Our solution included fetching external data from Bing, which helped correctly identify tricky cases like subsidiaries, mergers and acquisitions. Our proprietary algorithm then built a graph from this data, followed by a hierarchical clustering approach and finally a deduplication and post-processing step. The model obtained an adjusted mutual information (AMI) score of 91%. This helped improve the data quality significantly, and now the client can detect transactions related to the same supplier, even if their names are different.