Delivering boring AI with Decision Management

A great article appeared in Information Age recently based on an interview with Tom Davenport If you want to see the benefits of AI, forget moonshots and think boring. In it, Tom argues that “if enterprises ever want to see the benefits of AI, they must embrace the mundane”. This is particularly true for companies […]

Increasing Productivity in Insurance Operations with Digital Decisioning

Wednesday, September 25, 2019 10:00:00 AM PDT – 11:00:00 AM PDT McKinsey recently reported that “Most carriers are struggling to meet their cost of capital, and productivity has barely moved over the past decade” and that “The insurance industry is facing a serious structural challenge … the majority of carriers are not making their cost […]

Decision Modeling for Analytics Translators/Interpreters/Storytellers

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 […]

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 machine learning models that can be, will be, trusted is regarded as […]

James’ Notes from the Field

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. […]

Predictive Analytics World 2019 – What I Learned and What I Said

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 […]

Innovating the Insurance Customer Experience

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 […]

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 […]