<div class="exam-nav" data-color="#c026d3" data-prev="CR3|Introduction to Catastrophe Modeling Methodologies" data-current="CR4|Catastrophe Modeling Process" data-tracks="CSCR|Exams/Certifications/Certified Specialist in Catastrophe Risk (CSCR).md" </div> ## Exam CR4 Syllabus <div class="download-dropdown" data-color="#2563eb" data-label="Download Resources" data-files="Exam CR4 - Learning Objectives|https://www.catriskcredentials.org/wp-content/uploads/2020/09/Exam4-Catastrophe-Modeling-Process-Learning-Objectives.pdf, Learning Objective No. 13 Study Note|https://www.catriskcredentials.org/wp-content/uploads/2020/09/Study-Note-CSCR-Exam-4-LO13.pdf,Learning Objective No. 26|https://www.catriskcredentials.org/wp-content/uploads/2020/09/Study-Note-CSCR-Exam-4-LO26.pdf,Learning Objective No. 27-34|https://www.catriskcredentials.org/wp-content/uploads/2020/09/Study-Note-CSCR-Exam-4-Learning Objective No. 27_34.pdf,Learning Objective No. 35|https://www.catriskcredentials.org/wp-content/uploads/2020/09/Study-Note-CSCR-Exam-4-LO35.pdf,Learning Objective No. 38-40|https://www.catriskcredentials.org/wp-content/uploads/2020/09/Study-Note-CSCR-Exam-4-LO38_40.pdf"> </div> - [Register for Exam CR4](https://www.catriskcredentials.org/index.php/credential/certified-catastrophe-risk-specialist/procedures-and-registration/) The Catastrophe Modeling Process Exam (Exam 4) ### Learning Objectives > [!example]- General Data Concepts {unspecified %} > > ### Learning Objectives > #### SQL as a Data Description Language > > 1. Identify the utility and challenges when using [[SQL]] as a query language in [[Catastrophe Modelling]]. > 2. Recognize the use of other analytical tools in catastrophe modeling ([[R]], [[Python]]). > 3. Recognize the differences, advantages, and disadvantages of [[Relational Database]] designs ([[Star Schema]], [[Snowflake Schema]], etc.). > 4. Understand the concept of [[Database Permissions]] and how [[Database Administrators|DBAs]] would control permissions on critical databases. > 5. Describe how [[Relational Database|relational databases]] are used in the [[Catastrophe Modeling Process]], including [[Exposure Database]] (location, account, portfolio, reinsurance, conditions tables), [[Result Database]], and [[Reference Database]]. > 6. Understand the concept of [[Data Lake]] vs [[Data Warehouse]], [[Structured Data]] vs [[Unstructured Data]], and the pros and cons of both. > > ### Module 2: Data, Queries and Stored Procedures > > 7. Describe the use of the following [[SQL Data Types]]: Int, bigint, smallint, Float, Varchar(n), Nvarchar(n), char(n), Datetime, Bit. > 8. Explain the role of the following concepts in [[SQL Queries]] and [[Stored Procedures]]: [[Null|Null / Not Null]], [[Primary Key]], [[SQL Variable|Variable]], [[Stored Procedures]]. > > ### Module 3: Special Considerations for Geocoding > > 9. Understand the definition of [[Geocoding]]. > 10. Describe the key concepts of [[Geocoding]], including process and major steps, [[Geocoding Hierarchy|hierarchy]], and [[Geodetic Datum]]. > 11. Explain the various resolutions of [[Geocoding Resolution|geocoding]]. > 12. Understand the important attributes of [[Geocoding]]. > 13. Explain the concept of [[Centroid]] and [[Average Property]]. > > ### Module 4: Basics of SQL Queries > > 14. Describe the basic structure of a [[SELECT Query]] including SELECT, FROM, WHERE, and LIKE. > 15. Describe the difference between [[INNER JOIN]], [[OUTER JOIN]], and [[LEFT JOIN|LEFT/RIGHT JOIN]]. > 16. Describe the basic structure of an [[UPDATE Query]]. > 17. Understand the [[CAST Command]] and how it is used. > > ### Module 5: Aggregating > > 18. Describe basic [[Aggregate Functions|aggregating statements]]: [[COUNT]], [[SUM]], [[MIN]]/[[MAX]], [[AVG]]. > 