Data Management for Insurance Professionals (DMIP)

The ninth edition is now available.
This overview course is highly recommended for a broad audience including new hires, IT personnel who want to deepen their knowledge of the business side of data management, anyone who manages data in the industry, and anyone who needs to use or communicate data - from actuaries to underwriters. It is clear, well organized, well written, illuminating, and structured for easy study. A three-hour, 100-question, multiple-choice exam will be given three times a year, and the successful student will earn a certificate of completion diploma. IDMA also has a certification curriculum, and this course may well be the perfect introduction to that course of study.

 
Sample Course Objectives Order Form
 

Chapter Summaries

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CHAPTER ONE - The History of Data Management
In the History of Insurance Data Management, the student explores both the history and basic terms and fundamentals of Data Management. Here you will discover how the insurance industry has changed its perception of the value, the function, and the ownership of corporate information. The chapter covers fundamentals of insurance information processing, and describes how data collection was shaped by the limitations of early computer systems. You'll learn the key distinction between data and information, and how Data Management has emerged as a distinct profession to maximize the informational value of data. The chapter closes with a detailed discussion of the IDMA organization - its activities, its goals, and need for the participation of motivated Insurance Data Management professionals like you.

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CHAPTER TWO - The Role of the Insurance Data Manager
While Chapter One emphasized the importance of data, you'll begin to understand the responsibilities of the people who are entrusted with the collection and oversight of insurance information in Role of the Insurance Data Manager. The student will see the reasons why the actuary, the underwriter, and senior management decision-makers all rely on the work of the Data Manager. You will be introduced to the concept of data stewardship, and gain an appreciation for how the Data Manager's role differs in Property/Casualty insurers, Life/Health insurers, and Regulatory Agencies.

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CHAPTER THREE - Key Data Elements of Insurance
In Key Data Elements of Insurance, you will learn the fundamentals of insurance ratemaking, and understand how credible rates rely upon quality data. Since one of the primary purposes of insurance data is to provide an historical basis for future rates, it is vital for any Data Management professional to understand how actuaries use this information. You'll learn the components of a rate, the definition of an 'adequate' rate, and the distinction between accident year and policy year ratemaking data. Beyond the work of insurance actuaries, you'll discover the goals of insurance regulators as they try to fulfill their obligation to the industry and to the public. When you've completed this chapter, you will be prepared to better understand data collection requirements, both in published Statistical Plans and in new requirements that are asked by actuaries and regulators.

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CHAPTER FOUR - Insurance Companies' Use of Data
Insurance Companies' Use of Data continues the theme of Chapter Three, but moves beyond ratemaking. Here you will see how insurers rely upon quality data for internal departmental and corporate needs. You'll study the importance of data in establishing underwriting guidelines. Insurers and regulators have developed ratios used for assessing a company's profitability and financial health; you will learn the definition and uses of loss ratio, expense ratio, and combined ratio. The chapter demonstrates why insurers need a distinct Data Management section or department, and how its activities benefit the company by assuring access to a solid and reliable knowledge base of data. The elements of good enterprise data strategy are discussed. The chapter includes a discussion of the impact of Data Management on the insurance customer. The difference between ratemaking and statistical needs for data is explained.

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CHAPTER FIVE - The External Insurance Environment
In The External Insurance Environment, you'll learn about the variety of stakeholders who influence insurers and the need for information. Key organizations covered include government agencies, insurance advisory/rating services, Departments of Motor Vehicles, statistical agents, the NAIC, the IAIABC, MGAs, and TPAs. You'll see that there are many factors that influence the insurance contract beyond the insurer and the insured. Covered topics include the relationship between internal and external uses of data, an overview of the statutory environment (State Insurance Departments, State Worker's Compensation Commissions, The International Association of Industrial Accident Boards and Commissions, and the National Association of Insurance Commissioners), and some details on the major rating advisory organizations and data collection agencies (AAIS, ISO, NAII, NCCI, NCIS, NISS, AIPSO, SAA). You'll also discover how the role of statistical agents may dramatically change in response to industry consolidation, the Internet, deregulation, the threat of federal intervention, and social/legal pressure.

