Just last year, we released Boost to help accountants save time by automating their work. Boost instantly spots inconsistencies in their clients' ledgers, saving time and embarrassment! Every second, Digits sifts through every single transaction and performs a deep analysis. Boost alerts accountants if it finds errors like transactions in unexpected categories and suggests categories for transactions with missing categories.
The simplicity of the product is thanks to the powerful technology we built to make this possible.
With this three-part series, Digits’ Machine Learning team provides a look behind the scenes at how it works.
In this first blog post, we will explain why machine learning is crucial for accounting and how we detect categories for banking transactions with similarity-based machine learning models. In parts two and three, we will dive into how we use machine learning to accelerate the interactions between accountants and their clients.
Why Machine Learning?
Machine learning is a versatile tool for many applications, including accounting. For example, if we want to categorize transactions correctly, we can look at similar transactions and mimic their existing categorizations. We could find highly-similar transactions through traditional statistical methods like determining the Levenshtein distance between the transaction descriptions, but those methods would have failed in the following scenarios:
Because of the number of failure cases of traditional statistical methods, we decided to develop a custom machine learning-based solution.