19. Explain how [[GROUP BY]] and [[HAVING]] are used with [[Aggregate Functions|aggregating statements]]. > > ### Module 6: Indexes and Table Creation > > 20. Describe the difference between [[CREATE TABLE]] and [[SELECT INTO|INTO]]. > 21. Explain the concepts of [[ALTER TABLE]] and [[APPEND]]. > 22. Describe the difference between [[DROP TABLE]], [[DELETE]], and [[TRUNCATE]]. > 23. Identify a [[SQL Temporary Table]] and the rationale for using them. > 24. Explain the rationale behind having an [[Index]], and the commands used to create an index. > 25. Understand the use of [[Database Permissions|permissions]] in [[SQL]] databases, including why, in [[Catastrophe Modelling]], some tables or databases may be read only. > [!example]- Managing the Process and Workflow {unspecified %} > > ### Module 1: Major Steps in a Catastrophe Modelling Exercise > > 26. Understand the major steps in the [[Catastrophe Modeling Process]] and the important considerations at each step, including [[Policy Identification|identification of policies]], [[Data Cleansing]], [[Data Quality]], [[Data Import|import/attach]], [[Application of Financial Terms]], and [[Running Models]]. > > ### Module 2: Determining and Testing Data Quality > > 27. Describe the most important [[Exposure Data]] fields and their purpose. Explain what [[COPE Data]], [[Primary Modifiers]], and [[Secondary Modifiers]] are. > 28. Describe what [[Aggregate Data]] is, the potential pitfalls of using this type of data, and possible methods of [[Disaggregation]]. > 29. Determine and explain the impact of [[Address Accuracy]] and [[Geocoding]] on [[Modeled Loss]], including an understanding of the material differences by [[Peril]] and region. > 30. Describe instances where [[Mailing Address]] may have been provided in place of [[Risk Address]]. > 31. Explain the difficulty of obtaining accurate address information for certain [[Classes of Business]] (such as [[Builders Risk|builders' risk]], [[Offshore Energy]], [[Workers Compensation]]). > 32. Define [[Bulk Coding]] along with its potential uses and pitfalls in modeling. > 33. Identify [[Validation Checks]] which would indicate potential erroneous data or poor [[Data Quality]] (e.g., 100 story wood frame building). > 34. Define [[Data Quality]] and explain the rationale behind assessing it. > > ### Module 3: Auditing Consistency with Prior Year Data > > 35. Describe the reasons that [[Modeled Results]] may change over time in relation to [[Exposure Data]] consistency. > 36. List the main steps to perform a [[Data Audit]]. > > ### Module 4: Relevant Queries and Reports > > 37. Describe the potential outputs that may be required from the following analyses and compare the differences by potential users: [[Data Quality Matrix]], [[Geocoding Resolution]], [[Modeling Assumptions and Configuration]], [[Stochastic Loss Analysis]], [[Driving Perils]], [[Deterministic Loss Analysis]] ([[Realistic Disaster Scenario|RDS]], [[Bomb Blast]], [[Historical Event As-If]]), [[Hazard Analysis]], [[GeoSpatial Analysis]], [[Zonal Aggregates and Accumulations]], [[Marginal Impact]], [[Portfolio Optimization]], [[Capital Allocation]], [[Company Own View of Risk]], [[Trend Analysis]], [[Model Completeness and Validation]], and [[Modeling Process and Controls]]. > > ### Module 5: Annual Renewal Cycles, Resources and Technology > > 38. Describe the [[Market Renewal Cycles]] and what the [[Catastrophe Modelling]] focus is of each renewal period. > 39. Describe different [[Staffing Models]]. > 40. Describe [[Deployment Methods]] for [[Catastrophe Modelling]] applications. > [!example]- Understanding Cat Model Output {unspecified %} > > ### Module 6: Uncertainty in Modeling Results > > 41. Identify, define, and explain the differences