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Record W4232344644 · doi:10.1002/9780470012505.tam025

Dynamic Financial Modeling of an Insurance Enterprise

2004· other· en· W4232344644 on OpenAlex
Mary R. Hardy

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEncyclopedia of Actuarial Science · 2004
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPortfolioBusinessActuarial scienceProfit (economics)Asset (computer security)FinanceComputer scienceEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

Abstract A dynamic financial insurance model is an integrated model of the assets and liabilities of an insurer. In the United Kingdom, these models may be called ‘model offices’. Modeling the insurer as a whole entity differs from individual policy or portfolio approaches to modeling in that it allows for interactions and correlations between portfolios, as well as incorporating the effect on the organization of decisions and strategies, which depend on the performance of the insurer as a whole. For example, profit distribution is most sensibly determined after consideration of the results from the different insurance portfolios that comprise the company, rather than at the individual portfolio level. The insurance enterprise model takes a holistic approach to asset–liability modeling, enabling synergies such as economies of scale, and whole insurer operations to be incorporated in the modeling process.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.222
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it