Clustering is a way of grouping data into clusters based on similarities. A new hybrid algorithm combining K-means and BIRCH was developed. It first creates many clusters using BIRCH, then refines them with K-means for better accuracy and fewer errors. Testing on a banking dataset showed the new algorithm outperformed traditional K-means and K-Medoid algorithms in terms of accuracy and efficiency.