New Challenge “Recreational Fee”

We’ve just published a simple challenge offered by Ron Ross. A city has created a decision table to determine appropriate usage fees for its recreational facilities based on length of usage and when the usage occurs. The city also has the following behavioral business rule: A senior citizen must not be charged a recreational fee for use of facilities. Send us your models of this problem and we will ask Ron to compare different solutions. Link

Posted in Business Rules, Challenges, Decision Modeling | 2 Comments

Alexa for Insurers

Alexa, Amazon’s virtual assistant that powers Amazon’s Echo, is a tool insurance companies are using to leverage voice recognition and increase their value to their customers. Liberty Mutual offers its customers the option to use Alexa to get an auto insurance quote estimate through voice interaction with Liberty Mutual’s Guestimator tool. It also offers actionable advice on common home and auto queries. Insurance carrier Aviva uses Alexa to answer questions about insurance and regards Alexa as the future of their customer interactions. Safeco introduced an insurance advisor skill for Alexa, allowing customers to simply ask Alexa around 100 common customer questions about insurance policies. Safeco is also looking to offer customized insurance products directly via Alexa. Source: Insurers Can Benefit from Natural Language Processing produced by SAPIENS.

Posted in Human-Machine Interaction, Insurance Industry, Natural Language Processing | Leave a comment

Grammarly for Developers?

AI tools for code review and bug patching are getting better and better. A Zurich-based company DeepCode claims that they developed a “Grammarly for developers”, a tool that looks a lot like familiar code vetting apps in use today, and is intended to mesh easily into developer’s “normal, everyday workflow”. Their website shows examples of Java code with automatically generated suggestions for its improvement. An internal analysis shows that DeepCode catches four times more errors than any other tool they tested, without a notably high false-positive rate. Link

Posted in Artificial Intelligence, Java, Trends | Leave a comment

Explaining Decision Optimization Recommendations

Explainability is a hot topic. Decision models are used to trigger recommendations such as “accept” or “refuse” a loan, but they also need to explain WHY they recommended certain decisions. Alain Chabrier from IBM DecisionOptimization team uses Portfolion Allocation problem to demonstrate how explainability works with Decision Optimization models. Link

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Dynamic Questionnaires from Sparkling Logic

Sparkling Logic introduces Dynamic Questionnaires (DQs) for web applications that collect information for making near real-time decisions based on user responses and business policies. Learn more about the latest Sparkling Logic’s release and register for the upcoming webinar.

Posted in Human-Machine Interaction, Products | 1 Comment

MISMO® Recommends DMN™ Standard

MISMO, the mortgage industry’s standards organization, recommended the use of the Decision Model and Notation (DMN) standard for documentation, implementation, execution and exchange of business rules and decisions across the mortgage industry. Link

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Modeling and Solving Scheduling Problems with CP Optimizer

The Resource-Constrained Project Scheduling Problem (RCPSP) is a classical problem among the myriad scheduling problems studied both in academia and in industry. Philippe Laborie, a principal scientist at IBM, describes how it can easily be modeled and efficiently solved using the CP Optimizer engine of IBM ILOG CPLEX Optimization Studio. Link

Posted in Optimization, Scheduling and Resource Allocation | Leave a comment