knitr::opts_chunk$set(echo = TRUE)

library(tweedie)
library(cplm)
library(statmod)

library(rstan)
library(actuar)
library(fishMod)

library(ggplot2)
library(tidyr)
library(dplyr)
library(tibble)
# library(rbokeh)

library(openxlsx)
library(readr)
library(readxl)

library(knitr)
library(kableExtra)
# library(formattable)
library(RColorBrewer) # display.brewer.all()
# library(plotly)
# library(gapminder)

Disclaimer: This document is written by a Cal Insurance intern and is intended for the other members of the intern team as audience. We include detailed documentation and code in hopes of helping the intern better familiarize with working in R as well as open room for improvement moving forward. The analyses contained herein are for internal use within Cal Insurance only.

Completing the Data (Calculating Charged Premiums)

At the start of the project, we are given data from various tables. Consolidating these together into one table involves some simple index(...match(...)...) lines in Excel. This gives us a complete data table claims_history.csv, and all that remains is to calculate the filed premiums for each policyholder over each year from 2013 through 2018.

This can be automated in R in a real workplace setting if multiple .xlsx files are given with a common structure.

# # import base .csv
tbl <- as_tibble(read_csv("/Users/dsury/Dropbox/Actuary/proj/case-comp/GA/ga-csv/claims_history.csv")) # "https://files.dsury.com/claims_history.csv"

# quick kable for this document
qkable <- function(x, height="360px") {
  x %>% kable(format = "html") %>% kable_styling(bootstrap_options = c("condensed", "responsive", "striped", "hover", "bordered"), font_size = 11, position = "center") %>% scroll_box(width="100%", height= height, fixed_thead =  list(enabled = TRUE, background = "lightgrey") ) 
}

tbl[1:20,] %>% qkable()
Vehicle Number Policy Year Policy Number Marital Status Driver Age Driver Age Band Annual Mileage Territory Credit Score Car Value Car Value Band Liability Limit Physical Damage Deductible Reported Losses Late Fees Conviction Points Base Pure Premium Marital Status Relativity Driver Age Relativity Annual Mileage Relativity Territory Relativity Car Value Relativity Liability Limit Relativity Physical Damage Deductible Relativity Multi-Car Multi-Car Relativity Accident Points (AP) last 3 years AP Relativity Conviction Points (CP) last 3 years CP Relativity
6125 2010 1 Single 18 16-19 0-7500 4 High 3220 0-10000 5e+04 100 0 0 1 1500 1.2 3.0 0.7 0.8 0.720 0.60 1.080 2 0.9 -1 -1 -1 -1
10332 2010 1 Married 45 40-49 7500-10000 3 High 20090 20000-30000 5e+05 250 0 0 0 1500 0.8 0.9 0.9 0.9 1.000 1.12 1.045 1 1.0 -1 -1 -1 -1
1965 2010 2 Married 54 50-59 15000+ 1 Medium 13810 10000-20000 1e+05 500 0 0 0 1500 0.8 0.8 1.2 1.2 0.880 0.88 1.000 2 0.9 -1 -1 -1 -1
14130 2010 2 Married 49 40-49 10000-15000 1 High 37060 30000-40000 1e+05 100 0 0 1 1500 0.8 0.9 1.0 1.2 1.045 0.88 1.080 1 1.0 -1 -1 -1 -1
2976 2010 3 Single 31 30-39 7500-10000 4 High 20860 20000-30000 5e+04 250 0 0 0 1500 1.2 1.0 0.9 0.8 1.000 0.60 1.045 1 1.0 -1 -1 -1 -1
76 2010 4 Married 53 50-59 15000+ 2 High 80850 40000+ 5e+04 100 0 0 0 1500 0.8 0.8 1.2 1.1 1.080 0.60 1.080 1 1.0 -1 -1 -1 -1
8571 2010 5 Single 21 20-24 0-7500 3 Low 5610 0-10000 1e+05 500 0 0 0 1500 1.2 2.0 0.7 0.9 0.720 0.88 1.000 1 1.0 -1 -1 -1 -1
8530 2010 6 Single 28 25-29 7500-10000 2 High 20000 20000-30000 5e+04 250 0 0 0 1500 1.2 1.5 0.9 1.1 1.000 0.60 1.045 1 1.0 -1 -1 -1 -1
6104 2010 7 Single 33 30-39 0-7500 1 High 13020 10000-20000 5e+04 500 0 0 0 1500 1.2 1.0 0.7 1.2 0.880 0.60 1.000 2 0.9 -1 -1 -1 -1
12468 2010 7 Single 27 25-29 0-7500 2 High 26350 20000-30000 1e+05 250 0 0 0 1500 1.2 1.5 0.7 1.1 1.000 0.88 1.045 1 1.0 -1 -1 -1 -1
1547 2010 8 Married 52 50-59 0-7500 4 Low 39180 30000-40000 5e+05 1000 0 0 0 1500 0.8 0.8 0.7 0.8 1.045 1.12 0.935 1 1.0 -1 -1 -1 -1
736 2010 9 Married 49 40-49 10000-15000 4 Medium 30150 30000-40000 1e+05 500 0 0 0 1500 0.8 0.9 1.0 0.8 1.045 0.88 1.000 3 0.8 -1 -1 -1 -1
12236 2010 9 Single 50 50-59 0-7500 4 Medium 22600 20000-30000 5e+04 100 0 0 0 1500 1.2 0.8 0.7 0.8 1.000 0.60 1.080 2 0.9 -1 -1 -1 -1
14398 2010 9 Single 47 40-49 0-7500 3 Medium 28950 20000-30000 5e+04 1000 0 0 0 1500 1.2 0.9 0.7 0.9 1.000 0.60 0.935 1 1.0 -1 -1 -1 -1
6386 2010 10 Single 16 16-19 7500-10000 2 Low 1020 0-10000 5e+04 100 0 0 0 1500 1.2 3.0 0.9 1.1 0.720 0.60 1.080 1 1.0 -1 -1 -1 -1
5284 2010 11 Married 27 25-29 7500-10000 3 High 1270 0-10000 1e+05 250 0 0 0 1500 0.8 1.5 0.9 0.9 0.720 0.88 1.045 1 1.0 -1 -1 -1 -1
999 2010 12 Married 65 60-69 10000-15000 3 Medium 62030 40000+ 1e+05 500 0 0 0 1500 0.8 0.8 1.0 0.9 1.080 0.88 1.000 1 1.0 -1 -1 -1 -1
1553 2010 13 Single 37 30-39 15000+ 2 High 39430 30000-40000 3e+05 500 0 0 0 1500 1.2 1.0 1.2 1.1 1.045 1.00 1.000 1 1.0 -1 -1 -1 -1
8480 2010 14 Single 20 20-24 7500-10000 3 High 23570 20000-30000 5e+04 100 0 0 0 1500 1.2 2.0 0.9 0.9 1.000 0.60 1.080 2 0.9 -1 -1 -1 -1
10430 2010 14 Single 51 50-59 10000-15000 2 High 11020 10000-20000 1e+05 250 0 0 0 1500 1.2 0.8 1.0 1.1 0.880 0.88 1.045 1 1.0 -1 -1 -1 -1

Setting (Ordered) Factor Levels

Before going further into exploratory data analysis, we must treat our categorical variables (bands and groups) as factors in R. For our ggplots and GLM to function as desired, we assign orders to the variables.