in types of [[Uncertainty]] as they pertain to [[Catastrophe Modelling]]. > 42. Identify key sources of [[Primary Uncertainty]] and [[Secondary Uncertainty]] within models. > 43. Describe and be able to identify the impact [[Uncertainty]] has on an [[Exceedance Probability Curve]] and [[Modeled Results]], as well as the different approaches of incorporating and reporting uncertainty. > 44. Identify and describe examples of uncertainty around [[Hazard]], [[Exposure]], and [[Vulnerability]]. > 45. Understand the different approaches of incorporating and reporting [[Uncertainty]] in [[Modeled Output]]. > > ### Module 7: Model Output — Basic Metrics and Concepts > > 46. Understand the various components of an [[Event Loss Table]] and [[Year Loss Table]], including how these are used to develop an [[Exceedance Probability Curve]]. > 47. Identify, define, and understand relationships between common model output metrics such as [[AAL]], [[PML]], [[VaR]], and [[TVaR]]. Be able to compare and contrast these relationships for different portfolios. > 48. Understand the differences between [[Occurrence EP]] and [[Aggregate EP]] curves and be able to apply this information in the [[Underwriting]] and/or [[Pricing]] process. > > ### Module 8: Model Output — Advanced Metrics and Concepts > > 49. Be able to distinguish and explain the differences between [[XSAAL]] and [[TVaR]]. > 50. Define the concepts of [[Convergence]] and the importance of the [[Number of Simulations]]. > 51. Understand and be able to explain different statistical approaches of models to address [[Frequency]] and [[Severity]] ([[Poisson Distribution|Poisson]], [[Negative Binomial Distribution|Negative Binomial]], [[Beta Distribution|Beta]], [[Pareto Distribution|Pareto]]) and the importance of each in developing [[Loss Estimates]]. > > ### Module 9: Financial Structure and Loss Perspectives > > 52. Be able to identify, define, and explain the differences between the [[Financial Loss Perspectives]] of cat model output: [[Ground Up Loss|Ground Up]], [[Gross Loss|Gross]], [[Pre-Cat Loss|Pre-Cat]], [[Net Loss|Net]]. > 53. Be able to identify, define, and explain how [[Location Financial Terms|location]], [[Policy Financial Terms|policy]], and [[Reinsurance Financial Terms|reinsurance]] financial terms impact the [[Financial Loss Perspectives]] of both a [[Primary Insurance Company|primary]] and [[Reinsurance Company]]. > [!example]- Working with and Communicating Cat Model Output {unspecified %} > > ### Module 1: Impact of Loss Curves on Business Decision Making > > 54. Be able to describe the impacts of [[Data Quality]] and approaches to improve gaps in [[Exposure Data]]. > 55. Explain how [[Modeled Output]] may be used in the [[Underwriting]] process to accept/decline a piece of business. > 56. Explain actions that can be taken to turn an unacceptable piece of business into an acceptable piece of business. > 57. Explain how [[Modeled Output]] is used to develop the [[Technical Pricing]] for a [[Reinsurance Program]] (e.g., [[Facultative Reinsurance|facultative]], [[Cat XOL Treaty]], etc.). > > ### Module 2: Actual vs Modeled Losses > > 58. Explain the importance of comparing a company's own [[Loss Experience]] for actual events to a model's [[Event Reconstruction|reconstruction]] of these events. > > ### Module 3: Event Response > > 59. Explain why an [[Event Response]] process is necessary for a company. > 60. Describe how an [[Event Response]] process may vary between events. > 61. Describe how [[Modeled Footprints]] can be leveraged in an [[Event Response]] process, and identify characteristics of actual events that could lead to [[Model Underperformance]] in the [[Loss Estimation]] process. > 62. Identify the various business