Session 42 PD, Predictive Analytics for Actuaries: Building an Effective Predictive Analytics Team. Moderator: Courtney Nashan

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Session 42 PD, Predictive Analytics for Actuaries: Building an Effective Predictive Analytics Team. Moderator: Courtney Nashan"

Transcription

1 Session 42 PD, Predictive Analytics for Actuaries: Building an Effective Predictive Analytics Team Moderator: Courtney Nashan Presenters: Ian G. Duncan, FSA, FCIA, FIA, MAAA Andy Ferris, FSA, MAAA Christine Irene Hofbeck, FSA, MAAA Courtney Nashan

2 Building an Effective Predictive Modeling Team Christine Hofbeck, FSA, MAAA Ian Duncan, FSA, FIA, FCIA, FCA, MAAA Andy Ferris, FSA, FCA, CFA, MAAA Courtney Nashan October 12, 2015

3 Overall Approach To comprehensively incorporate predictive analytics into a core operational business process, we follow four phases: 1. Phase 1 Planning 2. Phase 2 - Data Assembly and Model Build 3. Phase 3 - Technical Implementation 4. Phase 4 - Business Implementation 2

4 Phase 1 3

5 Phase 1 - Planning 1. Assembling a Team 2. Laying the Foundation 3. Selecting a Project 4

6 Phase 1 - Assembling a Team Consider skillsets both individually and collectively 1. Ability to manipulate large datasets (SAS, R, SQL) 2. Modeling expertise 3. Business acumen 4. Ability to explain highly technical information to a nontechnical audience 5. Ability to represent results graphically for ease of communication 6. Consider mix of prior experience 7. Charisma Those who prepare the data are as important as those who build the model, who are as important as your business partners who provide subject matter expertise. 5

7 Phase 1 - Laying the Foundation Building a predictive modeling capability is not only about hiring a team. Consider: 1. Technology 2. Legal commitments to customers 3. Data privacy and compliance 4. Objective 5. Change management 6. Cross functional support 7. Budget Consider the cultural and political impacts of this change, not only the strategic. 6

8 Phase 1 - Selecting a Project Your first project will get a lot of attention select it wisely 1. Large enough that it can make a true business impact 2. Not so large that it takes over a year or more to build (your colleagues will be anxious to see results!) 3. Available data 4. Projects which may have been unsolvable in the past with current methods 5. The business wants to implement (use) it to improve decision making 6. What are my competitors doing? Where should I invest the effort? Remember that predictive modeling makes an impact when the model is implemented and better informed decisions are made 7

9 Phase 2 8

10 Phase 2 Data Assembly & Model Build There are two important challenges to keep in mind with modeling: 1. How to organize the data for efficient interrogation; and 2. How to organize the data for replicability (remember that at some point, your model is going to go into production). 9

11 Phase 2 Data Assembly & Model Build How to organize the data for efficient interrogation Here is an example of a data management and warehousing problem from healthcare: We know that diagnoses are an important contributing factor to illness, health risk and cost. There are about 17,000 diagnosis codes currently in use (ICD-9). With ICD-10 this number grows to 140,000 (from October 2015!) There are 100,000 CPT (procedure) codes, and the National Drug Code directory contains hundreds of thousands of drug codes (updated daily!) Obviously this creates an unmanageable set of codes for analysis purposes. In healthcare we have solved this problem with the use of grouper models. Grouper models group like diagnosis codes into diagnostic categories. Drug codes are similarly grouped into therapeutic classes. For a lot of analytical work, grouper models are all that is required. The SOA has studied the predictive accuracy of these models in three studies ( ); a fourth study is in preparation. 10

12 Phase 2 Data Assembly & Model Build How to organize the data for replicability The use of grouper-type models or models that assign a categorical value to a continuous variable is very valuable in modeling because these models can be built into a warehousing process. They will then be used in the practical application of the model in production. Another example from Healthcare: Body Mass Index is defined as Weight (in kg)/height 2 (in cm). Obviously, a continuous variable. But clinicians have provided categories, as follows, which provide a useful guide to the status of a particular patient: Category BMI Underweight < 18.0 Normal weight Heavy weight Obese Morbidly Obese

13 Phase 2 Data Assembly & Model Build A few quotes to keep us grounded: The year 1930, as a whole, should prove at least a fairly good year. -- Harvard Economic Service, December 1929 All models are wrong but some are useful. George E.P. Box, Professor Statistics, University of Wisconsin-Madison. 12

14 Phase 2 Data Assembly & Model Build Frequently-used software: SAS R Internally developed software Other commercially available models Not as popular: Python, SPSS, Salford Systems From SOA Sections Survey: Predictive Analytics

15 Phase 2 Data Assembly & Model Build Frequently-used models: OLS Regression GLM Time series Decision Trees Clustering Not as popular: Neural network, Bayes. From SOA Sections Survey: Predictive Analytics

16 Phase 3 15

17 Phase 3 Technical Implementation At this point in the overall approach What we have accomplished: We have a mathematical equation: What we have not accomplished: No real time scoring engine to enable use of the equation Objective of this phase: A real-time flow of data inputs from multiple internal and external sources to the scoring engine A real-time flow of model output ( score, reason codes, etc.) to business unit operations 16

18 Phase 3 Technical Implementation Common Challenges of this phase Lack of early engagement of IT staff in planning Lack of sufficient dedicated IT resources Format of data received (scanned images, etc.) in current environment Collecting data fields in real time business production from multiple internal systems (administrative system, agent licensing system, illustration, etc.) Sensitive data fields that prior phase found to be predictive Fixed system release dates conflict with desired program rollout 17

