Decoding Financial Health in Kenyas’ Medical Insurance Sector: A Data-Driven Cluster Analysis

ArXiv ID: 2502.17072 “View on arXiv”

Authors: Unknown

Abstract

This study examines insurance companies’ financial performance and reporting trends within the medical sector using advanced clustering techniques to identify distinct patterns. Four clusters were identified by analyzing financial ratios and time series data, each representing unique financial performance and reporting consistency combinations. Dynamic Time Warping (DTW) and KMeans clustering were employed to capture temporal variations and uncover key insights into company behaviors. The findings reveal that resilient performers consistently report and have financial stability, making them reliable options for policyholders. In contrast, clusters of underperforming companies and those with reporting gaps highlight operational challenges and issues related to data consistency. These insights emphasize the importance of transparency and timely reporting to ensure the sector’s resilience. This study contributes to the literature by integrating time series analysis into financial clustering, offering practical recommendations for improving data governance and financial stability in the insurance sector. Future research could further investigate non-financial indicators and explore alternative clustering methods to provide a deeper understanding of performance dynamics.

Keywords: Dynamic Time Warping (DTW), KMeans clustering, Financial ratios, Time series analysis, Insurance sector, Insurance

Complexity vs Empirical Score

• Math Complexity: 5.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced temporal clustering methods (DTW, LSTM) and statistical validation, demonstrating moderate mathematical complexity. Its heavy reliance on real financial data, specific time-series analysis, and actionable recommendations for regulators and insurers indicates a backtest-ready, data-driven empirical approach.

  flowchart TD
    A["Research Goal<br>Assess Financial Health<br>in Medical Insurance Sector"] --> B["Data Preparation<br>Financial Ratios &<br>Time Series Data"]
    B --> C["Key Methodology<br>DTW + KMeans Clustering"]
    C --> D{"Computation"}
    D --> E["Temporal Analysis<br>Dynamic Time Warping"]
    D --> F["Pattern Grouping<br>KMeans Clustering"]
    E & F --> G["Findings<br>4 Distinct Clusters Identified"]
    G --> H["Outcomes & Insights<br>Resilient vs Underperforming<br>Transparency & Governance Recommendations"]