner Analyst’s Take on Decision Management: What It Is and Why You Need It

February 20th, 2019 W. Roy Schulte, Research Vice President and Distinguished Analyst at Gartner gave an interesting webinar titled, “Decision Management: What It Is and Why You Need It.”  Roy kicked off the webinar by stating that “companies make millions of operational decisions every day,” and how well (or not) they make these decisions is […]

From Machine Learning to Business Learning

Machine learning is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on models and inference instead. It is seen as a subset of artificial intelligence. — Wikipedia Machine Learning is increasingly widely used to make predictions. Using machine learning makes it easier and […]

Using Technology to Add Value in Insurance

Matt Josefowticz wrote a great piece recently – Technology May be the Answer for Insurers, but What Was the Question? In this he argues that there are only three levers of value in insurance: 1. Sell More 2. Manage Risk Better (aka underwriting and adjusting) 3. Cost Less to Operate And that while some of these will […]