19 Phase 3 Technical Implementation Hints in overcoming common challenges Engage IT resources early in the project Plan in advance to discover more data challenges than you initially expect Avoid reputational risk by carefully considering how each data field will be used in new business process Consider temporarily outsourcing the scoring engine if needed 18

20 Phase 4 19

21 Phase 4 Business Implementation At this point in the overall approach What we have accomplished: We can deliver model output in real time to a business unit What we have not accomplished: Not changed any core business operations to take advantage of the model output Objective of this phase: Classic business process change exercise Change an existing business process to save time, save money, be more efficient, etc. 20

22 Phase 4 Business Implementation Common Challenges of this phase Lack of Early Engagement - by business unit in how algorithm will be used; how/why business process will change Lack of Sufficient Communication - with business stakeholders (other departments, customers, producers) on changes in operational procedures Unrealistic Expectations - by business stakeholders in impact of predictive modeling and associated changes to business processes Reputation Risk Are you comfortable explaining on 60 Minutes data sources used by your business process in making decisions on individual customers? Implementing tools and metrics to monitor the ongoing impact of the new business process Of all four phases, the business implementation phase is consistently the most challenging for most organizations. 21

23 Phase 4 Business Implementation Hints in overcoming common challenges Engage business unit early to ensure large model development effort will be deployed in tangible business process change Design change management plan, including any impacts to operating model, org design, as well as communications plan for program rollout Manage expectations to communicate what the new process will NOT do Carefully consider how any new data sources may be perceived as sensitive in future state business process Implement tools and metrics to monitor the ongoing impact of the algorithm on the business process As previously mentioned, predictive modeling makes a business impact only when the model is implemented and more informed decisions are made. 22

24 SOA Support of Members, Candidate and Students in Predictive Analytics 23

25 Expanding Opportunities for Actuaries Cultivate opportunities for SOA members in relevant fields for actuaries through: Identifying the opportunities Building relationships with decision makers Marketing and publicizing the skills of actuaries in new roles with traditional employers and new industries Informing the membership and share pioneer stories

26 Predictive Analytics Focus Growth and timing With proliferation of big data, use of analytics is growing Opportunity to expand roles for actuaries in predictive analytics Need to mobilize quickly or actuaries will not be considered for these roles

27 Strategic Direction Strategy Generate supply of trained actuaries Initiate multi-phase marketing communications campaign to generate demand, interest in members, candidates, and employers Tactics ASA Education FSA Education Professional Development Research Sections Marketing

28 Q&A

2012 3 R s and Predictive Modeling Boot Camp Nov. 8-9, 2012. Session #1: Predictive Modeling: An Overview Syed Muzayan Mehmud, ASA, FCA, MAAA

2012 3 R s and Predictive Modeling Boot Camp Nov. 8-9, 2012. Session #1: Predictive Modeling: An Overview Syed Muzayan Mehmud, ASA, FCA, MAAA 2012 3 R s and Predictive Modeling Boot Camp Nov. 8-9, 2012 Session #1: Predictive Modeling: An Overview Syed Muzayan Mehmud, ASA, FCA, MAAA Predictive Modeling: An Overview November 8, 2012 Syed M. Mehmud

More information

Session 121 PD, Medicare Advantage Risk Score Basics. Moderator: Christine Sue Bach, ASA, FCA, MAAA

Session 121 PD, Medicare Advantage Risk Score Basics. Moderator: Christine Sue Bach, ASA, FCA, MAAA Session 121 PD, Medicare Advantage Risk Score Basics Moderator: Christine Sue Bach, ASA, FCA, MAAA Presenters: Christine Sue Bach, ASA, FCA, MAAA Gregory Joseph Herrle, FSA, MAAA 2015 SOA Annual Meeting

More information

KnowledgeSTUDIO HIGH-PERFORMANCE PREDICTIVE ANALYTICS USING ADVANCED MODELING TECHNIQUES

KnowledgeSTUDIO HIGH-PERFORMANCE PREDICTIVE ANALYTICS USING ADVANCED MODELING TECHNIQUES HIGH-PERFORMANCE PREDICTIVE ANALYTICS USING ADVANCED MODELING TECHNIQUES Translating data into business value requires the right data mining and modeling techniques which uncover important patterns within

More information

In this presentation, you will be introduced to data mining and the relationship with meaningful use.

In this presentation, you will be introduced to data mining and the relationship with meaningful use. In this presentation, you will be introduced to data mining and the relationship with meaningful use. Data mining refers to the art and science of intelligent data analysis. It is the application of machine

More information

hmetrix Revolutionizing Healthcare Analytics with Vertica & Tableau

hmetrix Revolutionizing Healthcare Analytics with Vertica & Tableau Powered by Vertica Solution Series in conjunction with: hmetrix Revolutionizing Healthcare Analytics with Vertica & Tableau The cost of healthcare in the US continues to escalate. Consumers, employers,

More information

96 PD Predictive Modeling: Now What? Moderator: Kara L. Clark, FSA, MAAA

96 PD Predictive Modeling: Now What? Moderator: Kara L. Clark, FSA, MAAA 96 PD Predictive Modeling: Now What? Moderator: Kara L. Clark, FSA, MAAA Presenters: Philip Fiero Syed Muzayan Mehmud, ASA, FCA, MAAA Prashant Ratnakar Nayak, ASA, MAAA TM Advanced Predictive Modelling

More information

SOA 2013 Life & Annuity Symposium May 6-7, 2013. Session 30 PD, Predictive Modeling Applications for Life and Annuity Pricing and Underwriting

