  Financial data aggregators and Personal Financial Management (PFM) services
are software products that help individuals manage personal finances by
collecting information from multiple accounts at various Financial Institutes
(FIs), presenting data in a coherent and concentrated way, and highlighting
insights and suggestions. Money transfers consist of two sides and a direction.
From the perspective of a financial data aggregator, an incoming transaction
consists of a date, an amount, and a description string, but not the explicit
identity of the sending FI. In this paper we investigate supervised learning
based methods to infer the identity of the sending FI from the description
string of a money transfer transaction, using a blend of traditional and RNN
based NLP methods. Our approach is based on the observation that the textual
description field associated with a transactions is subjected to various types
of normalizations and standardizations, resulting in unique patterns that
identify the issuer. We compare multiple methods using a large real-word
dataset of over $10$ million transactions.
