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

TL;DR

This paper uses advanced clustering techniques to analyze financial performance patterns of Kenyan medical insurance companies, identifying distinct groups and emphasizing the importance of transparency and timely reporting for sector resilience.

Contribution

It introduces a novel application of time series clustering with DTW and KMeans to categorize insurance companies based on financial and reporting behaviors.

Findings

Resilient companies show consistent reporting and financial stability.

Underperforming clusters reveal operational challenges and data gaps.

Transparency is crucial for sector resilience.

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.