Financial spreading is the process of transcribing data from a company’s financial statements into a standardised format (like an Excel template) for analysis. Automating this process is a key goal for efficient credit teams. The right tool depends on a bank’s technical resources and specific needs.
Tools for automating financial spreading fall into four main categories: 1) off-the-shelf OCR software, 2) iXBRL parsers for modern digital filings, 3) third-party data APIs that provide pre-extracted, structured data as a service, and 4) all-in-one data platforms.
What is the goal of a spreading tool?
The goal is to convert an unstructured document (like a PDF) into structured data (like a database row or JSON object) with perfect accuracy, requiring minimal human intervention.
What are the main categories of automation tools?
While many tools exist, they generally fall into four types:
- OCR (Optical Character Recognition) Software:
- What it is: Software that scans image-based documents and converts text into a machine-readable format.
- Best for: Processing older, scanned-image PDFs where no digital data exists.
- Limitations: Can be error-prone with low-quality scans and typically requires significant human review. It extracts text, but often not the context or structure.
- iXBRL Parsers:
- What it is: A specialised script or tool designed to read the iXBRL (inline eXtensible Business Reporting Language) tags embedded in modern financial filings.
- Best for: Very high-accuracy, high-speed extraction from new UK filings.
- Limitations: Completely ineffective for any document not in iXBRL format (like older filings, scanned reports, or non-UK documents).
- Third-party data APIs (Application Programming Interfaces):
- What it is: These are services that perform the extraction themselves and provide the clean, structured data as a product. A bank’s internal system can then call this API to instantly retrieve financials for a specific company.
- Best for: Teams (like fintech developers at banks) that want to integrate a reliable data feed directly into their own proprietary risk models, portals, or databases. These often use modern protocols like GraphQL or REST.
- Limitations: Relies on an external vendor.
- All-in-one data platforms:
- What it is: Large, subscription-based platforms that bundle private company data with a user interface for search and analysis.
- Best for: Teams that need a broad, off-the-shelf research tool rather than a raw data feed for their own systems. Examples include platforms like PitchBook or S&P Capital IQ.
- Limitations: Can be very expensive and less flexible for integrating data into internal, proprietary systems.
How to choose the right tool
The best choice depends on your bank’s internal capabilities:
- For high-volume, automated workflows: A dedicated Data API is often the most efficient and scalable solution, as it feeds data directly into your systems.
- For occasional, one-off research: An all-in-one data platform may be sufficient.
- For technically savvy teams on a budget: Building an in-house iXBRL parser can be a good starting point, but it will have significant data gaps.