Decision CAMP 2013 Speakers & Talks
Insurance fraud is a growing problem in Asia and the majority of fraud detection systems have relied on statistics and analytics which require a sufficient quantity of fraud data to be effective. Small and medium-sized insurance companies, and many large insurance companies in Asia lack this data, and some attempts and deploying these systems have met with resistance from the business-side since they could not understand the output from these systems.
InsuPector is a next-generation insurance fraud detection system design to address these issues. Rather than relying solely on analytics and the availability of sufficient fraud data, InsuPector, built on Sparkling Logic SMARTS Decision Management System, combines analytics with over 500 fraud patterns that can be extended to meet the company's specific needs and refined and improved through a built-in performance monitoring feedback loop. InsuPector was designed to allow claims experts to customize and extend the business rules so that new products and fraud cases can be easily added.
In this session you will learn:
Talk on Wednesday in the Financial Services track
Decision modeling practitioners frequently face situations when a decision model has to analyze multiple alternative decisions to come up with an optimal one. Traditional decision modeling approaches mainly rely on business rules and/or scorecards to determine one “good” decision and in most cases do not backtrack to consider potentially better decisions. The reason is simple: even for relatively small decision models with 20-40 decision variables it is physically impossible to manually create rules that cover all (!) possible combinations of decision variables and to find a decision that minimizes a total cost or maximizes resource utilization.
In this presentation we describe how “Decision Optimization” utilizes off-the-shelf optimization tools to help a business user to create smarter decision models that are capable to find decisions that minimize/maximize certain optimization objectives. Instead of trying to specify all possible rules, a decision model creator uses decision tables to represent only major business constraints and relationships between different decision variables. An optimization objective may be represented as a special decision variable defined on other key variables. A decision optimization component does the rest of work by automatically considering multiple alternatives and selecting the best one within a time limit defined by a user. Contrary to traditional rule engines, the optimization component is specifically designed to solve constraint satisfaction and optimization problems using proven optimization techniques such as constraint and/or linear programming. We will demonstrate decision optimization at work using two real-world use cases.
Talk on Tuesday
Talk on Monday
Analytics is a broad subject area, but analytics in decision management has traditionally been a slow, artisanal process to create artifacts useful to rules engines at runtime. Those artifacts were created using techniques of data mining, predictive analysis and sometimes, optimization algorithms. But with the emergence of massively distributed processing grids, unstructured data and Hadoop, analytics has become something new - data science.
In this talk we'll discuss what the nature of data science and data scientists is, and apply some perspective on how it should and ultimately will affect your decision processes, both manual and automated.
Great strides have been made informing decisions with analytical processes. However, the challenge of analytics is communication and creating a shared understanding. It’s about focusing on high impact areas, moving forward one step at a time, being skeptical, being creative, searching for the truth.
Any company can compete on analytics.
Talk on Monday
Neil will moderate the CTO Panel:
Talk on Tuesday at 9:30am