More time for what matters with AI: Automating financial statement recording in FINOYO
In many companies, financial statement analysis as part of a credit assessment is still characterised by tedious, manual steps: transferring figures from PDFs, searching through documents, manually maintaining tables, identifying discrepancies and collating information from different sources. These tasks are time-consuming, monotonous – and tie up valuable expertise that could be put to much better use elsewhere.
However, the use of large language models is beginning to fundamentally change this process. These models make it possible to soften the requirement for technical understanding for the correct extraction of information and to make it machine-implementable. Technically, these models provide the basis for a shift from the traditional financial statement recording process to a modern financial statement auditing process that consistently focuses on the essentials: the content review and assessment of information.
Automated reading of annual financial statement information: technology that understands
Modern AI models are now capable of automatically reading and structuring complex financial documents such as annual financial statements, group reports, appendices and management reports. They recognise tables, extract numerical values, interpret information and assign it to a uniform data model. We establish the connection between these models and FINOYO through our AI platform ALEVA.
What does this mean for FINOYO in concrete terms?
- Automated reading of financial statements and income statement tables from almost any file format
- Intelligent assignment of items to standard charts of accounts or audit logic
- Recognition of notes to the financial statements for the extraction of specific information
- Automatic plausibility checks that immediately reveal inconsistencies
This not only makes data entry faster, but also more consistent and less prone to errors. Where hours were once spent typing in figures, AI now lays the foundation for a much more efficient analysis. In addition, FINOYO also enables the quality of the AI processes to be checked in a completely transparent manner through additional reporting, which shows in the medium and long term how the degree of automation of the extraction can be individually adjusted.
Change with a focus on AI: from recording to auditing
The automation of recording processes has given rise to a completely new approach to work: experts can finally devote their time and expertise to where they are really needed – in assessment, evaluation and auditing.
The new financial statement auditing process in FINOYO:
- Data collection by AI
AI takes over the reading, structuring and pre-validation of financial statement information. - Automated preliminary analysis
Anomalies or inconsistencies in financial statement ratios are automatically highlighted. If necessary, the item can also be displayed directly in the original document by right-clicking. - New option for analysing the management report
We present the core information of the components of the management report in a structured manner to reduce the amount of reading required by the auditor. - Technical review by experts
Auditors can concentrate on the essential tasks: assessing risks, analysing developments and evaluating accuracy. - Documentation & reporting
The corresponding results are generated automatically, with all relevant key figures and comments.
The result: more time for quality, less effort for routine tasks.
Why this change through AI is so important
The shortage of skilled workers, increasing regulatory requirements and increasingly complex corporate structures are putting companies and their analysts under increasing pressure. Automation therefore not only offers efficiency gains, but also enables:
- Greater depth of review due to more available time
- Improved data quality and transparency
- Scalable processes, regardless of document volume
- Traceable, reproducible audit steps
This creates new opportunities for a future-oriented and productive way of working, particularly in auditing and tax consulting. Furthermore, the use of language models also enables a more intensive focus on work steps in the evaluation of the management report, allowing individual responses to individual needs and structuring this previously purely human process into reproducible steps.
Conclusion: AI makes auditing a core task again
The use of AI in the automated reading of annual financial statement information is much more than a technological gimmick. It is a fundamental step towards a redefined audit and the possibility of rethinking the entire process – an audit that focuses on analysis, assessment and quality rather than data entry and hard work.
Technical development is iterative, but always with the aim of focusing the use of FINOYO on the essentials and continuously developing the process in conjunction with AI through ALEVA.
