About the Client
Supply Medium is a technology-driven organization specializing in cloud, data, analytics, and AI-powered business solutions. As the company expanded its financial operations and managed growing volumes of accounting data, it sought to leverage artificial intelligence to improve financial accuracy, operational efficiency, and governance.
Background
The finance team at Supply Medium processes large volumes of General Ledger (GL) data every month across multiple business units and financial systems. These GL records are critical for financial reporting, compliance, auditing, and executive decision-making.
As transaction volumes increased, monthly GL reviews became heavily dependent on manual validation. Reviewing thousands of ledger entries was time-consuming, repetitive, and prone to human error—especially when similar transactions contained subtle contextual differences. To improve efficiency while maintaining financial controls, Supply Medium required an AI-powered GL review solution that could intelligently assist finance teams without disrupting existing approval processes.
Challenge
Every month, thousands of General Ledger entries are generated from enterprise accounting systems. Common issues included:
- Ambiguous or inconsistent transaction descriptions.
- Human errors during journal entry creation.
- Limited visibility into historical posting patterns.
- Significant manual effort required to validate and correct misclassified entries.
Manual reviews consumed valuable finance resources and still carried the risk of overlooking errors. Supply Medium required a scalable AI-powered solution that could accelerate financial validation while ensuring finance professionals retained complete decision-making authority.
Solution
Supply Medium partnered with our team to implement an AI-powered General Ledger Review System that combines Large Language Models (LLMs), automation, explainable AI, and human oversight.
Secure File Upload
- Finance teams upload monthly GL files through a secure Streamlit web application.
- User authentication is managed using Microsoft Azure Entra ID.
- Uploaded files are automatically validated for structure and completeness.
- Validated data is stored securely in PostgreSQL, enriched with vector embeddings, and backed up to AWS S3 for resilience and traceability.
AI-Powered GL Analysis
- General Ledger entries are normalized and deduplicated before processing.
- Vector embeddings preserve contextual relationships across descriptions and accounting periods.
- AWS Bedrock powers the AI engine using advanced Large Language Models including Claude, Cohere, and LLaMA.
- The AI analyzes current GL entries against historical financial data to:
- Detect potentially misclassified transactions.
- Recommend appropriate GL accounts.
- Assign confidence scores.
- Provide transparent explanations supporting every recommendation.
Review & Continuous Learning
- Finance teams receive downloadable Excel or CSV reports highlighting flagged transactions.
- Users review recommendations and indicate whether each suggestion is correct.
- Feedback is re-ingested into the AI pipeline, continuously improving future recommendations through real-world learning.
Human Decision Authority
The system never posts transactions directly into accounting systems.
All approval, correction, and posting decisions remain under the control of Supply Medium’s finance team, ensuring full compliance, governance, and auditability.
Outcome
The AI-powered General Ledger Review System delivered measurable business improvements for Supply Medium:
- Significantly reduced manual effort during monthly GL review cycles.
- Earlier detection of misclassified accounting entries, minimizing downstream corrections.
- Improved financial accuracy through AI-assisted recommendations and explainable insights.
- Scalable cloud-native architecture capable of supporting continued business growth.
- Continuous model improvement driven by finance team feedback.
- Strong governance maintained through full human oversight, with AI serving as a decision-support tool rather than replacing financial judgment.