# set factor levels and order

tbl$`Marital Status` <- factor(
  tbl$`Marital Status`, levels= sort(unique(tbl$`Marital Status`), decreasing = TRUE), ordered = TRUE # 1 : single, 2 : married
)

tbl <- tbl %>% mutate(`Driver Experience` = `Driver Age` - 15) # gives ceiling of driver experience years, includes instructional period prior to license

tbl <- tbl %>% mutate(`Driver Experience Band` = `Driver Age Band` )

tbl$`Driver Experience Band` <- recode(tbl$`Driver Experience Band`, 
       `16-19` = "0-3",
       `20-24` = "4-8",
       `25-29` = "9-13",
       `30-39` = "14-23",
       `40-49` = "24-33",
       `50-59` = "34-43",
       `60-69` = "44-53",
       `70-79` = "54-63",
       `80-89` = "64-73",
         `90+` = "74+"
       )

tbl$`Driver Experience Band` <- factor(
  tbl$`Driver Experience Band`, levels = c("0-3", "4-8", "9-13", "14-23", "24-33", "34-43", "44-53", "54-63", "64-73", "74+"), ordered = TRUE
)

tbl$`Annual Mileage` <- factor(
  tbl$`Annual Mileage`, levels = c("0-7500", "7500-10000", "10000-15000", "15000+"), ordered = TRUE
)

tbl$`Credit Score` <- factor(
  tbl$`Credit Score`, levels = c("Low", "Medium", "High"), ordered = TRUE
)

tbl$`Car Value Band` <- factor(
  tbl$`Car Value Band`, levels = c("0-10000", "10000-20000", "20000-30000", "30000-40000", "40000+"), ordered = TRUE
)


tbl$`Policy Year` <- factor(
  tbl$`Policy Year`, levels = as.character(2010:2020), ordered = TRUE
) # maximum is 2018, but we'll go to 2020 here


tbl$`Conviction Points (CP) last 3 years` <- factor(
  tbl$`Conviction Points (CP) last 3 years`, levels = 0:3, ordered = TRUE
)

tbl$`Accident Points (AP) last 3 years` <- factor(
  tbl$`Accident Points (AP) last 3 years`, levels = 0:3, ordered = TRUE
)

tbl$Territory <- factor(
  tbl$Territory, levels = 1:4, ordered = FALSE # make un-ordered to allow permutation in GLM
)

Assuming we are in California (Cal Insurance), for regulatory compliance, we know that the filed premiums use certain relativities and exclude certain information. Particularly, California’s Proposition 103 requires that we calculate rates via sequential analysis using a primary set of three factors:

Primary Auto Rating Factors

  1. Driver Safety Record
  2. Annual Mileage
  3. Years Licensed (Driving Experience)

These above 3 factors must be used before considering other variables from the following prescribed list of “optional factors”:

Optional Factors

  1. Type of vehicle
  2. Vehicle performance capabilities
  3. Type of use of vehicle (pleasure only, commute, business, farm, etc)
  4. Percentage use of the vehicle by the rated driver
  5. Multi-vehicle households
  6. Academic standing
  7. Completion of driver training or defensive driver courses
  8. Vehicle characteristics (engine size, safety devices, theft deterrent devices, etc)
  9. Marital status
  10. Persistency
  11. Non-smoker
  12. Secondary Driver Characteristics (combination of: Safety Record, Years Licensed, Marital Status, Driver Training, and Academic Status)
  13. Multi-policies with the same or affiliated company
  14. Relative claims frequency (maximum of 20 categories/bands)
  15. Relative claims severity (maximum of 20 categories/bands)

Notice that gender, Credit Score, and driver’s age are not on this prescribed list and hence cannot be used as rating factors. However, with Driver Age, we can transform this easily into Driving Experience with the simplifying assumption that all registered policyholders begin driving at age 16. We exercise actuarial judgment here that although this is a biased transformation, it is the simplest and most transparent.

Because our later analyses are not applied to any immediate compliance filings, we use Driver Age and Driver Experience interchangeably as they are equivalent (simply offset by -16) and this variable does not involve the above assumption.

Technically, there is a mandatory “Good Driver Discount” (GDD) of at least \(20\%\) compared to “the lowest rate available to a comparable driver who is not a good driver.” Moreover, the calculation of factor weights is also regulated. Lacking information on these specifications, we choose not to make assumptions and modifications here.

As given by insurance.ca.gov, for Sequential Analysis via the Prior Relativities Method under the Multiplicative Algorithm, we have:

\[ \text{Premium} = \text{(Base Pure Premium)} \times F_1 \times F_2 \times F_3 \times GDD \times F_4 \times \cdots \times F_k\] where \(F_1, F_2, F_3\) are the relativites for the first, second and third factors in sequential analysis, respectively, all the way until the \(k\)th.

Ratemaking is prospective, meaning that we are estimating future claims and not recouping for past losses. Because we are only interested in the historical losses versus charged premiums, for our purposes we need not reinvent the Sequential Analysis technique used to arrive at the given values of relativities.

Recall the balancing fundamental insurance equation:

\[ \text{Premium} = \text{Loss} + \text{LAE} + \text{UW Expenses} + \text{UW Profit}. \]

In Excel, to get the Premium per policy, we first calculate the premium per vehicle for each year via:

\[ \begin{align*} \text{Premium} &= \text{(Base Pure Premium)} \times \text{[(3 yr Accident Points)} \times \text{(3 yr Conviction Points)]} \\ & \qquad \times \text{(Annual Mileage)} \times \text{(Driving Experience)} \times \text{(Physical Damage Deductible)} \\ & \qquad \times \text{(Liability Limit)} \times \text{(Car Value)} \times \text{(Multi-Car)} \times \text{(Marital Status)} \times \text{(Territory)} \end{align*} \]

where each factor multiplied to Base Pure Premium is the relativity of that factor.

Here’s an exhibit of the filed prices, loaded in from doing inputting the above formula into Excel. Notice these are adjustments to the Base Premium.

