By Anuradha RK, Business Head at IRIS CARBON®, winner at The SaaS Awards 2022 for the Most Agile / Responsive SaaS Solution of the Year category
A Gartner report on the Top Priorities for Finance Leaders in 2022 says that businesses are increasingly laying the groundwork for an autonomous future where technologies, such as artificial intelligence drive finance operations with limited human intervention.
One critical technology often overlooked in discussions about simplifying finance operations is eXtensible Business Reporting Language (XBRL) – a data standard that improves how financial information is produced, obtained, and analyzed. For nearly two decades, XBRL has provided regulators and markets with actionable financial data in a machine-readable format. The financial world has been slow to realize the benefits of machine-readable data because regulatory efforts to make XBRL reports widely available are seen through the narrow lens of a compliance burden. For context, more than 100 regulatory agencies in over 60 countries collect XBRL reports from the entities they oversee.
But how exactly does XBRL reporting reconcile with aims for an autonomous future where artificial intelligence drives finance operations?
XBRL data – which we will refer to as structured data for the remainder of this article – is produced using machine-readable code from a taxonomy to mark up individual disclosures in financial reports. A taxonomy is a dictionary of machine-readable code and represents the machine-readable version of a set of accounting standards. Financial data produced in a machine-readable format is easier to obtain and use than information locked in a static PDF – where the data will need to be keyed in into a spreadsheet before use.
Financial information in machine-readable form can be used for business insights in a more powerful way than paper-based reports. The data can be pulled into spreadsheets at the click of a button and sliced and diced for high-quality insights leading to better decision-making. However, the most critical piece in the equation is the technologies that work in conjunction with structured data.
The Technologies Driving Structured Reporting and Data Analysis
Software as a Service
The technology at the forefront of delivering structured reporting capabilities to global organizations is Software as a Service (SaaS). SaaS, or software and services delivery through the cloud, has removed geographical, chronomatic, and productivity constraints to doing business and its possibilities seem endless. Combine SaaS and structured reporting capabilities, and you have a solution that meets the need of internal and external audiences for actionable data. External audiences are stakeholders of financial information, such as regulators, investors, analysts, credit rating agencies, and lenders.
Make no mistake: A structured reporting solution is not accounting software. A structured reporting solution draws data from spreadsheets, enterprise resource planning software, disclosure management systems, and commercial accounting software before converting the data into a machine-readable format. While the solutions mentioned above are rich business data sources, they must rely on structured data solutions to provide enhanced value.
Artificial Intelligence & Machine Learning
As mentioned above, preparing structured reports involves marking-up individual disclosures in financial statements with machine-readable code from a taxonomy. The process of marking up or tagging financial information is where artificial intelligence and machine learning have their applications. A SaaS solution can remember the business or sector-specific machine-readable code and suggest appropriate labels for disclosures. AI/ML also allow prior year code to be rolled forward to reports of future periods so that the marking-up process needn’t repeat.
Web Browser Format for Structured Reporting
A more advanced version of the XBRL format mentioned above is preferred for producing and disseminating financial reports. That’s because Inline XBRL – the advanced format – causes reports to be human-readable and machine-readable at the same time. Machine-readable code is inserted against financial disclosures in an HTML document from the backend. The resultant report retains its human-readable appearance while having a machine-readable layer that can be processed for a detailed analysis or comparison. In certain jurisdictions such as the European Union and the United Kingdom, XHTML reports – HTML reports with XBRL code – are replacing PDF annual reports.
Use of Application Programming Interfaces
An Application Programming Interface or API is a software intermediary that allows two applications to interact with each other for the transfer of information. APIs and structured data are a potent combination because of the possibilities involved. Jurisdictions with a mature market for structured data – such as the United States and the United Kingdom – have more than a decade’s worth of information that can readily feed into third-party applications for automated analysis. APIs can also be used within structured data reporting solutions for insights to improve the reporting process.
Does Blockchain have an Application here?
Blockchain, a type of distributed ledger, is a possible future application for structured data. Blockchain was earlier thought to be a possible replacement for XBRL. However, it was later realized that blockchain can use structured data for marking out smart contracts between buyers and sellers which are executed when certain conditions are met. Smart contracts are not an inherent requirement of blockchain but can be incorporated into the technology for a permanent record of transactions. Smart contracts can be a vehicle for accounting rules that control how activities are performed and recorded. That’s where universally-accepted structured data standards can play a part. There would be no need for human intervention when smart contracts rely on data backed by financial standards to act.
As mentioned earlier, businesses will see the true value of structured data reporting when they stop considering it a compliance burden. Major jurisdictions where structured reporting requirements have been in place for more than a decade are the United States, the United Kingdom, China, Japan, South Korea, Singapore, and India. Organizations and investors in these jurisdictions have a massive pool of financial data to analyze and more data enters the system every quarter and year. This is data that can be used to forecast future cash flows, revenues, and market performance.
The first step organizations can take towards structured data is producing it themselves. Using the technologies mentioned in this article, organizations can produce and process high-quality and actionable financial data for business insights. It remains to be seen how organizations begin to leverage structured reporting and analysis capabilities.