Loss Development Factors (LDFs) and Tail Factors
This dataset provides detailed loss development factors, including age-to-age, cumulative, and tail factors by insurance line of business and development period. It enables actuaries and analysts to project ultimate losses, assess reserve adequacy, and benchmark against industry standards, supporting robust financial and risk management decisions.
Sample rows
preview · 8 of 400 rows · all 10 columns| ldf_idinteger | factor_sourcestring | development_period_endinteger | line_of_businessstring | development_period_startinteger | age_to_age_factorfloat | cumulative_development_factorfloat | tail_factorfloat | effective_datedate | notesstring |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Internal Study | 12 | Auto | 0 | 1.87 | 1.87 | blank | 2022-09-15 | Initial auto claims, rapid early development observed. |
| 2 | Industry Benchmark | 12 | Property | 0 | 1.95 | 1.95 | blank | 2022-10-01 | Large property losses, catastrophe event start period. |
| 3 | External Peer Group | 12 | Liability | 1 | 2.01 | 2.01 | blank | 2022-10-25 | Boundary: Large commercial liability start. |
| 4 | Consultant Analysis | 24 | Health | 12 | 1.11 | 1.44 | blank | 2022-11-10 | Major medical, large claims development in year two. |
| 5 | Industry Benchmark | 24 | Auto | 12 | 1.08 | 1.52 | blank | 2022-12-02 | Includes both personal and fleet auto policies. |
| 6 | Internal Study | 24 | Property | 12 | 1.09 | 1.49 | blank | 2022-12-15 | Property claims, moderate development after first year. |
| 7 | Consultant Analysis | 24 | Liability | 12 | 1.13 | 1.54 | blank | 2023-01-05 | Liability claims, ongoing litigation effects. |
| 8 | External Peer Group | 36 | Health | 24 | 1.07 | 1.66 | blank | 2023-01-30 | Outlier: epidemic scenario, high volatility. |
| 9 | Internal Study | 36 | Auto | 24 | 1.04 | 1.4 | blank | 2023-02-10 | Third year development, settled claims impact. |
| 10 | Industry Benchmark | 36 | Property | 24 | 1.1 | 1.58 | blank | 2023-02-28 | Fire losses, claims stabilize after second year. |
| 11 | Actuarial Judgment | 36 | Liability | 24 | 1.13 | 1.55 | blank | 2023-03-15 | Legal settlements, late reporting effect. |
| 12 | Consultant Analysis | 48 | Health | 36 | 1.03 | 1.69 | blank | 2023-03-29 | Final period, aging insured population. |
| 13 | Industry Benchmark | 48 | Auto | 36 | 1.02 | 1.45 | blank | 2023-04-15 | Edge case: minimal late development. |
| 14 | Actuarial Judgment | 48 | Property | 36 | 1.01 | 1.53 | blank | 2023-04-30 | End of development, catastrophe-exposed. |
| 15 | Consultant Analysis | 48 | Liability | 36 | 1 | 1.58 | blank | 2023-05-11 | Long-tailed liability, decade-old claims. |
| 16 | External Peer Group | 60 | Health | 48 | 1.02 | 1.69 | 1.01 | 2023-06-01 | Final tail period, rare chronic claim scenario. |
| 17 | Industry Benchmark | 60 | Auto | 48 | 1 | 1.46 | 1.01 | 2023-06-14 | Edge: Last possible development period. |
| 18 | Actuarial Judgment | 60 | Property | 48 | 1.03 | 1.57 | 1.04 | 2023-07-01 | End period, hurricane-exposed portfolio. |
| 19 | Consultant Analysis | 60 | Liability | 48 | 1.01 | 1.58 | 1.07 | 2023-07-22 | Tail: environmental liability, latent claims. |
| 20 | Industry Benchmark | 12 | Health | 0 | 1.88 | 1.88 | blank | 2023-08-02 | Initial health claims, rapid reporting. |
What the 400 rows show
from the 400-row sampleActuarial Judgment (factor source) stands out: mean development_
- 36median development_
period_ end - 24median development_
period_ start - 1.1median age_
to_ age_ factor - 1.6median cumulative_
development_ factor - 1.1median tail_
factor
Median 36, from 6 to 72.
- string 3
- integer 3
- float 3
- date 1
Columns
10 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 3 columns | |||
line_of_business | string | Insurance line of business (e.g., Auto, Property, Liability) for which the development factors apply | Auto |
factor_source | string | Source or method used to derive the development factors (e.g., industry benchmark, internal study)5 sources · optional | Internal Study |
notes | string | Additional notes or comments regarding the factor set or its applicationoptional | Initial auto claims, rapi… |
| Numbers 6 columns | |||
ldf_id | integer | Unique identifier for each loss development factor recordunique · 1 or more | 1 |
development_period_start | integer | Starting age or period (in months or years) for the age-to-age development factor0 or more | 0 |
development_period_end | integer | Ending age or period (in months or years) for the age-to-age development factor0 or more | 12 |
age_to_age_factor | float | Age-to-age development factor for projecting losses from one period to the next0 or more | 1.87 |
cumulative_development_factor | float | Cumulative development factor from origin to the current development period0 or more | 1.87 |
tail_factor | float | Tail factor applied beyond the last observed development period to estimate ultimate losses0 or more · optional | 1.01 |
| Dates and times 1 column | |||
effective_date | date | Date when the development factors become effective or were last updatedoptional | 2022-09-15 |
Use it for
An insurance dashboard
Development_
period_ end by factor_ source and a breakdown of line_ of_ business. Excel, Power BI or Tableau. Why do the 51 Actuarial Judgment rows have a mean development_
period_ end of 50.1? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Rows400112Internal…212Industry…312External…
A software demo
Believable rows with line_
of_ business, development_ period_ start and development_ period_ end to fill a screen in front of a buyer.
blueprint · loss-development-factors-ldfs-and-tail-factors
Behind this dataset
Same schema. As many rows as you need.
These 400 rows came out of a blueprint — 10 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
- Age-to-age factor = Loss at age N+1 / Loss at age N
- Average or weighted average of age-to-age factors
- Selection of development factors based on credibility
- Volume-weighted vs simple average methods
- Cumulative LDF (CDF) to ultimate
- Tail factor for development beyond triangle
- Industry benchmarks for tail (e.g., workers comp 1.02-1.10)
- Shorter tail for property (tail near 1.00)
1 credit per row. New accounts start with 25 free credits.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- loss-development-factors-ldfs-and-tail-factors