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Financial data aggregators and Personal Financial Management (PFM) servicesare software products that help individuals manage personal finances bycollecting information from multiple accounts at various Financial Institutes(FIs), presenting data in a coherent and concentrated way, and highlightinginsights and suggestions. Money transfers consist of two sides and a direction.From the perspective of a financial data aggregator, an incoming transactionconsists of a date, an amount, and a description string, but not the explicitidentity of the sending FI. In this paper we investigate supervised learningbased methods to infer the identity of the sending FI from the descriptionstring of a money transfer transaction, using a blend of traditional and RNNbased NLP methods. Our approach is based on the observation that the textualdescription field associated with a transactions is subjected to various typesof normalizations and standardizations, resulting in unique patterns thatidentify the issuer. We compare multiple methods using a large real-worddataset of over $10$ million transactions.

Identifying Financial Institutions by Transaction Signatures