areas/departments within a [[(Re)Insurance Company]] which may be included in any [[Real Time Event Response Communication]]. > 63. Identify the types of information that may be included in any [[Real Time Event Response Communication]]. > 64. Describe caveats/disclaimers that should be noted in any [[Real Time Event Response Communication]]. > > ### Module 4: Rating Agencies and Regulators > > 65. Describe the role of [[Rating Agencies]] in assessing a (re)insurer's ability to meet its financial obligations and be able to identify what is considered in the [[Standard and Poors Catastrophe Charge|Standard & Poor's catastrophe charge]] and the [[AM Best Rating Questionnaire]]. > 66. Explain why [[Regulators]] are interested in understanding a (re)insurance company's exposure to [[Catastrophe Risk]] and describe the link between [[Regulation]] and [[Catastrophe Modelling]]. ### Sources |Source|Coverage| |---|---| |[[Natural Catastrophe Risk Management and Modelling (Mitchell-Wallace - 2017)]]|Chapters 1–2, 4.2, 4.6, 5.2, 5.4.3 — used extensively across Assignments 1–4| |[[Catastrophe Modeling - A New Approach to Managing Risk (Grossi - 2005)]]|Chapters 1, 2.5.4.1, 4.3| |[[ABI Industry Good Practice for Catastrophe Modelling]]|Chapters 3–7, including Sections 1.5, 4.4.3, 4.4.4| |[[Uses of Catastrophe Model Output (AAA - 2018)]]|Extreme Events and Property Lines Committee monograph| |[[IAA Risk Book - Chapter 5 Catastrophe Risk]]|Section I: Underwriting and Pricing| |[[Quantifying the Source of Simulation Uncertainty in Natural Catastrophe Models (2017)]]|pp. 591–605| |[[Notes on Using Property Catastrophe Model Results (CAS - 2017)]]|CAS Forum, Spring 2017| |[[Catastrophe Analysis in AM Best Ratings]]|Rating methodology and questionnaire references| |[[Swiss Re Cat Modeling and Pricing Seminar (2011)]]|Reinsurance pricing via cat model output| |[[Modeling Fundamentals - Understanding Uncertainty]]|Uncertainty types in catastrophe modeling| |[[Cat Modeling Best Practices (2011)]]|Sections 5, 6, 9; users of catastrophe models| |[[IBC Handbook for Economic Capital Modelling]]|Section 9| |Study Notes (provided by Cat Risk Credentials)|Assignments 1–2 module-specific notes| **Online / Reference Readings** |Source|Topic| |---|---| |[AIR CEDE Open Source Data Schema](https://docs.air-worldwide.com/Database/CEDE/10.0/webframe.html#topic1.html)|Exposure and reference data| |[W3Schools SQL Tutorials](https://www.w3schools.com/sql/)|SELECT, WHERE, LIKE, JOIN, UPDATE, CAST, aggregations, GROUP BY, HAVING, NULL, stored procedures| |[ArcGIS Geocoding Process](http://desktop.arcgis.com/en/arcmap/10.3/guide-books/geocoding/the-geocoding-process.htm)|Geocoding process and major steps| |[GIS Geography — Geodetic Datums](https://gisgeography.com/geodetic-datums-nad27-nad83-wgs84/)|NAD27, NAD83, WGS84| |[RMS — Geocoding: The Underappreciated Science](https://www.rms.com/blog/2018/05/03/geocoding-the-underappreciated-science-of-catastrophe-modeling/)|Geocoding in catastrophe modeling| |[TechTarget — Relational Database Definition](https://searchdatamanagement.techtarget.com/definition/relational-database)|Relational database concepts| |[Guru99 — Star and Snowflake Schema](https://www.guru99.com/star-snowflake-data-warehousing.html)|Database design patterns| |[AWS — What Is a Data Lake?](https://aws.amazon.com/big-data/datalakes-and-analytics/what-is-a-data-lake/)|Data lake vs. data warehouse| |[Investopedia — COPE](https://www.investopedia.com/terms/c/cope-insurance.asp)|COPE data in insurance| |[AIR Unicede](https://unicede.air-worldwide.com/)|Exposure data standard| |[Insurance Journal — Rethinking Cat Modeling (2018)](https://www.insurancejournal.com/news/national/2018/07/16/495213.htm)|Event response and model performance|