SOA 2013 Life & Annuity Symposium May 6-7, 2013. Session 30 PD, Predictive Modeling Applications for Life and Annuity Pricing and Underwriting SOA 2013 Life & Annuity Symposium May 6-7, 2013 Session 30 PD, Predictive Modeling Applications for Life and Annuity Pricing and Underwriting Moderator: Barry D. Senensky, FSA, FCIA, MAAA Presenters: Jonathan

More information

Session 61 L, Applications of Data Analytics in Health Insurance. Moderator/Presenter: Henning Chiv, FSA, MAAA

Session 61 L, Applications of Data Analytics in Health Insurance. Moderator/Presenter: Henning Chiv, FSA, MAAA Session 61 L, Applications of Data Analytics in Health Insurance Moderator/Presenter: Henning Chiv, FSA, MAAA Session 61: Applications of Data Analytics in Health Insurance Henning Chiv, FSA, MAAA June

More information

Predictive Modeling and Big Data

Predictive Modeling and Big Data Predictive Modeling and Presented by Eileen Burns, FSA, MAAA Milliman Agenda Current uses of predictive modeling in the life insurance industry Potential applications of 2 1 June 16, 2014 [Enter presentation

More information

SOA 10 Health Meeting June 28-30, 2010. Session # 26 PD: The Use of Electronic Health Information in Actuarial Practice

SOA 10 Health Meeting June 28-30, 2010. Session # 26 PD: The Use of Electronic Health Information in Actuarial Practice SOA 10 Health Meeting June 28-30, 2010 Session # 26 PD: The Use of Electronic Health Information in Actuarial Practice David V. Axene, FSA, MAAA, FCA, CERA Radovan Bursac, ASA, MAAA Robert Plesha, ASA,

More information

The Data Mining Process

The Data Mining Process Sequence for Determining Necessary Data. Wrong: Catalog everything you have, and decide what data is important. Right: Work backward from the solution, define the problem explicitly, and map out the data

More information

White Paper. Version 1.2 May 2015 RAID Incorporated

White Paper. Version 1.2 May 2015 RAID Incorporated White Paper Version 1.2 May 2015 RAID Incorporated Introduction The abundance of Big Data, structured, partially-structured and unstructured massive datasets, which are too large to be processed effectively

More information

KnowledgeSEEKER POWERFUL SEGMENTATION, STRATEGY DESIGN AND VISUALIZATION SOFTWARE

KnowledgeSEEKER POWERFUL SEGMENTATION, STRATEGY DESIGN AND VISUALIZATION SOFTWARE POWERFUL SEGMENTATION, STRATEGY DESIGN AND VISUALIZATION SOFTWARE Most Effective Modeling Application Designed to Address Business Challenges Applying a predictive strategy to reach a desired business

More information

Predictive analytics. The rise and value of predictive analytics in enterprise decision making

Predictive analytics. The rise and value of predictive analytics in enterprise decision making WHITE PAPER Predictive analytics The rise and value of predictive analytics in enterprise decision making Give me a long enough lever and a place to stand, and I can move the Earth. Archimedes, 250 B.C.

More information

Session 11 PD, Provider Perspectives of Values Based Payment Programs. Moderator: William T. O'Brien, FSA, FCA

Session 11 PD, Provider Perspectives of Values Based Payment Programs. Moderator: William T. O'Brien, FSA, FCA Session 11 PD, Provider Perspectives of Values Based Payment Programs Moderator: William T. O'Brien, FSA, FCA Presenters: Donald Fry, M.D. Lillian Louise Dittrick, FSA, MAAA Colleen Audrey Norris, ASA,

More information

Big Data and Data Science: Behind the Buzz Words

Big Data and Data Science: Behind the Buzz Words Big Data and Data Science: Behind the Buzz Words Peggy Brinkmann, FCAS, MAAA Actuary Milliman, Inc. April 1, 2014 Contents Big data: from hype to value Deconstructing data science Managing big data Analyzing

More information

Master of Science in Healthcare Informatics and Analytics Program Overview

Master of Science in Healthcare Informatics and Analytics Program Overview Master of Science in Healthcare Informatics and Analytics Program Overview The program is a 60 credit, 100 week course of study that is designed to graduate students who: Understand and can apply the appropriate

More information

Data Science and Business Analytics Certificate Data Science and Business Intelligence Certificate

Data Science and Business Analytics Certificate Data Science and Business Intelligence Certificate Data Science and Business Analytics Certificate Data Science and Business Intelligence Certificate Description The Helzberg School of Management has launched two graduate-level certificates: one in Data

More information

KnowledgeSEEKER Marketing Edition

KnowledgeSEEKER Marketing Edition KnowledgeSEEKER Marketing Edition Predictive Analytics for Marketing The Easiest to Use Marketing Analytics Tool KnowledgeSEEKER Marketing Edition is a predictive analytics tool designed for marketers

More information

Please include the job reference quoted (and for LinkedIn adverts refer to the employer job ID) in the email subject heading and cover letter.

Please include the job reference quoted (and for LinkedIn adverts refer to the employer job ID) in the email subject heading and cover letter. IBM Leicester Service Centre (ISC) The IBM Services Centre (ISC) Leicester, is a wholly owned and new subsidiary of IBM and is the first of its kind in the UK. There are three other ISCs across Europe.