prems <- as_tibble(read_csv("Dropbox/Actuary/proj/case-comp/GA/ga-csv/filed_premiums.csv")) # "https://files.dsury.com/filed_premiums.csv"
prems[1:20,] %>% qkable()
Policy Number Vehicle Number Premium Policy Year Marital Status Driver Age Driver Age Band Annual Mileage Territory Credit Score Car Value Car Value Band Liability Limit Physical Damage Deductible Reported Losses Late Fees Conviction Points Base Pure Premium Marital Status Relativity Driver Age Relativity Annual Mileage Relativity Territory Relativity Car Value Relativity Liability Limit Relativity Physical Damage Deductible Relativity Multi-Car Multi-Car Relativity Accident Points (AP) last 3 years AP Relativity Conviction Points (CP) last 3 years CP Relativity Driver Experience Driver Experience Band
1 6125 1218.9981 2013 Single 21 20-24 0-7500 4 High 3220 0-10000 5e+04 100 200.0692 0 0 1500 1.2 2.0 0.7 0.8 0.720 0.60 1.080 2 0.9 1 1.2 1 1.2 6 4-8
1 10332 1023.8659 2013 Married 48 40-49 7500-10000 3 High 20090 20000-30000 5e+05 250 0.0000 0 0 1500 0.8 0.9 0.9 0.9 1.000 1.12 1.045 1 1.0 0 1.0 0 1.0 33 24-33
2 1965 963.4775 2013 Married 57 50-59 15000+ 1 Medium 13810 10000-20000 1e+05 500 0.0000 0 0 1500 0.8 0.8 1.2 1.2 0.880 0.88 1.000 2 0.9 0 1.0 0 1.0 42 34-43
2 14130 1144.1295 2013 Married 52 50-59 10000-15000 1 High 37060 30000-40000 1e+05 100 0.0000 0 0 1500 0.8 0.8 1.0 1.2 1.045 0.88 1.080 1 1.0 0 1.0 0 1.0 37 34-43
3 2976 812.5920 2013 Single 34 30-39 7500-10000 4 High 20860 20000-30000 5e+04 250 0.0000 0 0 1500 1.2 1.0 0.9 0.8 1.000 0.60 1.045 1 1.0 0 1.0 0 1.0 19 14-23
4 76 886.8372 2013 Married 56 50-59 15000+ 2 High 80850 40000+ 5e+04 100 0.0000 0 0 1500 0.8 0.8 1.2 1.1 1.080 0.60 1.080 1 1.0 0 1.0 0 1.0 41 34-43
5 8571 2069.2869 2013 Single 24 20-24 0-7500 3 Low 5610 0-10000 1e+05 500 10121.7588 0 1 1500 1.2 2.0 0.7 0.9 0.720 0.88 1.000 1 1.0 1 1.2 1 1.2 9 4-8
6 8530 1608.9322 2013 Single 31 30-39 7500-10000 2 High 20000 20000-30000 5e+04 250 28602.5937 1 1 1500 1.2 1.0 0.9 1.1 1.000 0.60 1.045 1 1.0 1 1.2 1 1.2 16 14-23
7 6104 718.5024 2013 Single 36 30-39 0-7500 1 High 13020 10000-20000 5e+04 500 0.0000 0 0 1500 1.2 1.0 0.7 1.2 0.880 0.60 1.000 2 0.9 0 1.0 0 1.0 21 14-23
7 12468 1784.3918 2013 Single 30 30-39 0-7500 2 High 26350 20000-30000 1e+05 250 0.0000 0 1 1500 1.2 1.0 0.7 1.1 1.000 0.88 1.045 1 1.0 0 1.0 2 1.4 15 14-23
8 1547 588.3086 2013 Married 55 50-59 0-7500 4 Low 39180 30000-40000 5e+05 1000 0.0000 0 0 1500 0.8 0.8 0.7 0.8 1.045 1.12 0.935 1 1.0 0 1.0 0 1.0 40 34-43
9 736 565.0022 2013 Married 52 50-59 10000-15000 4 Medium 30150 30000-40000 1e+05 500 0.0000 0 0 1500 0.8 0.8 1.0 0.8 1.045 0.88 1.000 3 0.8 0 1.0 0 1.0 37 34-43
9 12236 470.2925 2013 Single 53 50-59 0-7500 4 Medium 22600 20000-30000 5e+04 100 0.0000 0 0 1500 1.2 0.8 0.7 0.8 1.000 0.60 1.080 2 0.9 0 1.0 0 1.0 38 34-43
9 14398 508.9392 2013 Single 50 50-59 0-7500 3 Medium 28950 20000-30000 5e+04 1000 0.0000 0 0 1500 1.2 0.8 0.7 0.9 1.000 0.60 0.935 1 1.0 0 1.0 0 1.0 35 34-43
10 6386 3990.7676 2013 Single 19 16-19 7500-10000 2 Low 1020 0-10000 5e+04 100 6483.2473 0 0 1500 1.2 3.0 0.9 1.1 0.720 0.60 1.080 1 1.0 3 1.6 0 1.0 4 0-3
11 5284 772.2874 2013 Married 30 30-39 7500-10000 3 High 1270 0-10000 1e+05 250 0.0000 0 0 1500 0.8 1.0 0.9 0.9 0.720 0.88 1.045 1 1.0 0 1.0 1 1.2 15 14-23
12 999 821.1456 2013 Married 68 60-69 10000-15000 3 Medium 62030 40000+ 1e+05 500 0.0000 0 0 1500 0.8 0.8 1.0 0.9 1.080 0.88 1.000 1 1.0 0 1.0 0 1.0 53 44-53
13 1553 2234.6280 2013 Single 40 40-49 15000+ 2 High 39430 30000-40000 3e+05 500 0.0000 0 0 1500 1.2 0.9 1.2 1.1 1.045 1.00 1.000 1 1.0 0 1.0 0 1.0 25 24-33
14 8480 1700.6112 2013 Single 23 20-24 7500-10000 3 High 23570 20000-30000 5e+04 100 0.0000 0 0 1500 1.2 2.0 0.9 0.9 1.000 0.60 1.080 2 0.9 0 1.0 0 1.0 8 4-8
14 10430 1538.2186 2013 Single 54 50-59 10000-15000 2 High 11020 10000-20000 1e+05 250 0.0000 0 0 1500 1.2 0.8 1.0 1.1 0.880 0.88 1.045 1 1.0 0 1.0 1 1.2 39 34-43

Before we look into the premium structure, we’ll take a quick detour to understand more about the data and the factors.

1. Data Cleaning: Validation and Investigation

Here we’ll perform a pivot on our data. Our given data is very ‘tall’, with repeated entries. We notice that the Vehicle Number entries are periodic, where we have \[135,000 \text{ total rows of data} = 15,000 \text{ rows} \times 9 \text{ years}.\] Because we’ve sorted by Policy Year (PY) and Policy Number, each ‘block’ of 15000 rows corresponds to a year (we confirm this in data validation, but this will be evident in a moment). We’ll break these apart with pivot_wider().

# just run once

# rearrange to compare years
# select(tbl, `Vehicle Number`, `Policy Year`, `Marital Status` )
if  (names(tbl)[2] != "Policy Year") { print("Error: Make sure tbl has Policy Year as second column!") }

# tblnames <- c()

# generate tibbles
for (i in names(tbl)[- c(1,2) ]) {
  assign( paste0("tbl.",i), 
          select(tbl, `Vehicle Number`, `Policy Year`, i) %>%
            pivot_wider(names_from = "Policy Year", 
                        values_from = i,
                        names_prefix = paste0(i, " PY"))
          )
  # tblnames <- c(tblnames, paste0("tbl.",i))
  print(i) # debugging
}

This gives us a number of tables, one for each original column. A couple of examples is shown below. Notice that each row no longer has the Policy Year column, and now each row contains data for each year (as opposed to multiple rows before corresponding to the same Vehicle Number).

Now we’ll stitch these together back into a consolidated database. We’ll generally keep the same order as given in the original .xlsx file.

