| |||||||||||
Predictive Analytics World Government 2011 : Predictive Analytics World for Government | |||||||||||
Link: http://bit.ly/mAtZk4 | |||||||||||
| |||||||||||
Call For Papers | |||||||||||
PAW-GOV is for government executives, program managers, financial and HR managers, IT professionals, law enforcement, and analytics professionals, covering today's deployment of predictive analytics and data mining across government agencies and across software vendors. The conference delivers case studies, expertise and resources, empowering Federal, State, and Local government to achieve these objectives:
Drive Smarter Decisions from Data: Government agencies overwhelmed with vast quantities of data transform this challenge to an asset, employing predictive analytics to discover relationships and patterns hidden to the human eye that serve as actionable insights to drive smarter decisions. Reduce Fraud, Waste, and Abuse: Recover and prevent improper payments using predictive analytics to score potential payments, claims and benefits for errors, fraud, waste, and abuse. Automate Manual Processes: Employ analytics of both structured and unstructured data (text analytics) in order to streamline approvals of claims and benefits, and find documents of interest (E-discovery). Prioritize Resources and Maximize Productivity: Use predictive analytics to score cases where there are an overwhelming number to quickly process, search, or audit – including payments, hotline tips and complaints, applicants for benefits, cargo shipments, products pending approval or patents, and others – ranking them so that managers, investigators, and auditors are more productive and efficient, spending their time on the most valuable cases. PAW-GOV showcases case studies from application areas of predictive analytics in the Public Sector including: Improper Payment Recovery & Prevention Fraud Detection Credit Scoring / Predicting Default Operational Analytics Automating Claims Processing Tax and Revenue Collection Text Mining / Unstructured Data Cyber Security Crime Prediction Workforce Analytics Submit your speaker proposal and learn more: http://bit.ly/mAtZk4 |
|