More information

QUALITY CLINICAL PRACTICE DATA ANALYST SERIES

QUALITY CLINICAL PRACTICE DATA ANALYST SERIES QUALITY CLINICAL PRACTICE DATA ANALYST SERIES Code No. Class Title Area Area Period Date Action 4966 Clinical Practice Data Analyst 03 441 6 mo. 11/15/13 New 4967 Clinical Practice Data Analyst Specialist

More information

Using reporting and data mining techniques to improve knowledge of subscribers; applications to customer profiling and fraud management

Using reporting and data mining techniques to improve knowledge of subscribers; applications to customer profiling and fraud management Using reporting and data mining techniques to improve knowledge of subscribers; applications to customer profiling and fraud management Paper Jean-Louis Amat Abstract One of the main issues of operators

More information

How to Get More Value from Your Survey Data

How to Get More Value from Your Survey Data Technical report How to Get More Value from Your Survey Data Discover four advanced analysis techniques that make survey research more effective Table of contents Introduction..............................................................2

More information

Best Practices in Data Mining. Executive Summary

Best Practices in Data Mining. Executive Summary Executive Summary Prepared by: Database & Marketing Technology Council Authors: Richard Boire, Paul Tyndall, Greg Carriere, Rob Champion Released: August 2003 Executive Summary Canadian marketers have

More information

Center for Healthcare Transparency

Center for Healthcare Transparency RFP Contents I. Project Description and Background II. Funding Available III. Proposal Requirements IV. Proposal Scoring V. Proposal Submission Process VI. Proposal Documents I. Project Description and

More information

Data Mining Applications in Higher Education

Data Mining Applications in Higher Education Executive report Data Mining Applications in Higher Education Jing Luan, PhD Chief Planning and Research Officer, Cabrillo College Founder, Knowledge Discovery Laboratories Table of contents Introduction..............................................................2

More information

Article from: Health Watch. October 2013 Issue 73

Article from: Health Watch. October 2013 Issue 73 Article from: Health Watch October 2013 Issue 73 Nontraditional Variables in Health Care Risk Adjustment By Syed M. Mehmud Syed M. Mehmud, ASA, MAAA, FCA, is director and senior consulting actuary at Wakely

More information

Explore the Possibilities

Explore the Possibilities Explore the Possibilities 2013 HR Service Delivery Forum Got Predictive Analytics? 2013 Towers Watson. All rights reserved. Reporting and analytics progress continuum current state of market Late Bloomers

More information

COMPARISON ANALYSIS IMPLICATIONS REPORT OF EMPLOYER AND MEMBER RESEARCH

COMPARISON ANALYSIS IMPLICATIONS REPORT OF EMPLOYER AND MEMBER RESEARCH COMPARISON ANALYSIS IMPLICATIONS REPORT OF EMPLOYER AND MEMBER RESEARCH Prepared For: Society of Actuaries Prepared By: ERIN Research Leading Solutions Group September 2003 CONTENTS 1. Introduction 2 2.

More information

Easily Identify the Right Customers

Easily Identify the Right Customers PASW Direct Marketing 18 Specifications Easily Identify the Right Customers You want your marketing programs to be as profitable as possible, and gaining insight into the information contained in your

More information

Session 35 PD, Predictive Modeling for Actuaries: Integrating Predictive Analytics in Assumption Setting Moderator: David Wang, FSA, FIA, MAAA

Session 35 PD, Predictive Modeling for Actuaries: Integrating Predictive Analytics in Assumption Setting Moderator: David Wang, FSA, FIA, MAAA Session 35 PD, Predictive Modeling for Actuaries: Integrating Predictive Analytics in Assumption Setting Moderator: David Wang, FSA, FIA, MAAA Presenters: Guillaume Briere-Giroux, FSA, MAAA Eileen Sheila

More information

ANALYTICS CENTER LEARNING PROGRAM

ANALYTICS CENTER LEARNING PROGRAM Overview of Curriculum ANALYTICS CENTER LEARNING PROGRAM The following courses are offered by Analytics Center as part of its learning program: Course Duration Prerequisites 1- Math and Theory 101 - Fundamentals

More information

Session 190 PD, Model Risk Management and Controls Moderator: Chad R. Runchey, FSA, MAAA

Session 190 PD, Model Risk Management and Controls Moderator: Chad R. Runchey, FSA, MAAA Session 190 PD, Model Risk Management and Controls Moderator: Chad R. Runchey, FSA, MAAA Presenters: Michael N. Failor, ASA, MAAA Michael A. McDonald, FSA, FCIA Chad R. Runchey, FSA, MAAA SOA 2014 Annual

More information

Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA

Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA ABSTRACT Current trends in data mining allow the business community to take advantage of

More information

Statistical/ IT Skills

Statistical/ IT Skills Statistical/ IT Skills A Data Scientist must have or be able to quickly acquire a detailed knowledge and understanding of Big Data statistical methodology, concepts and research as they apply to the production

More information

Access. Action. Insight. Healthcare Analytics and Marketing Communications Consultative, Analytical, and Promotional Solutions

Access. Action. Insight. Healthcare Analytics and Marketing Communications Consultative, Analytical, and Promotional Solutions Cardinal Health Specialty Solutions Healthcare Analytics and Marketing Communications Consultative, Analytical, and Promotional Solutions Access Action Insight In today s increasingly competitive healthcare

More information

Strategic HR Partner Assessment (SHRPA) Feedback Results

Strategic HR Partner Assessment (SHRPA) Feedback Results Strategic HR Partner Assessment (SHRPA) Feedback Results January 04 Copyright 997-04 Assessment Plus, Inc. Introduction This report is divided into four sections: Part I, The SHRPA TM Model, explains how

More information

Numerical Algorithms Group. Embedded Analytics. A cure for the common code. www.nag.com. Results Matter. Trust NAG.

