The Top Challenges When Extracting Complex Financial Data

If you’ve ever tried to pull data from a PDF into Excel, you’ll know it’s harder than it looks! Financial modelling is a useful skill, but without the data needed to build the model, you’ll never get your financial model working. Even data from financial reporting tools, like accounting software, can be inconsistent and require some cleanup before using. 

Today, complex financial data can be retrieved more easily than before thanks to Natural Language Processing (NLP) models, machine learning, and AI agents. Although the nature of complex financial data means there will always need to be oversight, technology has made using this data easier than ever before. Choosing the right technology for your business will depend on how and why you’ll need this financial data. 

Why is it So Hard to Convert Financial Data from a PDF? 

Challenges with data extraction mean complex financial data often gets trapped in paperwork or left for manual review. Traditional methods of extracting financial information, like automation, had limited utility for financial professionals. Early automation technologies addressed structured, repetitive tasks, like pulling bank transactions automatically into accounting software. This automation was helpful, but incomplete – there were still a number of tasks that needed to be done by hand.  

Newer methods of extracting financial data from PDFs, like optical character recognition (OCR) progressed in the early 2000s and became widespread across industries and applications like invoice processing, accounts payable, and accounts receivable, generating the numbers on checks and making documents searchable.

• Tables rarely copy cleanly from a PDF or image 
• Numbers can be incorrect or omitted when transposed into a spreadsheet
• Inconsistent formatting (commas, date formatting, or decimal points) can make formulas work incorrectly. 

When you’re used to working with Excel hotkeys, clodgy data tables can be especially frustrating. The average finance team spends hours each month reformatting, cleaning, and data entry, and Gartner estimates that poor data quality costs teams at least $12.9 million per year (although as accounting teams would point out, this number might not always be material). 

Choosing a Technology Solution for Complex Financial Data 

AI tools process large volumes of data in a fraction of the time it would take a human user. However, this doesn’t mean leaving the most complex judgment calls up to a technology tool. A major challenge when working with complex financial data is accuracy. When financial data is on the table, there’s trouble with even one missing zero. 

‍Extractive summaries are designed to be relied on: they take data word for word from the source. Abstractive summaries are designed to be fast, summing up the most important pieces of a text and providing them to a user to save time. 

‍AI can quickly capture trends in financial data, anomalies, or troublesome patterns – but if heavy duty verification is required to make sure the cleaned data matches the source, abstractive summaries might not end up saving much time. For very sensitive reporting needs, AI assistants, agents, or platforms should help “serve up” clean data to finance professionals rather than summarizing or refining it itself. 

How can you find the right tool? It depends on:

• Willingness for manual workload: some organizations have high compliance barriers and need clean data for human teams. Others might use a purely automated workload. 
• Technology budget: are you trying an AI platform for the first time, or expanding on your existing workflow?
• Scope and scale of your enterprise needs: do you need exclusively financial statements cleaned and prepped for analysis? What about just the statement of cash flows? Will you need invoices, checks or other materials? 

Machine learning models and AI agents can also be tailored to your specific workflows. For example, one AI agent can pull a company’s cash flows from an analyst report each month (“Price’s Pickles earned $500,000 in Q1”). Another AI agent can summarize the report’s insights for a human to review (“class action legal threat to Price’s Pickles could be bad news”) and a final AI agent could package these insights into a spreadsheet (“Price’s Pickles Pricing Model”).

Using all 3 agents together helps create a more flexible and robust model than one single AI platform could design. 

The Best AI for Complex Financial Data 

Financial statements come in various forms, including PDFs, spreadsheets, quarterly reports, analyst insights, or even scanned images. Using and packaging the data from these reports isn’t always easy, but a carefully thought out AI model can dramatically cut down on data cleanup time.