Executive Summary

This report provides a summary of experience profile and common attributes shared by Lloyds agents and their surveyors that declare an aviation capability. Of the 98 assessors across 54 countries common attributes are identified to deepen understanding of the role that agents have in claims process within the insurance industry.

Key Findings

  • 72.4% of assessors hold Lloyd’s Agency Standards & Values certification
  • Average experience of 25.6 years with 28.1% having 31-40 years of experience
  • Global presence across 54 countries with strong representation in major maritime hubs
  • Core qualification combinations center around Lloyd’s standards with specialized technical skills

Data Overview

# Load and preprocess the data
df <- read_csv(csv_file, show_col_types = FALSE)

# Clean and preprocess the data
df <- df %>%
  mutate(
    qualifications = ifelse(is.na(qualifications), "", qualifications),
    years_of_experience = as.numeric(years_of_experience),
    # Extract country from agent_location
    country = str_extract(agent_location, "\\(([^)]+)\\)") %>%
      str_remove_all("[\\(\\)]")
  )

# Summary statistics
total_assessors <- nrow(df)
total_countries <- length(unique(df$country))
avg_experience <- round(mean(df$years_of_experience, na.rm = TRUE), 1)
median_experience <- round(median(df$years_of_experience, na.rm = TRUE), 1)

cat("Dataset Summary:")
## Dataset Summary:
cat("\n• Total Assessors:", total_assessors)
## 
## • Total Assessors: 98
cat("\n• Countries Represented:", total_countries)
## 
## • Countries Represented: 54
cat("\n• Average Experience:", avg_experience, "years")
## 
## • Average Experience: 25.6 years
cat("\n• Median Experience:", median_experience, "years")
## 
## • Median Experience: 26 years

Core Qualifications Analysis

Most Critical Qualifications

The analysis identified six primary qualifications among aviation assessors, with the following distribution:

# Split qualifications by '/' and clean them
all_qualifications <- df %>%
  filter(!is.na(qualifications), qualifications != "") %>%
  pull(qualifications) %>%
  str_split(" / ") %>%
  unlist() %>%
  str_trim() %>%
  .[. != ""]

# Count qualifications
qual_counts <- table(all_qualifications) %>%
  sort(decreasing = TRUE)

# Create qualifications data frame
qualifications_df <- data.frame(
  Qualification = names(qual_counts),
  Count = as.numeric(qual_counts),
  Percentage = round((as.numeric(qual_counts) / nrow(df)) * 100, 1)
)

# Display table
qualifications_df %>%
  kable(caption = "Qualification Distribution Among Aviation Assessors",
        col.names = c("Qualification", "Count", "Percentage (%)")) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
  row_spec(1, bold = TRUE, color = "white", background = "#2E8B57") %>%
  row_spec(2, bold = TRUE, color = "white", background = "#4682B4") %>%
  row_spec(3, bold = TRUE, color = "white", background = "#4682B4")
Qualification Distribution Among Aviation Assessors
Qualification Count Percentage (%)
Lloyd’s Agency Standards & Values 71 72.4
Technical Cargo Surveying 40 40.8
CCSP1 39 39.8
CCSP2 29 29.6
Claims & Recoveries 14 14.3
LLI eTutorial - NOT FOR PUBLIC VIEW 1 1.0

Qualifications Visualization

# Create qualifications frequency chart
top_quals <- head(qual_counts, 10)
qual_plot_df <- data.frame(
  Qualification = names(top_quals),
  Count = as.numeric(top_quals)
)

ggplot(qual_plot_df, aes(x = reorder(Qualification, Count), y = Count)) +
  geom_col(fill = "steelblue", alpha = 0.7) +
  geom_text(aes(label = Count), hjust = -0.3, size = 4) +
  coord_flip() +
  labs(
    title = "Top 10 Most Common Qualifications",
    subtitle = "Distribution of qualifications among aviation assessors",
    x = "Qualification",
    y = "Number of Assessors"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16, face = "bold"),
    plot.subtitle = element_text(size = 12),
    axis.text = element_text(size = 10)
  )