for (k in tblcols) {
  tbl.wider <- tbl.wider %>% left_join( k )
}
tbl.wider[1:20,] %>% qkable()
Vehicle Number Policy Number PY2010 Policy Number PY2011 Policy Number PY2012 Policy Number PY2013 Policy Number PY2014 Policy Number PY2015 Policy Number PY2016 Policy Number PY2017 Policy Number PY2018 Marital Status PY2010 Marital Status PY2011 Marital Status PY2012 Marital Status PY2013 Marital Status PY2014 Marital Status PY2015 Marital Status PY2016 Marital Status PY2017 Marital Status PY2018 Driver Age PY2010 Driver Age PY2011 Driver Age PY2012 Driver Age PY2013 Driver Age PY2014 Driver Age PY2015 Driver Age PY2016 Driver Age PY2017 Driver Age PY2018 Driver Age Band PY2010 Driver Age Band PY2011 Driver Age Band PY2012 Driver Age Band PY2013 Driver Age Band PY2014 Driver Age Band PY2015 Driver Age Band PY2016 Driver Age Band PY2017 Driver Age Band PY2018 Annual Mileage PY2010 Annual Mileage PY2011 Annual Mileage PY2012 Annual Mileage PY2013 Annual Mileage PY2014 Annual Mileage PY2015 Annual Mileage PY2016 Annual Mileage PY2017 Annual Mileage PY2018 Territory PY2010 Territory PY2011 Territory PY2012 Territory PY2013 Territory PY2014 Territory PY2015 Territory PY2016 Territory PY2017 Territory PY2018 Credit Score PY2010 Credit Score PY2011 Credit Score PY2012 Credit Score PY2013 Credit Score PY2014 Credit Score PY2015 Credit Score PY2016 Credit Score PY2017 Credit Score PY2018 Car Value PY2010 Car Value PY2011 Car Value PY2012 Car Value PY2013 Car Value PY2014 Car Value PY2015 Car Value PY2016 Car Value PY2017 Car Value PY2018 Car Value Band PY2010 Car Value Band PY2011 Car Value Band PY2012 Car Value Band PY2013 Car Value Band PY2014 Car Value Band PY2015 Car Value Band PY2016 Car Value Band PY2017 Car Value Band PY2018 Liability Limit PY2010 Liability Limit PY2011 Liability Limit PY2012 Liability Limit PY2013 Liability Limit PY2014 Liability Limit PY2015 Liability Limit PY2016 Liability Limit PY2017 Liability Limit PY2018 Physical Damage Deductible PY2010 Physical Damage Deductible PY2011 Physical Damage Deductible PY2012 Physical Damage Deductible PY2013 Physical Damage Deductible PY2014 Physical Damage Deductible PY2015 Physical Damage Deductible PY2016 Physical Damage Deductible PY2017 Physical Damage Deductible PY2018 Reported Losses PY2010 Reported Losses PY2011 Reported Losses PY2012 Reported Losses PY2013 Reported Losses PY2014 Reported Losses PY2015 Reported Losses PY2016 Reported Losses PY2017 Reported Losses PY2018 Late Fees PY2010 Late Fees PY2011 Late Fees PY2012 Late Fees PY2013 Late Fees PY2014 Late Fees PY2015 Late Fees PY2016 Late Fees PY2017 Late Fees PY2018 Conviction Points PY2010 Conviction Points PY2011 Conviction Points PY2012 Conviction Points PY2013 Conviction Points PY2014 Conviction Points PY2015 Conviction Points PY2016 Conviction Points PY2017 Conviction Points PY2018 Base Pure Premium PY2010 Base Pure Premium PY2011 Base Pure Premium PY2012 Base Pure Premium PY2013 Base Pure Premium PY2014 Base Pure Premium PY2015 Base Pure Premium PY2016 Base Pure Premium PY2017 Base Pure Premium PY2018 Marital Status Relativity PY2010 Marital Status Relativity PY2011 Marital Status Relativity PY2012 Marital Status Relativity PY2013 Marital Status Relativity PY2014 Marital Status Relativity PY2015 Marital Status Relativity PY2016 Marital Status Relativity PY2017 Marital Status Relativity PY2018 Driver Age Relativity PY2010 Driver Age Relativity PY2011 Driver Age Relativity PY2012 Driver Age Relativity PY2013 Driver Age Relativity PY2014 Driver Age Relativity PY2015 Driver Age Relativity PY2016 Driver Age Relativity PY2017 Driver Age Relativity PY2018 Annual Mileage Relativity PY2010 Annual Mileage Relativity PY2011 Annual Mileage Relativity PY2012 Annual Mileage Relativity PY2013 Annual Mileage Relativity PY2014 Annual Mileage Relativity PY2015 Annual Mileage Relativity PY2016 Annual Mileage Relativity PY2017 Annual Mileage Relativity PY2018 Car Value Relativity PY2010 Car Value Relativity PY2011 Car Value Relativity PY2012 Car Value Relativity PY2013 Car Value Relativity PY2014 Car Value Relativity PY2015 Car Value Relativity PY2016 Car Value Relativity PY2017 Car Value Relativity PY2018 Liability Limit Relativity PY2010 Liability Limit Relativity PY2011 Liability Limit Relativity PY2012 Liability Limit Relativity PY2013 Liability Limit Relativity PY2014 Liability Limit Relativity PY2015 Liability Limit Relativity PY2016 Liability Limit Relativity PY2017 Liability Limit Relativity PY2018 Physical Damage Deductible Relativity PY2010 Physical Damage Deductible Relativity PY2011 Physical Damage Deductible Relativity PY2012 Physical Damage Deductible Relativity PY2013 Physical Damage Deductible Relativity PY2014 Physical Damage Deductible Relativity PY2015 Physical Damage Deductible Relativity PY2016 Physical Damage Deductible Relativity PY2017 Physical Damage Deductible Relativity PY2018 Multi-Car PY2010 Multi-Car PY2011 Multi-Car PY2012 Multi-Car PY2013 Multi-Car PY2014 Multi-Car PY2015 Multi-Car PY2016 Multi-Car PY2017 Multi-Car PY2018 Multi-Car Relativity PY2010 Multi-Car Relativity PY2011 Multi-Car Relativity PY2012 Multi-Car Relativity PY2013 Multi-Car Relativity PY2014 Multi-Car Relativity PY2015 Multi-Car Relativity PY2016 Multi-Car Relativity PY2017 Multi-Car Relativity PY2018 Accident Points (AP) last 3 years PY2010 Accident Points (AP) last 3 years PY2011 Accident Points (AP) last 3 years PY2012 Accident Points (AP) last 3 years PY2013 Accident Points (AP) last 3 years PY2014 Accident Points (AP) last 3 years PY2015 Accident Points (AP) last 3 years PY2016 Accident Points (AP) last 3 years PY2017 Accident Points (AP) last 3 years PY2018 AP Relativity PY2010 AP Relativity PY2011 AP Relativity PY2012 AP Relativity PY2013 AP Relativity PY2014 AP Relativity PY2015 AP Relativity PY2016 AP Relativity PY2017 AP Relativity PY2018 Conviction Points (CP) last 3 years PY2010 Conviction Points (CP) last 3 years PY2011 Conviction Points (CP) last 3 years PY2012 Conviction Points (CP) last 3 years PY2013 Conviction Points (CP) last 3 years PY2014 Conviction Points (CP) last 3 years PY2015 Conviction Points (CP) last 3 years PY2016 Conviction Points (CP) last 3 years PY2017 Conviction Points (CP) last 3 years PY2018 CP Relativity PY2010 CP Relativity PY2011 CP Relativity PY2012 CP Relativity PY2013 CP Relativity PY2014 CP Relativity PY2015 CP Relativity PY2016 CP Relativity PY2017 CP Relativity PY2018 Driver Experience PY2010 Driver Experience PY2011 Driver Experience PY2012 Driver Experience PY2013 Driver Experience PY2014 Driver Experience PY2015 Driver Experience PY2016 Driver Experience PY2017 Driver Experience PY2018 Driver Experience Band PY2010 Driver Experience Band PY2011 Driver Experience Band PY2012 Driver Experience Band PY2013 Driver Experience Band PY2014 Driver Experience Band PY2015 Driver Experience Band PY2016 Driver Experience Band PY2017 Driver Experience Band PY2018
6125 1 1 1 1 1 1 1 1 1 Single Single Single Single Single Single Single Single Single 18 19 20 21 22 23 24 25 26 16-19 16-19 20-24 20-24 20-24 20-24 20-24 25-29 25-29 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 4 4 4 4 4 4 4 4 4 High High High High High High High High High 3220 3220 3220 3220 3220 3220 3220 3220 3220 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 100 100 100 100 100 100 100 100 100 0 0.000 0.0000 200.0692 0.0000 0.0000 12602.418 0.000 0.000 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 3.0 3.0 2.0 2.0 2.0 2.0 2.0 1.5 1.5 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.720 0.720 0.720 0.720 0.720 0.720 0.720 0.720 0.720 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 2 2 2 2 2 2 2 2 2 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 -1 -1 0 1 1 1 1 1 1 -1 -1 1.0 1.2 1.2 1.2 1.2 1.2 1.2 -1 -1 2 1 0 0 0 0 0 -1 -1 1.4 1.2 1.0 1.0 1.0 1.0 1 3 4 5 6 7 8 9 10 11 0-3 0-3 4-8 4-8 4-8 4-8 4-8 9-13 9-13
