Download
predictive modeling workshop training for development and deployment n.
Skip this Video
Loading SlideShow in 5 Seconds..
Predictive Modeling Workshop Training for development and deployment PowerPoint Presentation
Download Presentation
Predictive Modeling Workshop Training for development and deployment

Predictive Modeling Workshop Training for development and deployment

207 Vues Download Presentation
Télécharger la présentation

Predictive Modeling Workshop Training for development and deployment

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Predictive Modeling WorkshopTraining for development and deployment Francis Analytics and Actuarial Data Mining The following presentation outlines a introductory training course for actuaries, and quantitative analysts to learn basic approaches and tools to create predictive model solutions in Property & Casualty Insurance www.data-mines.com

  2. Introduction • Predictive Modeling is an important new development for modifying traditional actuarial approaches to risk in P&C insurance • Actuaries and Quantitative Analysts can help advance their company’s competitive position with a greater understanding of analytical methods and tools • Learning opportunities in advanced actuarial modeling area not widely available. Francis Analytics www.data-mines.com

  3. Predictive Modeling Survey Course • A two day hands-on workshop • Delivered by experienced practitioners • Includes case study learning approach • Includes instructions in using software and the latest statistical methods being deployed by industry leaders. • Custom course can be designed to fit specific needs Participants will be able to conduct a Predictive Modeling project in their organizations and present the business case to non-technical management Francis Analytics www.data-mines.com

  4. Course Agenda Session Description Introduction to P&C Actuarial Data Introduction and review of actuarial data for property & casualty insurance including basic operational view Introduction to Predictive Modeling Methods Survey of Predictive Modeling approaches and current state of the art in P&C insurance Walk-through model development phases Walk-through of the project process for delivering successful predictive modeling capabilities Focus: Data Preparation Techniques Focus teaching on data discovery and preparation steps used in the model development process Focus: Mathematical Methods Focused teaching of the key statistical algorithms used in Predictive Modeling Focus: Statistical Software Focused teaching and hands on tutoring of an statistical modeling package software Hands-on Case Study workshop Live end-to-end walk through of a predictive modeling project Validation Methods Teaching and illustration of model validation approaches to present to management Industry Leading Innovation Discussion of industry leading innovation in development and deployment of Predictive Models Francis Analytics www.data-mines.com

  5. Case Study Approach • Actual P&C example • Sample Actuarial Dataset provided • Explore challenges throughout the exploration/model/deployment life-cycle Practitioners can design a custom case study with a specific modeling need or data-set provided by the company sponsor or organization. Francis Analytics www.data-mines.com

  6. Leading Statistical Methods Covered • Generalized Linear Modeling including Logistic Regression • Neural Network • Classification and Regression Trees • Benchmarking with Naive Bayes • Tree ensemble method • Random Forest • Support Vector Machines Francis Analytics www.data-mines.com

  7. Software • Excel • Access • Free Software • R • Web based software • S-Plus (similar to commercial version of R) • SPSS • CART/MARS • Data Mining suites Participants will download, install, tour and model in R, a freeware modeling tool Francis Analytics www.data-mines.com

  8. Learning Outcomes • Working knowledge of the major statistical methods used for Predictive Modeling • Practical knowledge of advanced data mining techniques ready to be applied • General knowledge of Property & Casualty insurance data and related operations • General knowledge of Predictive Modeling project life-cycle management • Working knowledge of modeling software packages and review of leading software solutions and proprietary methods • The ability to manage the validation of models and present the validation results to laymen Francis Analytics www.data-mines.com

  9. Logistics Item 2 days Overnight Homework as assigned Session Schedule Course materials and Data Set Berry, M. and Linoff, G. Data Mining Techniques 2nd Edition, Wiley, supplied to all students California Department of Insurance public (CAARP) data on private passenger auto will be supplied to all participants as a download Software ‘R’ statistical programming language, including download and install instructions Limit of 25 participants Class Size Customized Content Arranged by agreement Francis Analytics www.data-mines.com

  10. Pricing Item Course Fee TBD Course Materials Included Follow-up Consultation Included On-site/Group/Company Sponsored Priced by custom proposal * * Pricing for customized workshop content and delivery by agreement Francis Analytics www.data-mines.com

