Moving from Delayed Data to Event-Level Visibility - with Alex Curran of Aptitude Software
Summary
The podcast episode features Alex Curran of Aptitude Software, discussing how finance functions can transition from architectures built for delayed, aggregated data to real-time, event-level visibility. CFOs face increasing pressure to influence outcomes in real-time, yet often operate on systems designed for monthly reporting cycles. The discussion emphasizes the critical need for capturing every financial event at the transaction level, enabling continuous reconciliation, and ensuring full data lineage. This fundamental shift allows finance teams to surface exceptions immediately, support decisions as they unfold, and move towards a proactive role. The content highlights that AI investments are frequently hampered by legacy data architectures that aggregate and summarize, rather than preserving the granular, trusted transaction-level information essential for AI's accuracy and explainability. Aptitude Software's Fine Apps product addresses these limitations by providing a modern tech stack for real-time processing of millions of transactions hourly, supporting live P&L views, and integrating with diverse AI technologies.
Key takeaway
For CFOs and finance leaders aiming to modernize their operations, prioritize establishing a real-time, event-level data foundation. Your existing architectures, designed for monthly reporting, hinder proactive decision-making and effective AI integration. Focus on capturing every financial transaction, enabling continuous reconciliation, and ensuring full data lineage. This approach allows you to influence business outcomes as they unfold, rather than merely reporting past results, and provides the governed data necessary for defensible AI applications.
Key insights
Finance needs real-time, event-level data and robust governance to enable proactive decision-making and effective AI integration.
Principles
- Finance's role has shifted from retrospective reporting to real-time prediction and recommendation.
- AI requires real-time, trusted, transaction-level data for effective operation and explainability.
- AI governance is a core finance responsibility, defining standards and owning controls.
Method
Modernize finance by establishing a robust data foundation first, ensuring financial information is held in vast quantities, books, and records quality within a real-time environment.
In practice
- Capture every financial event at the transactional level in real-time.
- Implement continuous reconciliation to surface exceptions the moment they occur.
- Build an audit trail for AI outputs, traceable to originating transactions instantly.
Topics
- Real-time Finance
- Event-level Data
- Financial Data Governance
- AI in Financial Services
- Finance Modernization
- Continuous Reconciliation
- Data Lineage
Best for: Executive, Director of AI/ML, Consultant
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Editorial summary, takeaway, and curation by AIssential. Original article published by The AI in Business Podcast.