Financial Close and Controllership Hub
Multi-agent AI Powered
To comply with my non-disclosure agreement, I have omitted and obfuscated confidential information in this case study. The information in this case study is my own and does not necessarily reflect the views of Amazon.
Problem statement
Amazon’s Finance and Accounting teams rely on more than 50 disparate tools across functions, geographies, and processes to support the financial close. This fragmented ecosystem creates a disconnected user experience, limits end-to-end visibility into close activities, and forces teams to depend on manual, reactive controls—introducing inefficiencies, delays, and increased operational risk during a time-critical process.
My Role
I collaborated with another UX designer and four engineering teams covering various finance and accounting functions. Starting without a PM, we led the product strategy initiative and successfully expanded it into an organization-wide project. The project began in June 2025 and is ongoing.
Vision statement
FCCH serves as the command center for Amazon’s financial close, enabling a seamless, intelligent, and compliant close process.
-
Unified View: Centralized command center providing real-time financial close visibility.
-
Insight Generation: Context-aware, intelligent data processing to surface actionable insights.
-
Multi-Agent AI: Specialized AI agents handling specific close tasks, with inter-agent communication and coordinated workflows.
-
Supervision: Risk-based, human-in-the-loop oversight to ensure compliance and maintain quality control.
Key personas
Executives
-
Use FCCH to validate financials, track 10-Q readiness, and drive performance decisions
Operational Owners
-
Execute and track journals, reconciliations, variance commentary
Design Solution
Financial statement:
Provides a role-based financial statement view with consolidated roll-ups and AI-driven insights, while allowing users to drill down into granular data for deeper analysis.
Analysis:
Enables top-down analysis, allowing users to leverage Agentic AI to examine data at any level—from row-level summaries down to individual cells.
Account details and transactions:
Customers can drill down to any level to view the aggregated account details and associated transactions.
