Embati Innovates Financial Treasury Management with Generative AI on Google Cloud Vertex AI
Embati, a Spanish fintech company, automated complex treasury and bank reconciliation processes using Google Cloud's Vertex AI Generative AI, reducing manual effort and errors. They utilized Vertex AI to enrich data sets for automatic reconciliation and generate intelligent accounting suggestions, increasing automation to cover 32.7% of datasets. The solution allowed customers to save up to 10 hours per week and eliminated minimal errors in automated suggestions, enabling finance teams to focus on strategic decision-making. Embati's platform is built on Google Cloud microservices and integrates with Gemini for Google Workspace, supporting agile and scalable financial operations.
Reported outcomes
+60%
quantified impactAutomation & deflection
Strategic outcomes
Catalog median for automation & deflection deployments: +68% across 125 reported metrics. Compare benchmarks →
Primary read
Use case focus
Showing 3 of 3
- 1Generative AI Automation
- 2Financial Process Automation
- 3Treasury Management
- Complex, manual treasury and bank reconciliation tasks caused inefficiencies and higher error rates in financial workflows.
- There was a need to improve cash flow visibility and reduce user intervention in reconciliation processing.
- Finance teams required automation to free up time for higher-value strategic activities.
- Leveraged Google Cloud Vertex AI Generative AI to decompose complex reconciliation processes into simpler tasks, improving data enrichment and reconciliation accuracy.
- Built a platform on Google Cloud microservices with integrated APIs and used Gemini for Google Workspace for enhanced productivity.
- Implemented a settlement proposal engine using AI and ML to generate automatic daily logs of receipts and payments.
- The generative AI solution required no specific training dataset and achieved high response rates and accuracy.
- Saved customers up to 10 hours per week by automating manual reconciliation tasks.
- Eliminated minimal errors produced by previous automation, enhancing data accuracy.
- Increased automation coverage to 32.7% of dataset processes, with some customers achieving over 60%.
- Enabled finance teams to spend more time on strategic, value-added activities improving business performance.
Sources & evidence1
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