10332 1 1 1 1 1 1 1 1 1 Married Married Married Married Married Married Married Married Married 45 46 47 48 49 50 51 52 53 40-49 40-49 40-49 40-49 40-49 50-59 50-59 50-59 50-59 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 3 3 3 3 3 3 3 3 3 High High High High High High High High High 20090 20090 20090 20090 20090 20090 20090 20090 20090 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 5e+05 5e+05 5e+05 5e+05 5e+05 5e+05 5e+05 5e+05 5e+05 250 250 250 250 250 250 250 250 250 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1 1 1 1 1 1 1 1 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 1 1 1 0 0 -1 -1 1.0 1.0 1.2 1.2 1.2 1.0 1 30 31 32 33 34 35 36 37 38 24-33 24-33 24-33 24-33 24-33 34-43 34-43 34-43 34-43
1965 2 2 2 2 2 2 2 2 2 Married Married Married Married Married Married Married Married Married 54 55 56 57 58 59 60 61 62 50-59 50-59 50-59 50-59 50-59 50-59 60-69 60-69 60-69 15000+ 15000+ 15000+ 15000+ 15000+ 15000+ 15000+ 15000+ 15000+ 1 1 1 1 1 1 1 1 1 Medium Medium Medium Medium Medium Medium Medium Medium Medium 13810 13810 13810 13810 13810 13810 13810 13810 13810 10000-20000 10000-20000 10000-20000 10000-20000 10000-20000 10000-20000 10000-20000 10000-20000 10000-20000 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 500 500 500 500 500 500 500 500 500 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 0.880 0.880 0.880 0.880 0.880 0.880 0.880 0.880 0.880 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 2 2 2 2 2 2 2 2 2 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 39 40 41 42 43 44 45 46 47 34-43 34-43 34-43 34-43 34-43 34-43 44-53 44-53 44-53
14130 2 2 2 2 2 2 2 2 2 Married Married Married Married Married Married Married Married Married 49 50 51 52 53 54 55 56 57 40-49 50-59 50-59 50-59 50-59 50-59 50-59 50-59 50-59 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 1 1 1 1 1 1 1 1 1 High High High High High High High High High 37060 37060 37060 37060 37060 37060 37060 37060 37060 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 100 100 100 100 100 100 100 100 100 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1 1 1 1 1 1 1 1 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 1 0 0 0 0 0 0 -1 -1 1.2 1.0 1.0 1.0 1.0 1.0 1 34 35 36 37 38 39 40 41 42 24-33 34-43 34-43 34-43 34-43 34-43 34-43 34-43 34-43
2976 3 3 3 3 3 3 3 3 3 Single Single Single Single Single Single Single Single Single 31 32 33 34 35 36 37 38 39 30-39 30-39 30-39 30-39 30-39 30-39 30-39 30-39 30-39 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 4 4 4 4 4 4 4 4 4 High High High High High High High High High 20860 20860 20860 20860 20860 20860 20860 20860 20860 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 250 250 250 250 250 250 250 250 250 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1 1 1 1 1 1 1 1 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 16 17 18 19 20 21 22 23 24 14-23 14-23 14-23 14-23 14-23 14-23 14-23 14-23 14-23
76 4 4 4 4 4 4 4 4 4 Married Married Married Married Married Married Married Married Married 53 54 55 56 57 58 59 60 61 50-59 50-59 50-59 50-59 50-59 50-59 50-59 60-69 60-69 15000+ 15000+ 15000+ 15000+ 15000+ 15000+ 15000+ 15000+ 15000+ 2 2 2 2 2 2 2 2 2 High High High High High High High High High 80850 80850 80850 80850 80850 80850 80850 80850 80850 40000+ 40000+ 40000+ 40000+ 40000+ 40000+ 40000+ 40000+ 40000+ 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 100 100 100 100 100 100 100 100 100 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1 1 1 1 1 1 1 1 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 38 39 40 41 42 43 44 45 46 34-43 34-43 34-43 34-43 34-43 34-43 34-43 44-53 44-53
8571 5 5 5 5 5 5 5 5 5 Single Single Single Single Single Single Single Single Single 21 22 23 24 25 26 27 28 29 20-24 20-24 20-24 20-24 25-29 25-29 25-29 25-29 25-29 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 3 3 3 3 3 3 3 3 3 Low Low Low Low Low Low Low Low Low 5610 5610 5610 5610 5610 5610 5610 5610 5610 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 500 500 500 500 500 500 500 500 500 0 0.000 0.0000 10121.7588 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 2.0 2.0 2.0 2.0 1.5 1.5 1.5 1.5 1.5 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.720 0.720 0.720 0.720 0.720 0.720 0.720 0.720 0.720 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1 1 1 1 1 1 1 1 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 1 1 1 0 0 0 -1 -1 1.0 1.2 1.2 1.2 1.0 1.0 1.0 -1 -1 0 1 1 1 0 0 0 -1 -1 1.0 1.2 1.2 1.2 1.0 1.0 1 6 7 8 9 10 11 12 13 14 4-8 4-8 4-8 4-8 9-13 9-13 9-13 9-13 9-13
8530 6 6 6 6 6 6 6 6 6 Single Single Single Single Single Single Single Single Single 28 29 30 31 32 33 34 35 36 25-29 25-29 30-39 30-39 30-39 30-39 30-39 30-39 30-39 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 2 2 2 2 2 2 2 2 2 High High High High High High High High High 20000 20000 20000 20000 20000 20000 20000 20000 20000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 250 250 250 250 250 250 250 250 250 0 0.000 0.0000 28602.5937 0.0000 0.0000 0.000 0.000 70200.000 0 2 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.5 1.5 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1 1 1 1 1 1 1 1 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 1 1 1 0 0 1 -1 -1 1.0 1.2 1.2 1.2 1.0 1.0 1.2 -1 -1 0 1 1 2 1 1 0 -1 -1 1.0 1.2 1.2 1.4 1.2 1.2 1 13 14 15 16 17 18 19 20 21 9-13 9-13 14-23 14-23 14-23 14-23 14-23 14-23 14-23
6104 7 7 7 7 7 7 7 7 7 Single Single Single Single Single Single Single Single Single 33 34 35 36 37 38 39 40 41 30-39 30-39 30-39 30-39 30-39 30-39 30-39 40-49 40-49 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 1 1 1 1 1 1 1 1 1 High High High High High High High High High 13020 13020 13020 13020 13020 13020 13020 13020 13020 10000-20000 10000-20000 10000-20000 10000-20000 10000-20000 10000-20000 10000-20000 10000-20000 10000-20000 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 500 500 500 500 500 500 500 500 500 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.9 0.9 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.880 0.880 0.880 0.880 0.880 0.880 0.880 0.880 0.880 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 2 2 2 2 2 2 2 2 2 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 18 19 20 21 22 23 24 25 26 14-23 14-23 14-23 14-23 14-23 14-23 14-23 24-33 24-33
12468 7 7 7 7 7 7 7 7 7 Single Single Single Single Single Single Single Single Single 27 28 29 30 31 32 33 34 35 25-29 25-29 25-29 30-39 30-39 30-39 30-39 30-39 30-39 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 2 2 2 2 2 2 2 2 2 High High High High High High High High High 26350 26350 26350 26350 26350 26350 26350 26350 26350 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 250 250 250 250 250 250 250 250 250 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.5 1.5 1.5 1.0 1.0 1.0 1.0 1.0 1.0 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1 1 1 1 1 1 1 1 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 1 2 2 2 1 0 0 -1 -1 1.2 1.4 1.4 1.4 1.2 1.0 1 12 13 14 15 16 17 18 19 20 9-13 9-13 9-13 14-23 14-23 14-23 14-23 14-23 14-23
1547 8 8 8 8 8 8 8 8 8 Married Married Married Married Married Married Married Married Married 52 53 54 55 56 57 58 59 60 50-59 50-59 50-59 50-59 50-59 50-59 50-59 50-59 60-69 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 4 4 4 4 4 4 4 4 4 Low Low Low Low Low Low Low Low Low 39180 39180 39180 39180 39180 39180 39180 39180 39180 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 5e+05 5e+05 5e+05 5e+05 5e+05 5e+05 5e+05 5e+05 5e+05 1000 1000 1000 1000 1000 1000 1000 1000 1000 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 0.935 0.935 0.935 0.935 0.935 0.935 0.935 0.935 0.935 1 1 1 1 1 1 1 1 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 37 38 39 40 41 42 43 44 45 34-43 34-43 34-43 34-43 34-43 34-43 34-43 34-43 44-53
