This is the final entry in a set of three posts – you can see the first on Reducing the cost of manual claims handling here and the second on Fast-tracking disability claims here. Each post is a real-world story that illustrates the importance of handling your claims...
This is the second in a set of three posts – you can see the first on Reducing the cost of manual claims handling here. The series is designed to use specific real-world stories to illustrate the importance of handling your claims effectively, not just efficiently....
Our CTO, Ryan Trollip, recently gave a webinar on claims automation with our partner Red Hat. Many companies spend heavily automating their claims process, but this spend is largely on handling a claim efficiently not on claims handling effectiveness. Digitizing...
COVID-19 accelerated the need for next-generation claims-processing goals. To make these capabilities a reality and create long-term value, insurers should focus on five key areas.
Continuing our series on AI management options (kicked off by the HBR article Managing AI Decision-Making Tools), the next option is Human on the Loop (HOTL). HOTL systems are most common in situations there is a need for 100% straight through or automated processing...
by Charlotte DeKeyrel, Expert Decision Modeler Decision Management Solutions You know who they are—the members of the brain trust in your organizations. They are the seasoned subject matter experts who understand business decision-making and business processes down to...
Some years ago we did some research on the landscape of analytics capabilities. The research looked at the increasingly broad portfolio of analytic capabilities available to enterprises - everything from traditional Business Intelligence (BI) capabilities like...
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...
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...
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...
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...
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...
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...
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.
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...