Gordonwest08/Geographic-Analysis-Of-Insurance-Claims-And-Risk-Concentration-In-Kenya

Domaine:

socioeconomicgeospatial

Type de record:

dataset

Créateur:

Gor

Hôte:

# 🌍 Geographic Analysis of Insurance Claims and Risk Concentration in Kenya ## 📜 Overview This project analyzes the **geographic distribution of insurance claims across Kenya** to uncover patterns in claim frequency, claim amounts, and regional risk hotspots. By combining **SQL-based analysis** with **interactive Power BI dashboards**, the study helps insurance companies make **data-driven decisions** in underwriting, pricing, and claims management. --- ## 🎯 Objectives ### Primary Objective To analyze and visualize the geographic distribution of insurance claims in Kenya. ### Specific Objectives 1. Identify towns/regions with the highest number and amount of claims. 2. Analyze seasonal claim trends (e.g., rainy vs. dry season). 3. Compare claim reasons across different regions. 4. Evaluate average claim severity by region and policy type. 5. Develop interactive dashboards for geographic and temporal analysis. --- ## 📊 Dataset Description The dataset contains simulated records of **policyholders**, **policies**, and **claims** from various Kenyan towns and regions. | Table | Description | Rows | Key Columns | |--------|--------------|-------|--------------| | `policyholders` | Customer demographics | 1000+ | `policyholder_id`, `name`, `region`, `date_of_birth` | | `policies` | Policy details | 1200+ | `policy_id`, `policyholder_id`, `start_date`, `end_date`, `premium_amount` | | `claims` | Claim details | 2000+ | `claim_id`, `policy_id`, `claim_date`, `claim_amount`, `claim_status`, `claim_type` | 📅 **Date Range:** 2018 – 2025 --- ## 🛠 Tools & Technologies - 🐬 **MySQL 8** — Data storage and SQL analysis - 📊 **Power BI** — Dashboard creation and visualization - 📄 **Google Sheets** — Data exploration and documentation --- ## 📈 Methodology 1. **Data Preparation** - Loaded datasets into MySQL - Cleaned NULL values, corrected invalid dates, standardized region names 2. **Exploratory Data Analysis (SQL)** - Aggregated claims by region, month, and policy ty …

Visit

github.com

Similaires

Predictive Modeling of Insurance Claims in RwandaFRAUDULENT ACTIVITIES AND SURGE IN PUBLIC SERVICE VEHICLE CLAIMS IN INSURANCE INDUSTRY IN KENYADeterminants of Claims in Motor Insurance in RwandaReinsurance Pricing of Large Motor Insurance Claims in Nigeria: An Extreme Value AnalysisCredibility Premium Estimation of Insurance Claims in NigeriaUnpaid insurance claims: Language and communication gaps

Predictive Modeling of Insurance Claims in Rwanda

Purpose: The aim of the study was to examine the predictive modeling of insurance claims in Rwanda.&

FRAUDULENT ACTIVITIES AND SURGE IN PUBLIC SERVICE VEHICLE CLAIMS IN INSURANCE INDUSTRY IN KENYA

ABSTRACT In Kenya, all Public Service Vehicles are obligated to have third party liability cover hen

Determinants of Claims in Motor Insurance in Rwanda

The paper examines the determinants of claims in the motor insurance sector in Rwanda. We utilised t

Reinsurance Pricing of Large Motor Insurance Claims in Nigeria: An Extreme Value Analysis

Insurers that undertake high risk profiles usually exceed their financial capabilitie

Credibility Premium Estimation of Insurance Claims in Nigeria

In this paper one of the foundational theories in actuarial science-the Empirical Bay

Unpaid insurance claims: Language and communication gaps

Language and communication play pivotal roles in the insurance industry, impacting claims processing