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Exploring Algorithms in Insurance Reserving: From Chain Ladder to Regression Models

This informative text delves into the world of algorithms in insurance reserving, covering topics such as the Chain Ladder method, Regression models, utilization of Excel functions, and model design considerations. Learn about techniques, assumptions, and practical applications of algorithms in estimating reserves. Discover the benefits and challenges of using multiple algorithms and explore the interplay between parsimony and realism in model design. Gain insights into model validation techniques and general discussions on algorithm applications in the insurance industry.

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Exploring Algorithms in Insurance Reserving: From Chain Ladder to Regression Models

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Presentation Transcript

  1. Algorithms • What is an algorithm? • A planned sequence of calculations and decisions, basically a set of mathematical instructions • How can algorithms be used? • How can the Chain Ladder method be viewed as an algorithm?

  2. Regression • Mathematical technique used to estimate the parameters of a model • Simple case (line in 2 variables) Y=mX + b • Excel Functions • Function wizard

  3. Excel Regression Functions • SLOPE • INTERCEPT • STEYX • TREND • FORECAST • RSQ • LINEST

  4. Chain Ladder Method Assumptions • Linear Relationship of Incremental Losses (y=mx) • Linear relationship between incremental loss amounts and previous cumulative amount? Intercept=zero? • What does it mean if the assumption does not hold? • “Last three” selection implies a change • Does there appear to be a change? • What does this mean? • Loss development factors are uncorrelated • Do they appear correlated? • What are the implications for estimated reserves?

  5. Multiple Algorithms and Reserving • Why is reserve data organized into aggregated loss triangles? • What information is lost? • What are the advantages of using multiple algorithms? • What are the disadvantages of using multiple algorithms? • How much weight do you give to each?

  6. Lunch!

  7. Regression Models of Loss Development • Regression through the Origin Incremental Loss(y) =m*Previous Cumulative Paid Loss(x) • Regression with an intercept Incremental Loss(y) =m*Previous Cumulative Paid Loss(x) + b • Weighted Least Squares Incremental Loss(y) =m*Ultimate Loss(x) + b

  8. Model Design Considerations • Parsimony • Benefits • Pitfalls • Rank models used today • Realism • Rank models used today • How are Parsimony and Realism in conflict? • Modesty • Benefits • Robustness • Techniques for measuring • Techniques for improving

  9. Model Validation • Do the fitted values look like the actual values? • Does removing data points significantly impact the results?

  10. General Discussion

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