Tomorrow’s finance function is built with data today
As a results-driven finance consultant, AAA has been helping CFOs optimize their order-to-cash cycles for 18 years. All too often, suboptimal processes, unstructured data, and inconsistent follow-up have a major impact on the financial health of companies.
CrediBill credit management software was born from this practical experience. It arose from the need to automate repetitive work as much as possible, provide finance teams with faster insights, and above all: free up time for work that truly requires human expertise.
With approximately 7 million unique invoices processed annually via the platform, representing a value of 10 billion, CrediBill is currently one of the fastest-growing software players in the sector. For the past 2 years, CrediBill has standardly added a virtual finance team member for each of its clients via artificial intelligence. It learns from large amounts of structured data, summarizes complex files, suggests the best possible next step, adjusts its tone as needed, and builds a script that the finance professional can use in their human interactions.
Finance teams are evolving into data teams. Through automation and AI, finance professionals gain command of the future instead of having to account for the past.
Every finance team today has an abundance of data at its disposal. This data is often scattered across systems, files, and business units. It is frequently inconsistent, incomplete, insufficiently structured, or corrupted. This limits the value of any AI application applied to it, no matter how powerful it may be. ‘Garbage in, garbage out’ applies exponentially to AI applications.
For many CFOs, this makes the search for AI frustrating. Although the technology exists and appears particularly inexpensive, use cases remain stuck in the concept phase and the ROI is not tangible. Data maturity is crucial here.
Both CrediBill’s developers and AAA’s consultants fight against substandard data daily. It is important to determine which goal you want to achieve and what qualitative data you need for that. Only then can you start working with the tooling.
Is your data ready for AI?
E-invoicing (via Peppol in Belgium) is a major step forward in structuring financial data. Data flows through financial systems in a uniform way. This creates a clean data source, allowing AI processes to work smoothly.
“We must dare to view the Peppol mandate as a step toward an AI-ready finance department.”
Companies that connect this structured data with, for example, payment behavior, credit information, payment follow-up, and ultimate cash flow create a solid internal financial data flow. Enriched with relevant external data sources, you get a particularly powerful context layer. It is precisely thanks to this crucial context that you can maximize the use of existing AI tools, with measurable ROI.
AI-ready finance starts with finance-ready data.
