How AngelList Automates Tax Form Parsing

Venture funds, like any financial vehicle, can be represented as a collection of events, transactions, and documents (legal, accounting, finance, tax). Every fund admin provider supports and tracks these vehicles in different ways, which makes porting a cross-team collaboration between operations, tax, finance, accounting, and engineering. This process involves converting the existing representation of the financial vehicle into one that our systems can readily use.

A single fund can have hundreds of tax documents associated with it: K-1s, Schedule Bs, Form 1042s, etc. To offer our all-in-one fund administration with tax services, we have to construct scalable ways to ingest and integrate these tax documents into our systems, verify this data at scale, and identify uncommon fact patterns.

Tax filings are semi-structured in nature and include a labyrinth of vertical and horizontal lines, solid and dotted lines, boxes, X’s, columns, and rows. While fairly straightforward to maneuver with the human eye, it’s harder to decompose into a set of consistent machine-interpretable rules. Using a few tools and techniques, we were able to extract relevant information from the tax form and integrate it into our data pipelines.

Upload Form 1065 Filing → Parse → Integrate → Data and Sanity Checks

We began by using AWS Textract to parse these tax documents, but found that Textract regularly failed to recognize the X’s in Yes/No columns. To diagnose the cause of the detection issues, we whited out the X’s, overlaid other letters in various fonts and sizes, and found that letters without sharp edges like C and S were picked up more frequently.

Additionally, Textract was unable to logically draw the connections between row-major line-items and column-major answers (picture). So we integrated with to extract liberally formatted key-value pairs across various document configurations—all without rebuilding a document parser in-house.