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CHAPTER SIX - Data Quality
You'll begin to understand the tools, methodologies, and fundamental tenets embraced by Data Management professionals in this chapter, Data Quality. The basic components of data quality are listed and defined, the concept of systems testing is introduced, and the process of error evaluation is covered. To establish and promote the quality of information in your own company, you'll benefit by learning these proven techniques that have become the standards of the profession. You will understand the difference between financial and statistical data, in terms of means of collection, customers, uses, and standards. Data standards, especially key in electronic communication, are introduced and discussed. Quality assurance tools are covered in an overview of statistical sampling measures, data audits, and data monitoring systems. No architect of data quality is equipped for the job without a command of these toolsets.

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CHAPTER SEVEN - Data Repositories
The nature of data storage and retrieval has changed along with available technology. From ledgers and filing cabinets, early information processing was advanced by the use of computers, tabulation machines, and the 80-column keypunch cards. There has been a metamorphosis from the legacy computer environment in which millions of electronic records (still largely analogous to the 80-column keypunch card) are stored and manipulated in their 'flat file' format, to relational databases of information stored in a hierarchical but flexible series of data tables. In Data Repositories, you'll study the latest advances in technology for the storage of key insurance data, such as Data Warehouse and Data Mining. You'll discover the advantages inherent in the new technology, including the ease of query by members of business units. The importance of metadata is explained. Data backup and recovery are introduced and discussed as key elements in ensuring data integrity.

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CHAPTER EIGHT - Future Data Management Issues
The final chapter, Future Data Management Issues, examines the critical concerns in technology, financial services, and regulation that face data managers. You'll understand the key concept of convergence and how the necessity for change is constant. Historical perspectives on convergence precede a discussion on the digital nature of the New Economy and convergence in financial services and in regulation. The course and chapter conclude with an emphasis on the increasing need for professionalism within data management, and how data managers must recognize that what they do is both a discipline and a profession. You'll find a deeper appreciation of the history, the current efforts, and the continuing need to educate and promote the value of certified, professional data managers in organizations like IDMA. You will see a newly focused discussion with emphases on public trust, the post 9/11 world, and the post-Enron world.


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Look over these sample objectives from the course — what would your responses be like?

The History of Insurance Data Management

  • Explain why insurers produce and use data, and summarize how it is important to the company's competitive advantage.
  • List the responsibilities of Data Administration and explain how it relates to the database environment.
  • Differentiate Data Management and Database Administration.
  • Trace the improvements that set one generation of computers apart from the preceding generation beginning in the 1960s and onward.
  • Explain the difference between data and information.
  • Identify the founding purpose of the Insurance Data Management Association (IDMA) and list eight activities and explain how they relate to IDMA's stated purpose.

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Role of the Insurance Data Manager

  • Describe some impediments to efficient communication.
  • List important questions to ask when investigating meta-data. Propose more that assure clarity.
  • Define data stewardship and explain its purpose.
  • Explain how property and casualty data managers support actuaries, underwriters, and senior management decision-makers.
  • List the characteristics of Statistical Plans and explain their purpose.
  • Name the similarities and differences between the property and liability industry and the life and health industry, vis-à-vis the role of the data manager.
  • Trace the logic that holds that with increasing consolidation of the industry, the data manager's role will become more critical.

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Key Data Elements of Insurance

  • Quote the phrase that regulators use to judge insurance rates (hint: it includes three conditions).
  • Define an adequate rate, and go into some detail concerning how that is determined; discuss what might complicate judging whether or not a rate is adequate.
  • Explain the "fair rate of return" approach and then state the insurers' contrary argument.
  • Distinguish between fair discrimination and unfair discrimination.
  • List the seven goals regulators address in order to fulfill their obligation to the industry and the public.
  • Name the types of insurance regulation.
  • List the five types of laws used in rate regulation.
  • Define key ratemaking terms.
  • Explain why accident year data and policy year data are more useful for ratemaking than calendar year data.
  • Differentiate policyholder, insured, and beneficiary in the data elements required for life and health insurance.

