One of the most common problems for large, high-growth businesses is dealing with increasing volumes and varieties of financial data - more specifically, extracting the data from PDF documents such as quarterly reports, balance sheets, bank statements and cash flow statements.
Without a solution to handle these data extraction tasks at scale, operations quickly become error-prone and time-consuming. This is why a growing number of organisations are now implementing AI data extraction tools.
It’s estimated that enterprises worldwide spend over £30 billion per year on manually extracting data from documents.
Without AI support, workers are often forced to extract data manually. However, this method can take up to 70% of a worker’s day. As a result, it’s estimated that enterprises worldwide spend over £30 billion per year on manually extracting data from documents.
For large-scale financial reporting tasks, optical character recognition (OCR) technology has traditionally been the go-to automation solution. However, OCR is inflexible - it relies on fixed standard templates, and lacks the flexible cognitive capabilities of modern AI. Therefore, as the complexity of digital financial statements and other documents has evolved, it is no longer effective as a standalone technology.
Financial teams have often suffered with costly data extraction processes due to the inadequacies of both manual methods and legacy OCR tech. Additionally, reporting quality has been impacted due to inaccuracies in the exported data.
Of course, these issues are not specific to financial data. There are many other business areas with a need for improved data extraction capabilities.
For example, asset management firms are under increasing pressure from investors and regulators to deliver accurate reporting on the ESG (economic, social and governance) performance of their portfolios. Because the content of these ESG reports can vary greatly, AI-powered data extraction software is essential to ensure these documents can be accurately processed at scale.
The value of the IDP industry is estimated to reach £3.4 billion by 2026.
Intelligent Data Extraction, also known as IDP (Intelligent Document Processing), is the modern solution to the financial data extraction problem.
IDP uses AI algorithms to extract data from PDFs (both scanned documents and native PDFs) and other complex document types at scale. Leading enterprises are now implementing this technology to automate document processing, delivering significant cost reductions and operational improvements.
The number of enterprises adopting IDP tech is growing rapidly. In fact, the value of the IDP industry is estimated to reach £3.4 billion by 2026.
With the demand for data extraction solutions growing across multiple sectors, the pressure is now on the IDP vendors themselves to address a wide variety of sector-specific requirements.
In order to achieve maximum ROI, companies need an IDP partner with skills and expertise suited to their unique requirements.
As Mark Qualter, formerly Head of AI for RBS Group’s Commercial and Private Banking division (2018), stated:
We are a regulated industry, so partners we work with have to be cognisant of that and also empathetic. If they don’t get that, it really is end of story. We also have to see that they can provide the sort of documentation that will help us when we talk to regulators about what we’re building.
Evolution AI CEO, Martin Goodson, and Mark Qualter presenting at Futureland Milan
The right solution can create immense operational, cost and productivity benefits for organisations within the first few months. Evolution AI, for example, have delivered the following results for clients in the financial sector:
It’s important to note that Evolution AI’s data extraction capabilities are not limited to PDFs. Our products are also able to process Excel files and many other document types.
If you’d like to learn more on how Evolution AI extracts financial data with high speed and accuracy, book a demo or email email@example.com.