Auto-clustering of Financial Reports Based on Formatting Style and Author’s Fingerprint

We present a new clustering algorithm of financial reports that is based on the reports’ formatting and style. The algorithm uses layout and content information to automatically generate as many clusters as needed. This allows us to reduce the effort of labeling the reports in order to train text-based machine learning models for extracting person or company names, addresses, financial categories, etc. In addition, the algorithm also produces a set of sub-clusters inside each cluster, where each sub-cluster corresponds to a set of reports made by the same author (person or firm). The information about sub-clusters allows us to evaluate the change in the author over time. We have applied the algorithm to a dataset with over 38,000 financial reports (last Annual Account presented by a company) from the Luxembourg Business Registers (LBR) and found 2,165 clusters between 2 and 850 documents with a median of 4 and an average of 14. When adding 2,500 new documents to the existing cluster set (previous annual accounts presented by companies), we found that 67.3% of the financial reports were placed in the correct cluster and sub-cluster. From the remaining documents, 65% were placed in a different subcluster because the company changed the formatting style, which is expected and correct behavior. Finally, labeling 11% of the entire dataset, we can replicate these labels up to 72% of the dataset, keeping a high feature coverage.