Many articles and surveys recently have identified the critical role that analytics translators, analytic interpreters or analytic storytellers play. These mediators help analytics teams, machine learning developers, AI engineers and other data scientists bring their results to life. By showing how the model works, showing how model outcomes align with the desired business outcomes, they […]
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 […]
I’m in Asia for two weeks. Last week I had the privilege of catching up with marketing and operations leaders at a major local insurer. We built both a next best offer (NBO) solution and a claims handling solution for them. Both teams are self-sufficient, with multiple business people making changes to the decisioning system. […]
I presented on Backwards Engineering – planning Machine Learning (ML) deployment in reverse. Data shows that a traditional data-first approach to analytics is not generating much value for companies and I urged the audience instead to adopt a decisions-first mindset. The last mile – getting ML models embedded into production systems – is critically important […]
By: Zoe Zhou There is a common high-level customer journey for insurance customers. The customer researches providers and policy options. Once a preferred provider and policy are decided, the customer applies for the policy. If they are approved the policy is issued. If they have a claim, they fill out the relevant paperwork and the claim […]
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 […]
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 […]
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 […]
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), […]
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 […]