Open AccessLarge‐Scale Personalized Categorization of Financial Transactions
Author(s) -
Lesner Christopher,
Ran Alexander,
Wang Wei,
Rukonic Marko
Publication year - 2020
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v41i3.5319
Subject(s) - database transaction , chart , computer science , financial transaction , categorization , scale (ratio) , automation , transaction processing , transaction data , task (project management) , accounting information system , accounting , finance , business , database , artificial intelligence , engineering , management , economics , mathematics , quantum mechanics , mechanical engineering , statistics , physics
A major part of financial accounting involves organizing business transactions using a customizable filing system that accountants call a “chart of accounts.” This task must be carried out for every financial transaction, and hence automation is of significant value to the users of accounting software. In this article we present a large‐scale recommendation system used by millions of small businesses in the USA, UK, Australia, Canada, India, and France to organize billions of financial transactions each year. The system uses machine learning to combine fragments of information from millions of users in a manner that allows us to accurately recommend chart‐of‐accounts categories even when users have created their own or named them using abbreviations or in foreign languages. Transactions are handled even if a given user has never categorized a transaction like that before. The development of such a system and testing it at scale over billions of transactions is a first in the financial industry.