Numerical Algorithms Group. Embedded Analytics. A cure for the common code. www.nag.com. Results Matter. Trust NAG. Embedded Analytics A cure for the common code www.nag.com Results Matter. Trust NAG. Executive Summary How much information is there in your data? How much is hidden from you, because you don t have access

More information

The Analytical Revolution

The Analytical Revolution Predictive Analytics World 19 October 2011 The Analytical Revolution Colin Shearer Worldwide Industry Solutions Leader SPSS Business Analytics software Our world is becoming smarter Instrumented Interconnected

More information

Nine Common Types of Data Mining Techniques Used in Predictive Analytics

Nine Common Types of Data Mining Techniques Used in Predictive Analytics 1 Nine Common Types of Data Mining Techniques Used in Predictive Analytics By Laura Patterson, President, VisionEdge Marketing Predictive analytics enable you to develop mathematical models to help better

More information

top issues An annual report

top issues An annual report top issues An annual report Volume 6 2014 Strategy: Creating a data science office The insurance industry in 2014 FPO Creating a data science office Most insurers are inundated with data and have difficulty

More information

Building and Deploying Customer Behavior Models

Building and Deploying Customer Behavior Models Building and Deploying Customer Behavior Models February 20, 2014 David Smith, VP Marketing and Community, Revolution Analytics Paul Maiste, President and CEO, Lityx In Today s Webinar About Revolution

More information

Using Your Fundraising Software to Effectively Manage Your Prospects

Using Your Fundraising Software to Effectively Manage Your Prospects Using Your Fundraising Software to Effectively Manage Your Prospects Learning Objectives How do we use our fundraising software to help manage our prospects more effectively? Note that this presentation

More information

Discovering, Not Finding. Practical Data Mining for Practitioners: Level II. Advanced Data Mining for Researchers : Level III

Discovering, Not Finding. Practical Data Mining for Practitioners: Level II. Advanced Data Mining for Researchers : Level III www.cognitro.com/training Predicitve DATA EMPOWERING DECISIONS Data Mining & Predicitve Training (DMPA) is a set of multi-level intensive courses and workshops developed by Cognitro team. it is designed

More information

It s about you What is performance analysis/business intelligence analytics? What is the role of the Performance Analyst?

It s about you What is performance analysis/business intelligence analytics? What is the role of the Performance Analyst? Performance Analyst It s about you Are you able to manipulate large volumes of data and identify the most critical information for decision making? Can you derive future trends from past performance? If

More information

POSITION STATEMENT ACADEMIC STAFF UTS:HUMAN RESOURCES

POSITION STATEMENT ACADEMIC STAFF UTS:HUMAN RESOURCES POSITION STATEMENT ACADEMIC STAFF UTS:HUMAN RESOURCES POSITION: FACULTY: Research Fellow: Data Science Connected Intelligence Centre, Office of the DVC (Education and Students) ACADEMIC SUPERVISOR S NAME:

More information

DATA MINING AND REPORTING IN HEALTHCARE

DATA MINING AND REPORTING IN HEALTHCARE DATA MINING AND REPORTING IN HEALTHCARE Divya Gandhi 1, Pooja Asher 2, Harshada Chaudhari 3 1,2,3 Department of Information Technology, Sardar Patel Institute of Technology, Mumbai,(India) ABSTRACT The

More information

HR STILL GETTING IT WRONG BIG DATA & PREDICTIVE ANALYTICS THE RIGHT WAY

HR STILL GETTING IT WRONG BIG DATA & PREDICTIVE ANALYTICS THE RIGHT WAY HR STILL GETTING IT WRONG BIG DATA & PREDICTIVE ANALYTICS THE RIGHT WAY OVERVIEW Research cited by Forbes estimates that more than half of companies sampled (over 60%) are investing in big data and predictive

More information

KEY ETHICAL CONCERNS FACING THE ACTUARIAL PROFESSION

KEY ETHICAL CONCERNS FACING THE ACTUARIAL PROFESSION COUNCIL ON PROFESSIONALISM KEY ETHICAL CONCERNS FACING THE ACTUARIAL PROFESSION Perceptions of Members of the April 2015 2015. All rights reserved. May not be reproduced without express permission. Table

More information

REUSABLE CONTAINER MANAGEMENT & TRACKING

REUSABLE CONTAINER MANAGEMENT & TRACKING REUSABLE CONTAINER MANAGEMENT & TRACKING Implementing and Proving ROI (Part 2 of the CHEP Asset Tracking Whitepaper Series) As outlined in Part One of this Asset Management & Tracking White Paper Series,

More information

Supply chain intelligence: benefits, techniques and future trends

Supply chain intelligence: benefits, techniques and future trends MEB 2010 8 th International Conference on Management, Enterprise and Benchmarking June 4 5, 2010 Budapest, Hungary Supply chain intelligence: benefits, techniques and future trends Zoltán Bátori Óbuda

More information

Foundations of Business Intelligence: Databases and Information Management

Foundations of Business Intelligence: Databases and Information Management Foundations of Business Intelligence: Databases and Information Management Problem: HP s numerous systems unable to deliver the information needed for a complete picture of business operations, lack of

More information

Predictive Analytics Certificate Program

Predictive Analytics Certificate Program Information Technologies Programs Predictive Analytics Certificate Program Accelerate Your Career Offered in partnership with: University of California, Irvine Extension s professional certificate and

More information

Session 62 TS, Predictive Modeling for Actuaries: Predictive Modeling Techniques in Insurance Moderator: Yonasan Schwartz, FSA, MAAA