736 9 9 9 9 9 9 9 9 9 Married Married Married Married Married Married Married Married Married 49 50 51 52 53 54 55 56 57 40-49 50-59 50-59 50-59 50-59 50-59 50-59 50-59 50-59 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 4 4 4 4 4 4 4 4 4 Medium Medium Medium Medium Medium Medium Medium Medium Medium 30150 30150 30150 30150 30150 30150 30150 30150 30150 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 500 500 500 500 500 500 500 500 500 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 52977.089 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 3 3 3 3 3 3 3 3 3 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 -1 -1 0 0 0 0 0 0 1 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.2 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 34 35 36 37 38 39 40 41 42 24-33 34-43 34-43 34-43 34-43 34-43 34-43 34-43 34-43
12236 9 9 9 9 9 9 9 9 9 Single Single Single Single Single Single Single Single Single 50 51 52 53 54 55 56 57 58 50-59 50-59 50-59 50-59 50-59 50-59 50-59 50-59 50-59 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 4 4 4 4 4 4 4 4 4 Medium Medium Medium Medium Medium Medium Medium Medium Medium 22600 22600 22600 22600 22600 22600 22600 22600 22600 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 100 100 100 100 100 100 100 100 100 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 2 2 2 2 2 2 2 2 2 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 35 36 37 38 39 40 41 42 43 34-43 34-43 34-43 34-43 34-43 34-43 34-43 34-43 34-43
14398 9 9 9 9 9 9 9 9 9 Single Single Single Single Single Single Single Single Single 47 48 49 50 51 52 53 54 55 40-49 40-49 40-49 50-59 50-59 50-59 50-59 50-59 50-59 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 0-7500 3 3 3 3 3 3 3 3 3 Medium Medium Medium Medium Medium Medium Medium Medium Medium 28950 28950 28950 28950 28950 28950 28950 28950 28950 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 1000 1000 1000 1000 1000 1000 1000 1000 1000 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.935 0.935 0.935 0.935 0.935 0.935 0.935 0.935 0.935 1 1 1 1 1 1 1 1 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 32 33 34 35 36 37 38 39 40 24-33 24-33 24-33 34-43 34-43 34-43 34-43 34-43 34-43
6386 10 10 10 10 10 10 10 10 10 Single Single Single Single Single Single Single Single Single 16 17 18 19 20 21 22 23 24 16-19 16-19 16-19 16-19 20-24 20-24 20-24 20-24 20-24 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 2 2 2 2 2 2 2 2 2 Low Low Low Low Low Low Low Low Low 1020 1020 1020 1020 1020 1020 1020 1020 1020 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 100 100 100 100 100 100 100 100 100 0 2440.102 239.7231 6483.2473 2026.9269 220.6801 1897.678 200.273 2659.388 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 3.0 3.0 3.0 3.0 2.0 2.0 2.0 2.0 2.0 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.720 0.720 0.720 0.720 0.720 0.720 0.720 0.720 0.720 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1 1 1 1 1 1 1 1 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 2 3 3 3 3 3 3 -1 -1 1.4 1.6 1.6 1.6 1.6 1.6 1.6 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 1 2 3 4 5 6 7 8 9 0-3 0-3 0-3 0-3 4-8 4-8 4-8 4-8 4-8
5284 11 11 11 11 11 11 11 11 11 Married Married Married Married Married Married Married Married Married 27 28 29 30 31 32 33 34 35 25-29 25-29 25-29 30-39 30-39 30-39 30-39 30-39 30-39 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 3 3 3 3 3 3 3 3 3 High High High High High High High High High 1270 1270 1270 1270 1270 1270 1270 1270 1270 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 0-10000 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 250 250 250 250 250 250 250 250 250 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 3 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1.5 1.5 1.5 1.0 1.0 1.0 1.0 1.0 1.0 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.720 0.720 0.720 0.720 0.720 0.720 0.720 0.720 0.720 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1 1 1 1 1 1 1 1 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 1 1 1 1 1 0 0 -1 -1 1.2 1.2 1.2 1.2 1.2 1.0 1 12 13 14 15 16 17 18 19 20 9-13 9-13 9-13 14-23 14-23 14-23 14-23 14-23 14-23
999 12 12 12 12 12 12 12 12 12 Married Married Married Married Married Married Married Married Married 65 66 67 68 69 70 71 72 73 60-69 60-69 60-69 60-69 60-69 70-79 70-79 70-79 70-79 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 3 3 3 3 3 3 3 3 3 Medium Medium Medium Medium Medium Medium Medium Medium Medium 62030 62030 62030 62030 62030 62030 62030 62030 62030 40000+ 40000+ 40000+ 40000+ 40000+ 40000+ 40000+ 40000+ 40000+ 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 500 500 500 500 500 500 500 500 500 0 0.000 0.0000 0.0000 0.0000 0.0000 3620.310 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1 1 1 1 1 1 1 1 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 1 1 1 -1 -1 1.0 1.0 1.0 1.0 1.2 1.2 1.2 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 50 51 52 53 54 55 56 57 58 44-53 44-53 44-53 44-53 44-53 54-63 54-63 54-63 54-63
1553 13 13 13 13 13 13 13 13 13 Single Single Single Single Single Single Single Single Single 37 38 39 40 41 42 43 44 45 30-39 30-39 30-39 40-49 40-49 40-49 40-49 40-49 40-49 15000+ 15000+ 15000+ 15000+ 15000+ 15000+ 15000+ 15000+ 15000+ 2 2 2 2 2 2 2 2 2 High High High High High High High High High 39430 39430 39430 39430 39430 39430 39430 39430 39430 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 30000-40000 3e+05 3e+05 3e+05 3e+05 3e+05 3e+05 3e+05 3e+05 3e+05 500 500 500 500 500 500 500 500 500 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.0 1.0 1.0 0.9 0.9 0.9 0.9 0.9 0.9 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1 1 1 1 1 1 1 1 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 22 23 24 25 26 27 28 29 30 14-23 14-23 14-23 24-33 24-33 24-33 24-33 24-33 24-33
8480 14 14 14 14 14 14 14 14 14 Single Single Single Single Single Single Single Single Single 20 21 22 23 24 25 26 27 28 20-24 20-24 20-24 20-24 20-24 25-29 25-29 25-29 25-29 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 7500-10000 3 3 3 3 3 3 3 3 3 High High High High High High High High High 23570 23570 23570 23570 23570 23570 23570 23570 23570 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 20000-30000 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 5e+04 100 100 100 100 100 100 100 100 100 0 0.000 0.0000 0.0000 236.8179 0.0000 0.000 0.000 0.000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 2.0 2.0 2.0 2.0 2.0 1.5 1.5 1.5 1.5 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 1.080 2 2 2 2 2 2 2 2 2 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 -1 -1 0 0 1 1 1 0 0 -1 -1 1.0 1.0 1.2 1.2 1.2 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 5 6 7 8 9 10 11 12 13 4-8 4-8 4-8 4-8 4-8 9-13 9-13 9-13 9-13
10430 14 14 14 14 14 14 14 14 14 Single Single Single Single Single Single Single Single Single 51 52 53 54 55 56 57 58 59 50-59 50-59 50-59 50-59 50-59 50-59 50-59 50-59 50-59 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 10000-15000 2 2 2 2 2 2 2 2 2 High High High High High High High High High 11020 11020 11020 11020 11020 11020 11020 11020 11020 10000-20000 10000-20000 10000-20000 10000-20000 10000-20000 10000-20000 10000-20000 10000-20000 10000-20000 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 1e+05 250 250 250 250 250 250 250 250 250 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1500 1500 1500 1500 1500 1500 1500 1500 1500 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.880 0.880 0.880 0.880 0.880 0.880 0.880 0.880 0.880 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1.045 1 1 1 1 1 1 1 1 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 -1 1 1 1 0 0 0 0 -1 -1 1.2 1.2 1.2 1.0 1.0 1.0 1 36 37 38 39 40 41 42 43 44 34-43 34-43 34-43 34-43 34-43 34-43 34-43 34-43 34-43

