What’s challenging about financial data compared to other types of data? Let’s explore a few you may encounter!
Navigating financial data requires a firm understanding of financial principles. Even calculating simple ratios requires understanding what constitutes an asset and a liability. In a world where 28% of Gen Z don’t consider themselves financially literate, entering and structuring financial data may be a surprisingly challenging demand for the average data entry operator.
Financial data necessitates high (or even perfect) accuracy. It is not the only high-stakes domain data. Medical or legal data, for example, also have a low tolerance for errors. Consequently, accuracy is necessary for all financial data outputted by Optical Character Recognition (OCR).
However, you must also balance accuracy with timeliness and clarity to ensure financial information is actionable and easy to interpret. The Financial Accounting Standards Board (FASB) lists six qualitative characteristics of financial data:
In other words, financial data must walk a tightrope of different requirements. The successful presentation of financial data can, therefore, realistically only happen via an experienced financial professional or a rigorously trained AI tool (with human oversight).
The successful presentation of financial data cannot be achieved via OCR. In this article, we’ll address how OCR struggles to read and extract financial data. Firstly, to understand why OCR is often unfit for this purpose, it’s important to understand how it works and what other options are on the market.
OCR (sometimes known as text recognition) is a firmly established data extraction technology. Data extraction can also be conceptualised as automated data entry or converting PDFs into an accessible format, depending on the use case.
When successful, OCR engines extract document data and output it into a user-defined schema. They do this by digitalising the text – binarising the image by converting each pixel to black or white (i.e. measuring the black text against the white background).
The OCR algorithms will then extract the text’s characters via pattern matching. Each line and loop in the character is compared to their database of known characters. The extracted text is then compiled into a searchable and machine-readable output (e.g. an Excel file, CSV, JSON, etc.).
Since OCR relies on binarising black and white colours, blurry images present a problem to rudimentary OCR engines.
Practically, not every company will need to extract data from poor-quality scans. For instance, OCR would likely experience no issues extracting data from beautifully constructed PDFs, such as annual reports.
However, use cases like commercial lending might require users to upload financial documents. Smeared cameras or poor lighting means difficult-to-read scans, which will likely yield inaccurate or missing outputted data from the OCR technology. The result? Delays, missed opportunities and manual intervention.
Traditional OCR’s inability to handle visual ambiguity also renders it ineffective with unseen document types (i.e. documents that the OCR engine hasn’t been trained on).
Firstly, it’s worth noting that OCR’s prescriptive setup means that unstructured data that don’t cohere with their pre-trained schemas will likely yield an inaccurate output. Common financial documents containing unstructured data include contracts, loan agreements and credit card statements. In contrast, OCR is best deployed for structured documents such as cheques or bubble exams.
‘OCR is great at what it does – it just doesn't infer any structure from the text’.
~Vincent Polfliet, Senior Machine Learning Engineer
Even if they contain structured data, new document types require training the OCR model to generate the correct outputs. OCR’s inherent inflexibility may limit its usefulness for fast-moving businesses handling multiple types of financial data. Ultimately, OCR cannot provide accurate data without extensive training on new document types.
Considering the astonishing capabilities of AI in the 2020s – from the mundanities of compiling grocery lists to beating expert benchmarks in Maths and Science – automated data extraction on its own may seem a little staid.
OCR can be a fundamental technology of an end-to-end automation process (e.g. automated underwriting or decision-making). However, compared to its cousin AI, OCR’s abilities are limited.
Consequently, if you’re looking to structure, compute or classify financial data, OCR will require time-consuming technical bolt-ons. Nowadays, OCR is embedded into AI tools – such as virtual agents like ChatGPT – that can leverage other technologies to compensate for OCR’s shortcomings. For example, if OCR misses a word, Natural Language Processing can assess the context and reconstruct the text.
AI-powered OCR is the way forward for the vast majority of firms. If you need proof, consider the performance of your current OCR solution.
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Count how many of these statements apply to you:
Your OCR solution seems to be performing well – keep monitoring its performance and whether it meets industry standards.
Your OCR solution might need finetuning or even replacement. Consider how much time and money your OCR technology is wasting.
It’s time to investigate alternatives. OCR is wasting your firm’s resources at an unsustainable rate. Luckily, AI to the rescue… right?
There is only one alternative to OCR, and that’s AI. It would be almost impossible to overstate the hype AI has received since ChatGPT’s release in November 2022. However, it’s important to remain realistic about what it can and cannot achieve.
For example, AI models like ChatGPT’s are not specialised to deal with financial data. Whilst users might marvel at their financial pyrotechnics – the ability to calculate ratios, forecasts, generate visualisations, etc. – their hallucination rate is too high for financial data’s qualitative and quantitative requirements.
So surely AI and OCR are in the same boat of not meeting such requirements, right? Well, there’s a superior alternative – a specialised AI-powered solution. When AI models are trained on financial data, they can combine OCR’s text recognition capabilities with AI’s, weeding out and correcting hallucinations.
AI, then, is inherently more accurate than OCR. For instance, you should expect AI-powered OCR solutions to deliver a high accuracy rate (99.9 -100%).
Despite criticisms of traditional OCR, experts project the OCR market to grow by 15.4% CAGR or $33.44 billion (USD) by 2030. It’s a helpful and versatile tool with AI features to manage OCR’s technical limitations. AI-powered OCR is, therefore, a better alternative to OCR. If you’re struggling with outdated OCR, consider a free trial or Proof of Concept of an AI-powered OCR tool.
Evolution AI is a multiple-award-winning AI-powered data extraction technology. Trained on millions of documents, we’ve helped companies like NatWest and Deutsche Bank transition into modern, competitive financial data extraction solutions. Find out more by booking a demo with our financial data project team or contacting us.
