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.

We recently launched Digits Search, which brings fast, beautiful, full-depth search to business financial data. We’ve received a ton of great customer feedback, but one question just keeps coming up…

How did you build this!?

Well, let’s break down how Digits Search puts your finances at your fingertips.

Digits Architecture

First, some quick background on Digits:

Digits links with your business’ accounting software and your financial institutions to build, and continually maintain, a living model of your business with the most up-to-date data. Once linked, we ingest all of that financial data in raw form, a collection that we call “facts”.

We then use machine learning and data processing to normalize all of that overlapping, unstructured data. We perform a significant number of calculations to fill holes in the picture, such as predicting how your latest transactions will be categorized, detecting and predicting recurring activity, etc.

The end result of all this work is a “view”, which is then efficiently loaded into Google Cloud Spanner as well as encrypted and archived in Google Cloud Storage for secondary processing.

Each view we produce is a complete, standalone picture of your company’s entire financial history.

Views are served by our serving layer, which is composed of a number of services communicating over TLS-encrypted GRPC APIs, written in Go and hosted in GKE. Our serving layer aims to optimize for efficiency, security, and reliability.

Understanding banking transactions as they happen, in real-time, is core to our mission with Digits Search. You can’t answer important finance questions with bad data.

Transaction descriptions contain valuable information which helps us understand and communicate our customers’ business activity. The information we extract is then indexed and made available via Digits Search, and presented in a far more human-readable and intuitive manner than they would get from reviewing their raw bank or credit card statements.

Here we wanted to share a peek behind the curtains on how we extract transaction information with Natural Language Processing (NLP) at Digits. You’ll learn how we apply state-of-the-art Transformer models to this problem and how we go from an ML model idea all the way to a production integration with our Digits Search product.

Our Plan

Information can be extracted from unstructured text through a process called Named Entity Recognition (NER). This NLP concept has been around for many years, and its goal is to classify tokens into predefined categories, such as dates, persons, locations, and entities.

For example, the transaction below could be transformed into the following structured format:

We had seen outstanding results from NER implementations applied to other industries and we were eager to implement our own banking-related NER model. Rather than adopting a pre-trained NER model, we envisioned a model built with a minimal number of dependencies. That avenue would allow us to continuously update the model while remaining in control of “all moving parts.” With this in mind, we discarded available tools like the SpaCy NER implementation or HuggingFace models for NER. We ended up building our internal NER model based only on TensorFlow 2.x and the ecosystem library TensorFlow Text.

At Digits, we strive to push the bounds of technology in order to deliver radically more useful, delightful software experiences for our customers. We’re excited to begin sharing a closer look at the technical foundations that underpin our products in a new series of blog posts called Building Digits. Without further ado…

Let’s talk about viewing complex data. One of our primary goals at Digits is to provide business owners with insightful and holistic views of their company’s finances, in substantially real-time.

Achieving this involves three major, independent steps:

1. We collect all of their relevant data from various sources, such as their QuickBooks, the financial institutions they bank with, their corporate credit card providers, etc.
2. We apply our algorithms and proprietary datasets to extend, interpret, and tease out meaning from all of their data.
3. We consolidate and aggregate the results into a holistic view that we then visualize for them on their dashboard.

Facts

We refer to the pieces of data that we receive from third party systems as Facts. This is not a judgement of the credibility or immutability of these systems, but rather a delineation of what is (and what is not) under our control.

For example, if we receive a transaction from an external source that looks like 05/12/20 - Taxi $15.05, we might classify it as Transportation. Later, we may receive another piece of information that leads us to believe that this transaction was actually a client expense, and is better classified as Meals & Entertainment. In this example, the transaction itself, the fact, did not change—but our interpretation of it did.

Computed Data

We refer to insights and analysis that are performed by Digits, based on all of the Facts that we have received, as Computed Data. In the example above, this involved a category classification of a transaction. In other cases, this might involve determining that two external pieces of information actually represent the same physical transaction, or detecting that a particular transaction tends to recur on a regular interval and that it should be treated as a subscription.