From the display of tbl.wider[1:20,], we can see that many columns are redundant. Before we remove any of these columns, this provides critical information for us as actuaries. Because we are trying to create a prospective underwriting guideline, we want to understand the data that go into our models.

Deleting Repeated Columns

# k <- 2 # counting index
keep <- c(1,2) # columns to keep
for (i in 3:(length(names(tbl.wider)) ) ) {
  compare <- prod( tbl.wider[i-1] == tbl.wider[i] )
  if ( is.na(compare) ) { compare <- TRUE }
  # print(diff)
  if ( !as.logical(compare)  ) { # if not exact match
    keep <- c(keep, i)
  }
}
tbl.cleaned <- tbl.wider[keep]
# View(tbl.cleaned, title = "Cleaned Data Table")
tbl.cleaned[1:20,] %>% qkable()
Vehicle Number Policy Number PY2010 Marital Status PY2010 Driver Age PY2010 Driver Age PY2011 Driver Age PY2012 Driver Age PY2013 Driver Age PY2014 Driver Age PY2015 Driver Age PY2016 Driver Age PY2017 Driver Age PY2018 Driver Age Band PY2010 Driver Age Band PY2011 Driver Age Band PY2012 Driver Age Band PY2013 Driver Age Band PY2014 Driver Age Band PY2015 Driver Age Band PY2016 Driver Age Band PY2017 Driver Age Band PY2018 Annual Mileage PY2010 Territory PY2010 Credit Score PY2010 Car Value PY2010 Car Value Band PY2010 Liability Limit PY2010 Physical Damage Deductible PY2010 Reported Losses PY2010 Reported Losses PY2011 Reported Losses PY2012 Reported Losses PY2013 Reported Losses PY2014 Reported Losses PY2015 Reported Losses PY2016 Reported Losses PY2017 Reported Losses PY2018 Late Fees PY2010 Late Fees PY2011 Late Fees PY2012 Late Fees PY2013 Late Fees PY2014 Conviction Points PY2010 Conviction Points PY2011 Conviction Points PY2012 Conviction Points PY2013 Conviction Points PY2014 Conviction Points PY2015 Conviction Points PY2016 Base Pure Premium PY2010 Marital Status Relativity PY2010 Driver Age Relativity PY2010 Driver Age Relativity PY2011 Driver Age Relativity PY2012 Driver Age Relativity PY2013 Driver Age Relativity PY2014 Driver Age Relativity PY2015 Driver Age Relativity PY2016 Driver Age Relativity PY2017 Driver Age Relativity PY2018 Annual Mileage Relativity PY2010 Car Value Relativity PY2010 Liability Limit Relativity PY2010 Physical Damage Deductible Relativity PY2010 Multi-Car PY2010 Multi-Car Relativity PY2010 Accident Points (AP) last 3 years PY2010 Accident Points (AP) last 3 years PY2011 Accident Points (AP) last 3 years PY2012 Accident Points (AP) last 3 years PY2013 Accident Points (AP) last 3 years PY2014 Accident Points (AP) last 3 years PY2015 Accident Points (AP) last 3 years PY2016 Accident Points (AP) last 3 years PY2017 Accident Points (AP) last 3 years PY2018 AP Relativity PY2010 AP Relativity PY2011 AP Relativity PY2012 AP Relativity PY2013 AP Relativity PY2014 AP Relativity PY2015 AP Relativity PY2016 AP Relativity PY2017 AP Relativity PY2018 Conviction Points (CP) last 3 years PY2013 Conviction Points (CP) last 3 years PY2014 Conviction Points (CP) last 3 years PY2015 Conviction Points (CP) last 3 years PY2016 Conviction Points (CP) last 3 years PY2017 Conviction Points (CP) last 3 years PY2018 CP Relativity PY2010 CP Relativity PY2012 CP Relativity PY2013 CP Relativity PY2014 CP Relativity PY2015 CP Relativity PY2016 CP Relativity PY2017 CP Relativity PY2018 Driver Experience PY2010 Driver Experience PY2011 Driver Experience PY2012 Driver Experience PY2013 Driver Experience PY2014 Driver Experience PY2015 Driver Experience PY2016 Driver Experience PY2017 Driver Experience PY2018 Driver Experience Band PY2010 Driver Experience Band PY2011 Driver Experience Band PY2012 Driver Experience Band PY2013 Driver Experience Band PY2014 Driver Experience Band PY2015 Driver Experience Band PY2016 Driver Experience Band PY2017 Driver Experience Band PY2018
6125 1 Single 18 19 20 21 22 23 24 25 26 16-19 16-19 20-24 20-24 20-24 20-24 20-24 25-29 25-29 0-7500 4 High 3220 0-10000 5e+04 100 0 0.000 0.0000 200.0692 0.0000 0.0000 12602.418 0.000 0.000 0 0 0 0 0 1 1 0 0 0 0 0 1500 1.2 3.0 3.0 2.0 2.0 2.0 2.0 2.0 1.5 1.5 0.7 0.720 0.60 1.080 2 0.9 -1 -1 0 1 1 1 1 1 1 -1 -1 1.0 1.2 1.2 1.2 1.2 1.2 1.2 1 0 0 0 0 0 -1 1.4 1.2 1.0 1.0 1.0 1.0 1 3 4 5 6 7 8 9 10 11 0-3 0-3 4-8 4-8 4-8 4-8 4-8 9-13 9-13
10332 1 Married 45 46 47 48 49 50 51 52 53 40-49 40-49 40-49 40-49 40-49 50-59 50-59 50-59 50-59 7500-10000 3 High 20090 20000-30000 5e+05 250 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 1 0 0 1500 0.8 0.9 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.9 1.000 1.12 1.045 1 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0 1 1 1 0 0 -1 1.0 1.0 1.2 1.2 1.2 1.0 1 30 31 32 33 34 35 36 37 38 24-33 24-33 24-33 24-33 24-33 34-43 34-43 34-43 34-43
1965 2 Married 54 55 56 57 58 59 60 61 62 50-59 50-59 50-59 50-59 50-59 50-59 60-69 60-69 60-69 15000+ 1 Medium 13810 10000-20000 1e+05 500 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 0 1500 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1.2 0.880 0.88 1.000 2 0.9 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0 0 0 0 0 0 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 39 40 41 42 43 44 45 46 47 34-43 34-43 34-43 34-43 34-43 34-43 44-53 44-53 44-53
14130 2 Married 49 50 51 52 53 54 55 56 57 40-49 50-59 50-59 50-59 50-59 50-59 50-59 50-59 50-59 10000-15000 1 High 37060 30000-40000 1e+05 100 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 1 0 0 0 0 0 0 1500 0.8 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1.0 1.045 0.88 1.080 1 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0 0 0 0 0 0 -1 1.2 1.0 1.0 1.0 1.0 1.0 1 34 35 36 37 38 39 40 41 42 24-33 34-43 34-43 34-43 34-43 34-43 34-43 34-43 34-43
2976 3 Single 31 32 33 34 35 36 37 38 39 30-39 30-39 30-39 30-39 30-39 30-39 30-39 30-39 30-39 7500-10000 4 High 20860 20000-30000 5e+04 250 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 0 1500 1.2 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.9 1.000 0.60 1.045 1 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0 0 0 0 0 0 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 16 17 18 19 20 21 22 23 24 14-23 14-23 14-23 14-23 14-23 14-23 14-23 14-23 14-23