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    Insurance Companies' Use of Data

  • Name at least three types of data which statistical organizations provide that staff underwriters likely to evaluate before they consider writing a new line of business or expanding an existing line of business.
  • Contrast the data collected by LOMA, LIMRA, and HIAA with the data collected by property and casualty statistical bureaus, in terms of regulatory demands, its completeness in representing its side of the industry (life and health or property and casualty), and its uses.
  • Compare class rating with individual rating.
  • List six ways data may be used to satisfy internal department or company needs.
  • Data are used to create three ratios important to determining profitability. Define loss ratio, expense ratio, and combined ratio.
  • Advocate for sharing loss data - all forms of loss data - with Underwriting.
  • Give reasons life and health companies report to various organizations when they are not required to by law.

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    The External Insurance Environment

  • Name the various departments and organizations that make up the statutory environment and list the activities that these departments engage in as they regulate the insurance industry.
  • List the seven major types of external organizations that collect insurance data and explain how and why they collect and use the data.
  • Contrast the role of a statistical agent with that of an advisory/rating organization.
  • Define the role of an advisory organization and describe the functions of each of the eight rating advisory organizations named in this chapter.
  • Enumerate the various types and roles of vendors and be able to compare and contrast the two types of vendors used for policy and claims processing.
  • Identify the services provided with claims review and explain how these services save the industry money.
  • Explain how submitting data to external users may benefit the insurer.
  • Name and briefly explain each of the forces behind change in the statistical agents’ role.
  • Explain how it can be that vendors who report on behalf of carriers and who process data find their tasks both more complex and increasingly easy.
  • List the major changes of interest to the statistical community during the 1990s.  Also, name three events that have occurred in the 2000s that impacted the insurance industry, and explain what changes they prompted.

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Data Quality

  • Explain the difference between financial and statistical data in terms of means of collection, customers, uses and standards.
  • List several examples of the impact of using inaccurate or inconsistent data within the industry and among its customers.
  • Promote the concept of systems testing.
  • Name and define the standard components of data quality.
  • Characterize unit testing, system testing and acceptance testing (user acceptance testing or field-testing).
  • Define a Test Plan by referring to its five essentials.
  • Name and explain seven steps in the process of error evaluation.
  • Describe three error evaluation tools
  • Enumerate the elements of a successful data audit.
  • State the purpose of data monitoring systems, and specify what insurers and statistical agents are responsible for under a data-monitoring program.
  • Define standards. List and explain the industry challenges — both those that demand standards and those that stand in the way of acceptance of standards.

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Data Repositories

  • Define the data warehouse, citing the key components and explaining what each term means specifically in relation to this definition.
  • List six functions and decision-making activities which data contained in a data warehouse can support.
  • As you read the "Historical Perspective - Where We Were" portion of this chapter, realizing that the period covered is only a few years, name the problems inherent in the early data warehouses and tools developed to use them.
  • Define naturally evolving architecture.
  • Legacy systems, as their faults and uses are recounted here, are still with the industry; name three reasons mainframe computers are still in use today.
  • Discuss the advantages and disadvantages of enterprise data warehouses.
  • Discuss and explain data backup and recovery reasons and methods.

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Future Data Management Issues

  • Use convergence as a model for evaluating the changes taking place in the insurance marketplace.
  • Identify critical issues in technology, financial services and regulation facing data managers.
  • Discuss various strategies and techniques for managing in times of convergence.
  • Identify the key aspects of a professional, and apply them to data managers.
  • Explain how professionalism is integral to successful data management.
  • Data managers routinely collaborate with other functions and activities. Explain how this collaboration will broaden and intensify as a result of the passage of the U.S. Terrorism Risk Insurance Act.
  • Discuss the impact of the tragedies of 9/11/2001 upon data management.
 

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