  11. Workshop Presenters Louise Francis, FCAS, MAAA Louise Francis is the Consulting Principal and founder of Francis Analytics and Actuarial Data Mining, Inc. where she leads data mining and related actuarial projects and engagements. Ms. Francis has introduced insurance professionals to data mining methods both as a speaker at conferences and as an author of papers and articles on data mining topics. Three of her papers were awarded the Data Quality/Data Technology prize by the CAS (Casualty Actuarial Society) and IDMA (Insurance Data Management Association). As an insurance professional, Ms. Francis has expertise in a variety of techniques for pricing and reserving insurance products, both on a traditional and leading edge basis. She also has experience planning and evaluating programs for claims loss cost reduction. Ms. Francis pioneered the application of data mining technologies such as neural networks and decision trees to insurance data to identify patterns useful for mitigating claims costs and making underwriting decisions. Ms. Francis introduced procedures for benchmarking individual customer's data to industry targets to assess the performance of third party claims administrators and to assist clients with cost reduction efforts. Ms. Francis has over 20 years of experience in the actuarial profession. Prior to starting her own firm, she was Director and Associate Actuary for CIGNA Property and Casualty where she provided actuarial expertise to the Claims Division and ESIS, CIGNA's third party claims administrator. She was responsible for evaluating the overall performance of the Claims Division and measuring the effectiveness of specific Claims Division initiatives to reduce claims costs. Ms. Francis has also worked as a consultant with Towers Perrin and Sedgwick James. Ms Francis is chair of the Casualty Actuarial Society's (CAS) Committee on the Theory of Risk, a committee charged with the sponsorship and dissemination of research efforts on risk measurement and analysis. She participates on an American Academy of Actuaries committee which develops standards of actuarial practice for the actuarial profession. Ms. Francis received her Bachelor of Arts degree from William Smith College and a Master of Science degree in Health Sciences from the State University of New York at Stony Brook. She is a Fellow of the Casualty Actuarial Society and a member of the American Academy of Actuaries. Francis Analytics www.data-mines.com

  12. Workshop Presenters RICHARD A. DERRIG, PH.D. Dr. Derrig is President of OPAL Consulting LLC, established in February, 2004 to provide research and regulatory support to the property-casualty insurance industry. Prior to forming OPAL, Dr. Derrig held various positions at the Automobile Insurers Bureau of Massachusetts and at the Insurance Fraud Bureau of Massachusetts over a twenty seven year period, retiring in January, 2004 as Senior Vice President at AIB and Vice President of Research at IFB. During the spring semesters of 1994 and 2002, he was a Visiting Lecturer and Research Fellow in the Department of Risk Management and Insurance at the Wharton School, University of Pennsylvania. Dr. Derrig has been appointed a visiting scholar at Wharton for 2004 and 2005. Prior to joining the Bureaus, he taught graduate and undergraduate mathematics at Villanova University, Wheaton College (MA) and Brown for a total of thirteen years. He earned a Bachelor of Science degree in mathematics from St. Peter's College as well as Master's and Doctoral degrees from Brown University. Dr. Derrig is the recipient of numerous awards and recognitions for his contribution to mathematics and actuarial sciences. He the author or co-author of numerous articles, review and contributions in the field. He has lectured extensively on insurance topics to professional actuarial groups; the Casualty Actuarial Society, ASTIN, trade associations, and law enforcement personnel; and to seminars at the Universities of Barcelona, Hamburg, Montreal, Tel Aviv, Pennsylvania, Illinois, Texas, Minnesota, Wisconsin and others in the U.S. He was a director of the American Risk and Insurance Association (1992-1995) and a recipient of the President’s Award (1997). He is an academic correspondent of the CAS and a member of the Mathematical Association of America, American Statistical Association, and the Association of Certified Fraud Examiners. He serves on the Insurance Fraud and Auto Injury Study Committees of the Insurance Research Council and the CPCU Advisory Committee of the American Institutes. Since 1991, he has compiled an annotated bibliography of worldwide insurance fraud research that is made available at www.derrig.com/ifrr/ifrr.asp. Francis Analytics www.data-mines.com

  13. Contact Ms. Louise Francis, FCAS, MAAA Francis Analytics 215-923-1567 louise.francis@data-mines.com www.data-mines.com Francis Analytics www.data-mines.com