Babel is part of the small group of companies already implementing and experimenting with SAP’s native artificial intelligence in real business processes. This initiative is part of the company’s commitment to applying technological innovation from within, validating its impact before bringing it to the market.
A universal financial challenge that demands innovation
Payment collection reconciliation is one of the most frequent—and at the same time most manual and complex—processes within financial management in any organization. Every day, teams must review payments, identify associated invoices, validate amounts, and resolve pending cases—an intensive task that consumes large amounts of time and directly affects critical indicators such as cash flow and DSO.
Despite its importance, this process remains highly manual in most companies, relying on static rules, case-by-case reviews, and limited traceability.
Babel’s commitment: applying SAP AI in a real process
In this context, Babel has begun integrating the artificial intelligence capabilities natively available in SAP S/4HANA to transform its financial reconciliation process.
The goal is to automate a large part of the operational workload, reduce errors, and improve the efficiency of the finance team, allowing them to focus on higher-value strategic activities.
The AI applied by Babel provides key capabilities:
- Identification of patterns between payments and invoices.
- Automatic reconciliation suggestions, speeding up the review of items.
- Continuous learning, adapting to human decisions to improve accuracy.
- Complete traceability, without losing human control over critical decisions.
All of this is applied to real data in a productive environment, making this initiative a pioneering case within the SAP ecosystem.
Tangible results: financial efficiency and accuracy
Implementing AI in payment reconciliation has enabled Babel to achieve measurable results:
- Intelligent automation of 90% of the process, freeing the team from repetitive tasks.
- Over 50% reduction in reconciliation time.
- Near-zero errors, with a 95% improvement in accuracy.
- Optimization of resources and FTEs, allowing talent to be redirected toward strategic activities.
- Improved DSO and cash flow, directly impacting financial health.
- Full visibility and control over every decision, ensuring transparency and compliance.
Conclusion
Beyond automation, this initiative demonstrates how AI can accelerate essential administrative processes, improve accuracy, reduce operational workload, and free up capacity for analysis and strategic decision-making.
Thanks to the integration of SAP’s AI, Babel is transforming payment and collection reconciliation into a more agile, accurate, and intelligent process. Fewer errors, greater efficiency, and increased strategic capacity for the finance function are the tangible results of this innovation.
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