Session 62 TS, Predictive Modeling for Actuaries: Predictive Modeling Techniques in Insurance Moderator: Yonasan Schwartz, FSA, MAAA Session 62 TS, Predictive Modeling for Actuaries: Predictive Modeling Techniques in Insurance Moderator: Yonasan Schwartz, FSA, MAAA Presenters: Jean-Frederic Breton David A. Moore, FSA, MAAA Session 62:

More information

Healthcare Measurement Analysis Using Data mining Techniques

Healthcare Measurement Analysis Using Data mining Techniques www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 03 Issue 07 July, 2014 Page No. 7058-7064 Healthcare Measurement Analysis Using Data mining Techniques 1 Dr.A.Shaik

More information

Introduction. Today s World of Sales and the Role of Analytics

Introduction. Today s World of Sales and the Role of Analytics Introduction The smart use of sales analytics and decision frameworks helps ensure that the right sales team is in place and is engaged in the right activities for driving success with customers and delivering

More information

Healthcare data analytics. Da-Wei Wang Institute of Information Science wdw@iis.sinica.edu.tw

Healthcare data analytics. Da-Wei Wang Institute of Information Science wdw@iis.sinica.edu.tw Healthcare data analytics Da-Wei Wang Institute of Information Science wdw@iis.sinica.edu.tw Outline Data Science Enabling technologies Grand goals Issues Google flu trend Privacy Conclusion Analytics

More information

Operationalise Predictive Analytics

Operationalise Predictive Analytics Operationalise Predictive Analytics Publish SPSS, Excel and R reports online Predict online using SPSS and R models Access models and reports via Android app Organise people and content into projects Monitor

More information

Using Evidence-Based Search Marketing to Improve Results and Reduce Costs

Using Evidence-Based Search Marketing to Improve Results and Reduce Costs Using Evidence-Based Search Marketing to Improve Results and Reduce Costs January 2011 Using Evidence-Based Search Marketing to Improve Results and Reduce Costs Introduction The pace of research and innovation

More information

Countdown to Change. DSM-5 and ICD-10. Sharon A. Shover, CPC, CEMC sshover@blueandco.com 502.992.3511

Countdown to Change. DSM-5 and ICD-10. Sharon A. Shover, CPC, CEMC sshover@blueandco.com 502.992.3511 Countdown to Change DSM-5 and ICD-10 Sharon A. Shover, CPC, CEMC sshover@blueandco.com 502.992.3511 Today s Discussion Differences between DSM and ICD Specificity of the code sets Financial Impacts Implementation

More information

Hexaware E-book on Predictive Analytics

Hexaware E-book on Predictive Analytics Hexaware E-book on Predictive Analytics Business Intelligence & Analytics Actionable Intelligence Enabled Published on : Feb 7, 2012 Hexaware E-book on Predictive Analytics What is Data mining? Data mining,

More information

ISSN: 2321-7782 (Online) Volume 3, Issue 4, April 2015 International Journal of Advance Research in Computer Science and Management Studies

ISSN: 2321-7782 (Online) Volume 3, Issue 4, April 2015 International Journal of Advance Research in Computer Science and Management Studies ISSN: 2321-7782 (Online) Volume 3, Issue 4, April 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online

More information

WHITEPAPER. How to Credit Score with Predictive Analytics

WHITEPAPER. How to Credit Score with Predictive Analytics WHITEPAPER How to Credit Score with Predictive Analytics Managing Credit Risk Credit scoring and automated rule-based decisioning are the most important tools used by financial services and credit lending

More information

Health IT Certificate Series Introduction. Health IT Certificate Series - Professional Tracks

Health IT Certificate Series Introduction. Health IT Certificate Series - Professional Tracks Health IT Certificate Series Introduction According to the Bureau of Labor Statistics, there will be a shortfall of Health IT workers through 2015 and beyond. These workers will be in great demand as hospitals,

More information

WEDI ICD-10 Emergency Summit Summary

WEDI ICD-10 Emergency Summit Summary WEDI ICD-10 Emergency Summit Summary This document summarizes key discussion areas from the WEDI Emergency ICD-10 Summit held April 30, 2014 in response to the ICD-10 compliance date change necessitated

More information

Session 15 OF, Unpacking the Actuary's Technical Toolkit. Moderator: Albert Jeffrey Moore, ASA, MAAA

Session 15 OF, Unpacking the Actuary's Technical Toolkit. Moderator: Albert Jeffrey Moore, ASA, MAAA Session 15 OF, Unpacking the Actuary's Technical Toolkit Moderator: Albert Jeffrey Moore, ASA, MAAA Presenters: Melissa Boudreau, FCAS Albert Jeffrey Moore, ASA, MAAA Christopher Kenneth Peek Yonasan Schwartz,

More information

HCC REVENUE CYCLE management Reveal the Overlooked, Omitted and Obscure Capture Full Clinical Support for Requisite Revenue Mitigate Audit Risk medicare advantage revenue cycle management Driver of Plan

More information

Using Data Mining to Detect Insurance Fraud

Using Data Mining to Detect Insurance Fraud IBM SPSS Modeler Using Data Mining to Detect Insurance Fraud Improve accuracy and minimize loss Highlights: combines powerful analytical techniques with existing fraud detection and prevention efforts

More information

Data Mining Algorithms Part 1. Dejan Sarka

Data Mining Algorithms Part 1. Dejan Sarka Data Mining Algorithms Part 1 Dejan Sarka Join the conversation on Twitter: @DevWeek #DW2015 Instructor Bio Dejan Sarka (dsarka@solidq.com) 30 years of experience SQL Server MVP, MCT, 13 books 7+ courses

More information

Data Analytical Framework for Customer Centric Solutions

Data Analytical Framework for Customer Centric Solutions Data Analytical Framework for Customer Centric Solutions Customer Savviness Index Low Medium High Data Management Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics

More information

Predictive Modeling Techniques in Insurance

Predictive Modeling Techniques in Insurance Predictive Modeling Techniques in Insurance Tuesday May 5, 2015 JF. Breton Application Engineer 2014 The MathWorks, Inc. 1 Opening Presenter: JF. Breton: 13 years of experience in predictive analytics

More information

TABLE OF CONTENTS. Company Background... 3. Our Mission... 3. Client Benefits... 3. Our Values... 4. Recruitment Process Outsourcing (RPO)...

