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.
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.