Analysis and Forecasting of Sales Volumes of the Regional Insurance Company: The Russian Experience
Bibliographic record
Abstract
The article describes the main types of insurance services provided by one of the largest insurance companies of Primorsky Krai in the border areas, determines the methods of analysis of insurance activity and analyzes the size of insurance premiums received during the period of 2012-2014 in order to develop forecasting and risk minimization models. The main value of the study lies with using a well-known methodology in the activities of the regional insurance organization in order to increase its effectiveness (adapting existing methods of forecasting time series with respect to forecasting sales volumes of the insurance company). Forecasting sales volumes using a Holt-Winters multiplicative model of exponential smoothing gives good results and a certain idea about the events of the future. The models obtained in the course of the study and data calculated on them can be used in the future to generate control and management of sales volumes in the regional insurance company, as well as to calculate the coefficients of the financial condition of the company, which will allow to track the decline in yield. Thus, the proposed algorithm for the analysis of sales volumes based on the methods of comparison, selection of bottlenecks, method of dynamic range and economic analysis allows to control the dynamics of sales and the development of measures that ensure its successful growth, which has practical relevance to the insurance company of any scale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".