TABLE OF CONTENTS. Company Background... 3. Our Mission... 3. Client Benefits... 3. Our Values... 4. Recruitment Process Outsourcing (RPO)... TABLE OF CONTENTS Company Background... 3 Our Mission... 3 Client Benefits... 3 Our Values... 4 Recruitment Process Outsourcing (RPO)... 5 Contract Consulting/Staff Augmentation... 5 Direct Hire/Permanent

More information

In-Database Analytics

In-Database Analytics Embedding Analytics in Decision Management Systems In-database analytics offer a powerful tool for embedding advanced analytics in a critical component of IT infrastructure. James Taylor CEO CONTENTS Introducing

More information

You Must Ask Your Internet Marketing Consultant to Multiply Results

You Must Ask Your Internet Marketing Consultant to Multiply Results The 25 Things You Must Ask Your Internet Marketing Consultant to Multiply Results Learn the Secret Methods of Internet Success from one pioneer who cut his teeth on the internet in the mid-1990 s; and

More information

Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing

Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing Introduction to Data Mining and Machine Learning Techniques Iza Moise, Evangelos Pournaras, Dirk Helbing Iza Moise, Evangelos Pournaras, Dirk Helbing 1 Overview Main principles of data mining Definition

More information

CA Service Desk On-Demand

CA Service Desk On-Demand PRODUCT BRIEF: CA SERVICE DESK ON DEMAND -Demand Demand is a versatile, ready-to-use IT support solution delivered On Demand to help you build a superior Request, Incident, Change and Problem solving system.

More information

Developing an Analytics Strategy that Drives Healthcare Transformation

Developing an Analytics Strategy that Drives Healthcare Transformation Developing an Analytics Strategy that Drives Healthcare Transformation Trevor Strome, MSc, PMP Analytics Lead, Winnipeg Regional Health Authority Emergency Program Assistant Professor, Dept. of Emergency

More information

Evaluating CPSI s Accounts Receivable Management Services In Community Hospitals:

Evaluating CPSI s Accounts Receivable Management Services In Community Hospitals: Evaluating CPSI s Accounts Receivable Management Services In Community Hospitals: Contributors to Success Sponsored by CPSI Reported by Porter Research January 2007 EXECUTIVE SUMMARY Computer Programs

More information

WHITE PAPER. Talend Infosense Solution Brief Master Data Management for Health Care Reference Data

WHITE PAPER. Talend Infosense Solution Brief Master Data Management for Health Care Reference Data WHITE PAPER Talend Infosense Solution Brief Master Data Management for Health Care Reference Data Table of contents BUSINESS ISSUE: SOCIAL COLLABORATION AND DATA STEWARDSHIP... 5 BUSINESS ISSUE: FEEDBACK

More information

Utilizing Credit Scoring to Predict Patient Outcomes. An Equifax Predictive Sciences Research Paper September 2005

Utilizing Credit Scoring to Predict Patient Outcomes. An Equifax Predictive Sciences Research Paper September 2005 Utilizing Credit Scoring to Predict Patient Outcomes An Equifax Predictive Sciences Research Paper September 2005 Introduction Improving Your Revenue Cycle Performance Through Financial Management Solutions

More information

Three proven methods to achieve a higher ROI from data mining

Three proven methods to achieve a higher ROI from data mining IBM SPSS Modeler Three proven methods to achieve a higher ROI from data mining Take your business results to the next level Highlights: Incorporate additional types of data in your predictive models By

More information

DIVA Advanced Stochastic & Dynamic Financial Analysis Modeling

DIVA Advanced Stochastic & Dynamic Financial Analysis Modeling DIVA Advanced Stochastic & Dynamic Financial Analysis Modeling A flexible, intuitive tool to model the financial impact of complex, volatile insurance s in a rapidly changing environment Introducing DIVA

More information

Better planning and forecasting with IBM Predictive Analytics

Better planning and forecasting with IBM Predictive Analytics IBM Software Business Analytics SPSS Predictive Analytics Better planning and forecasting with IBM Predictive Analytics Using IBM Cognos TM1 with IBM SPSS Predictive Analytics to build better plans and

More information

Pentaho Data Mining Last Modified on January 22, 2007

Pentaho Data Mining Last Modified on January 22, 2007 Pentaho Data Mining Copyright 2007 Pentaho Corporation. Redistribution permitted. All trademarks are the property of their respective owners. For the latest information, please visit our web site at www.pentaho.org

More information

Potential Value of Data Mining for Customer Relationship Marketing in the Banking Industry

Potential Value of Data Mining for Customer Relationship Marketing in the Banking Industry Advances in Natural and Applied Sciences, 3(1): 73-78, 2009 ISSN 1995-0772 2009, American Eurasian Network for Scientific Information This is a refereed journal and all articles are professionally screened

