Recent studies have illustrated historical financial data could be used to predict future revenues and profits. Prediction models would be accurate when long-run data that traces back for multiple years is available. However, changes in service structures often result in alteration of the nomenclatures of the services, making the streams of financial transactions associated with affected services discontinue. Manually inquiring the history of changes can be tedious and unsuccessful especially in large companies. In this paper, we propose a machine learning based algorithm for automatically discovering service name replacements. The proposed methodology draws heterogeneous features from financial data available in most ledger databases, and hence is generalizable. Our proposed methodology is shown to be effective on ground-truth synthesized data generated from real-world IBM service delivery ledger database.