76 4 Married 53 54 55 56 57 58 59 60 61 50-59 50-59 50-59 50-59 50-59 50-59 50-59 60-69 60-69 15000+ 2 High 80850 40000+ 5e+04 100 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 2 0 0 0 0 0 0 0 0 0 1500 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1.2 1.080 0.60 1.080 1 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0 0 0 0 0 0 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 38 39 40 41 42 43 44 45 46 34-43 34-43 34-43 34-43 34-43 34-43 34-43 44-53 44-53
8571 5 Single 21 22 23 24 25 26 27 28 29 20-24 20-24 20-24 20-24 25-29 25-29 25-29 25-29 25-29 0-7500 3 Low 5610 0-10000 1e+05 500 0 0.000 0.0000 10121.7588 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 1 0 0 0 1500 1.2 2.0 2.0 2.0 2.0 1.5 1.5 1.5 1.5 1.5 0.7 0.720 0.88 1.000 1 1.0 -1 -1 0 1 1 1 0 0 0 -1 -1 1.0 1.2 1.2 1.2 1.0 1.0 1.0 1 1 1 0 0 0 -1 1.0 1.2 1.2 1.2 1.0 1.0 1 6 7 8 9 10 11 12 13 14 4-8 4-8 4-8 4-8 9-13 9-13 9-13 9-13 9-13
8530 6 Single 28 29 30 31 32 33 34 35 36 25-29 25-29 30-39 30-39 30-39 30-39 30-39 30-39 30-39 7500-10000 2 High 20000 20000-30000 5e+04 250 0 0.000 0.0000 28602.5937 0.0000 0.0000 0.000 0.000 70200.000 0 2 0 1 0 0 0 0 1 0 1 0 1500 1.2 1.5 1.5 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.9 1.000 0.60 1.045 1 1.0 -1 -1 0 1 1 1 0 0 1 -1 -1 1.0 1.2 1.2 1.2 1.0 1.0 1.2 1 1 2 1 1 0 -1 1.0 1.2 1.2 1.4 1.2 1.2 1 13 14 15 16 17 18 19 20 21 9-13 9-13 14-23 14-23 14-23 14-23 14-23 14-23 14-23
6104 7 Single 33 34 35 36 37 38 39 40 41 30-39 30-39 30-39 30-39 30-39 30-39 30-39 40-49 40-49 0-7500 1 High 13020 10000-20000 5e+04 500 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 0 1500 1.2 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.9 0.9 0.7 0.880 0.60 1.000 2 0.9 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0 0 0 0 0 0 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 18 19 20 21 22 23 24 25 26 14-23 14-23 14-23 14-23 14-23 14-23 14-23 24-33 24-33
12468 7 Single 27 28 29 30 31 32 33 34 35 25-29 25-29 25-29 30-39 30-39 30-39 30-39 30-39 30-39 0-7500 2 High 26350 20000-30000 1e+05 250 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 1 0 1 1 0 0 1500 1.2 1.5 1.5 1.5 1.0 1.0 1.0 1.0 1.0 1.0 0.7 1.000 0.88 1.045 1 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2 2 2 1 0 0 -1 1.2 1.4 1.4 1.4 1.2 1.0 1 12 13 14 15 16 17 18 19 20 9-13 9-13 9-13 14-23 14-23 14-23 14-23 14-23 14-23
1547 8 Married 52 53 54 55 56 57 58 59 60 50-59 50-59 50-59 50-59 50-59 50-59 50-59 50-59 60-69 0-7500 4 Low 39180 30000-40000 5e+05 1000 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 2 0 0 0 0 0 0 0 0 0 0 1500 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.7 1.045 1.12 0.935 1 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0 0 0 0 0 0 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 37 38 39 40 41 42 43 44 45 34-43 34-43 34-43 34-43 34-43 34-43 34-43 34-43 44-53
736 9 Married 49 50 51 52 53 54 55 56 57 40-49 50-59 50-59 50-59 50-59 50-59 50-59 50-59 50-59 10000-15000 4 Medium 30150 30000-40000 1e+05 500 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 52977.089 0 0 0 0 0 0 0 0 0 0 0 0 1500 0.8 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1.0 1.045 0.88 1.000 3 0.8 -1 -1 0 0 0 0 0 0 1 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.2 0 0 0 0 0 0 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 34 35 36 37 38 39 40 41 42 24-33 34-43 34-43 34-43 34-43 34-43 34-43 34-43 34-43
12236 9 Single 50 51 52 53 54 55 56 57 58 50-59 50-59 50-59 50-59 50-59 50-59 50-59 50-59 50-59 0-7500 4 Medium 22600 20000-30000 5e+04 100 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 0 1500 1.2 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.7 1.000 0.60 1.080 2 0.9 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0 0 0 0 0 0 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 35 36 37 38 39 40 41 42 43 34-43 34-43 34-43 34-43 34-43 34-43 34-43 34-43 34-43
14398 9 Single 47 48 49 50 51 52 53 54 55 40-49 40-49 40-49 50-59 50-59 50-59 50-59 50-59 50-59 0-7500 3 Medium 28950 20000-30000 5e+04 1000 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 0 1500 1.2 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.7 1.000 0.60 0.935 1 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0 0 0 0 0 0 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 32 33 34 35 36 37 38 39 40 24-33 24-33 24-33 34-43 34-43 34-43 34-43 34-43 34-43
6386 10 Single 16 17 18 19 20 21 22 23 24 16-19 16-19 16-19 16-19 20-24 20-24 20-24 20-24 20-24 7500-10000 2 Low 1020 0-10000 5e+04 100 0 2440.102 239.7231 6483.2473 2026.9269 220.6801 1897.678 200.273 2659.388 0 0 0 0 0 0 0 0 0 0 0 0 1500 1.2 3.0 3.0 3.0 3.0 2.0 2.0 2.0 2.0 2.0 0.9 0.720 0.60 1.080 1 1.0 -1 -1 2 3 3 3 3 3 3 -1 -1 1.4 1.6 1.6 1.6 1.6 1.6 1.6 0 0 0 0 0 0 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 1 2 3 4 5 6 7 8 9 0-3 0-3 0-3 0-3 4-8 4-8 4-8 4-8 4-8
5284 11 Married 27 28 29 30 31 32 33 34 35 25-29 25-29 25-29 30-39 30-39 30-39 30-39 30-39 30-39 7500-10000 3 High 1270 0-10000 1e+05 250 0 0.000 0.0000 0.0000 0.0000 0.0000 0.000 0.000 0.000 0 0 3 0 0 0 1 0 0 1 0 0 1500 0.8 1.5 1.5 1.5 1.0 1.0 1.0 1.0 1.0 1.0 0.9 0.720 0.88 1.045 1 1.0 -1 -1 0 0 0 0 0 0 0 -1 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1 1 1 1 0 0 -1 1.2 1.2 1.2 1.2 1.2 1.0 1 12 13 14 15 16 17 18 19 20 9-13 9-13 9-13 14-23 14-23 14-23 14-23 14-23 14-23
999 12 Married 65 66 67 68 69 70 71 72 73 60-69 60-69 60-69 60-69 60-69 70-79 70-79 70-79 70-79 10000-15000 3 Medium 62030 40000+ 1e+05 500 0 0.000 0.0000 0.0000 0.0000 0.0000 3620.310 0.000 0.000 0 0 0 0 0 0 0 0 0 0 0 0 1500 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1.0 1.080 0.88 1.000 1 1.0 -1 -1 0 0 0 0 1 1 1 -1 -1 1.0 1.0 1.0 1.0 1.2 1.2 1.2 0 0 0 0 0 0 -1 1.0 1.0 1.0 1.0 1.0 1.0 1 50 51 52 53 54 55 56 57 58 44-53 44-53 44-53 44-53 44-53 54-63 54-63 54-63 54-63
1553 13 Single 37 38 39 40 41 42 43 44 45 30-39 30-39 30-39 40-49 40-49 40-49 40-49 40-49 40-49 15000+ 2 High 39430 30000-40000