Real results with nBanks
Companies face different setups, but the challenges are often the same:
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Too many accounts
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Disconnected systems
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Time lost on manual work
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This page walks through three real-world scenarios, showing the starting point, how nBanks fits into daily operations, and the measurable results achieved.
Open Banking: Centralized Visibility Across Global Accounts
The Challenge: A multinational company operating across 4 continents manages over 100 bank accounts across different institutions. Financial data is fragmented, requiring manual extraction from multiple banking portals and consolidation in Excel. There is no real-time visibility, and over 50% of the finance team’s effort is spent just gathering data.
Value Delivered:
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Centralization of all bank accounts into a single platform
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Automatic data aggregation from multiple banks
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Real-time balance and transaction visibility
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Elimination of manual data collection and Excel consolidation
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Standardized data structure across all entities
1000
Hours Saved
per Year
€45K
Estimated Annual Savings
55%
Cost
Reduction
600%
ROI
3-6
Payback (months)
Open Finance: End-to-End Process Automation
The Challenge: A mid-to-large enterprise handles high transaction volumes across multiple entities. Financial processes such as reconciliation, payments, and reporting are manual, slow, and error-prone. Reporting is fragmented and often outdated, limiting operational efficiency and increasing risk.
Value Delivered:
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Automated bank reconciliation processes
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End-to-end financial workflow automation
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Integration between banking data and internal financial systems
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Real-time reporting and dashboards
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Reduction of manual intervention in repetitive tasks
1500
Hours Saved
per Year
€30K
Estimated Annual Savings
35%
Cost
Reduction
400%
ROI
4-7
Payback (months)
AI Agents: Intelligent Financial Analysis and Decision-Making
The Challenge: Finance teams spend most of their time on operational work, leaving little room for analysis and strategic decision-making. Even when data is available, extracting insights requires manual effort, limiting the CFO’s ability to act quickly and confidently.
Value Delivered:
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AI-driven financial analysis and anomaly detection
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Automated insights generation from real-time data
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Decision-support tools for forecasting and performance tracking
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Intelligent alerts and recommendations
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Continuous learning from financial patterns and behaviors
3500
Hours Saved
per Year
€80K
Estimated Annual Savings
60%
Cost
Reduction
800%
ROI
2-5