More information

University of Michigan Dearborn Graduate Psychology Assessment Program

University of Michigan Dearborn Graduate Psychology Assessment Program University of Michigan Dearborn Graduate Psychology Assessment Program Graduate Clinical Health Psychology Program Goals 1 Psychotherapy Skills Acquisition: To train students in the skills and knowledge

More information

Session 55 PD, Actuaries and the International Market. Moderator/Presenter: Ian G. Duncan, FSA, FIA, FCIA, MAAA

Session 55 PD, Actuaries and the International Market. Moderator/Presenter: Ian G. Duncan, FSA, FIA, FCIA, MAAA Session 55 PD, Actuaries and the International Market Moderator/Presenter: Ian G. Duncan, FSA, FIA, FCIA, MAAA Presenters: Jorge A. Alvidrez, ASA, MAAA Jeremiah D. Reuter, ASA, MAAA Actuaries and the International

More information

American Academy of Actuaries/Society of Actuaries Long-Term Care Valuation Work Group. Report on Long-Term Care Valuation.

American Academy of Actuaries/Society of Actuaries Long-Term Care Valuation Work Group. Report on Long-Term Care Valuation. American Academy of Actuaries/Society of Actuaries Long-Term Care Valuation Work Group Report on Long-Term Care Valuation February 2014 The American Academy of Actuaries is a 18,000-member professional

More information

A Look at the Varied Responsibilities of Internal Auditors. internal auditing: All in a days work

A Look at the Varied Responsibilities of Internal Auditors. internal auditing: All in a days work ALL IN A DAY S WORK A Look at the Varied Responsibilities of Internal Auditors internal auditing: All in a days work The Institute of Internal Auditors Achieving Objectives For the most part, companies

More information

Fundamentals of Information Systems, Seventh Edition

Fundamentals of Information Systems, Seventh Edition Chapter 1 An Introduction to Information Systems in Organizations 1 Principles and Learning Objectives The value of information is directly linked to how it helps decision makers achieve the organization

More information

Using Data Mining to Detect Insurance Fraud

Using Data Mining to Detect Insurance Fraud IBM SPSS Modeler Using Data Mining to Detect Insurance Fraud Improve accuracy and minimize loss Highlights: Combine powerful analytical techniques with existing fraud detection and prevention efforts Build

More information

Content. Management Summary... 3

Content. Management Summary... 3 Real Time Marketing Self-learning, intelligent customer scoring offers financial service providers a made-to-measure forecasting model for individual customers Content Management Summary... 3 Intelligent,

More information

Grow Revenues and Reduce Risk with Powerful Analytics Software

Grow Revenues and Reduce Risk with Powerful Analytics Software Grow Revenues and Reduce Risk with Powerful Analytics Software Overview Gaining knowledge through data selection, data exploration, model creation and predictive action is the key to increasing revenues,

More information

Web analytics: Data Collected via the Internet

Web analytics: Data Collected via the Internet Database Marketing Fall 2016 Web analytics (incl real-time data) Collaborative filtering Facebook advertising Mobile marketing Slide set 8 1 Web analytics: Data Collected via the Internet Customers can

More information

CRISP - DM. Data Mining Process. Process Standardization. Why Should There be a Standard Process? Cross-Industry Standard Process for Data Mining

CRISP - DM. Data Mining Process. Process Standardization. Why Should There be a Standard Process? Cross-Industry Standard Process for Data Mining Mining Process CRISP - DM Cross-Industry Standard Process for Mining (CRISP-DM) European Community funded effort to develop framework for data mining tasks Goals: Cross-Industry Standard Process for Mining

More information

69 PD Underwriting Issues for Group Life and Disability Insurance. Moderator: Peter A. Heinrichs, FSA, MAAA

69 PD Underwriting Issues for Group Life and Disability Insurance. Moderator: Peter A. Heinrichs, FSA, MAAA 69 PD Underwriting Issues for Group Life and Disability Insurance Moderator: Peter A. Heinrichs, FSA, MAAA Presenters: Susan L. Ebertz Michael F. Vassar Mark R. Yoest, FSA, MAAA Underwriting Trends in

More information

whitepaper 16 TIPS FOR BUILDING AN ENGAGING ECOMMERCE EXPERIENCE How to tackle the obvious and not-so obvious challenges

whitepaper 16 TIPS FOR BUILDING AN ENGAGING ECOMMERCE EXPERIENCE How to tackle the obvious and not-so obvious challenges whitepaper 16 TIPS FOR BUILDING AN ENGAGING ECOMMERCE EXPERIENCE How to tackle the obvious and not-so obvious challenges - 16 Tips for Building an Engaging Ecommerce Experience INTRODUCTION 1 INTRODUCTION

More information

A New Foundation For Customer Management

A New Foundation For Customer Management The Customer Data Platform: A New Foundation For Customer Management 730 Yale Avenue Swarthmore, PA 19081 info@raabassociatesinc.com The Marketing Technology Treadmill Marketing automation. Inbound marketing.

More information

ICD-10: Industry Perceptions and Readiness

ICD-10: Industry Perceptions and Readiness ICD-10: Industry Perceptions and Readiness John Kasey Andrew Naugle, MBA Patricia Zenner, RN Introduction to ICD-10 The U.S. healthcare industry is poised to undergo many radical changes in the coming

More information

HEALTHCARE BUSINESS INTELLIGENCE SYSTEM

HEALTHCARE BUSINESS INTELLIGENCE SYSTEM HEALTHCARE BUSINESS INTELLIGENCE SYSTEM A six-in-one healthcare data analysis solution to drive innovation and access while controlling overall costs There is no single business intelligence system like

More information