Enhancing Material Mapping Accuracy

About the Client

The client is a global leader in the oil and gas industry, headquartered in California, and operates across upstream, midstream, and downstream value chains in more than 180 countries. With approximately 45,000 employees worldwide and a broad customer base, the company manages extensive volumes of financial records, audit documents, contracts, and material data daily. Its scale and complexity demand stringent regulatory compliance, financial transparency, and operational efficiency.

Business Challenges

The client faced significant challenges within its finance and audit functions. Managing large volumes of structured and unstructured data had become increasingly complex and resource-intensive, and continued reliance on manual audit checks made processes slow, inconsistent, and vulnerable to errors. Material mapping automation offered a solution to these challenges by streamlining data extraction and management from diverse sources, even when information is inconsistent or unstructured. Addressing these challenges is crucial for improving operational efficiency and data accuracy.

Modern organizations frequently encounter substantial challenges when extracting and managing data from various sources, particularly when the information is inconsistent or unstructured. Addressing these challenges is crucial for improving operational efficiency and data accuracy.

Building an AI or GenAI-based solution

capable of extracting data from both vendor-provided invoice records and internal system documents proved complex and resource-intensive.

Managing inconsistencies in names, descriptions, and properties

across diverse documents created significant hurdles for accurate data extraction and standardization.

Ensuring data accessibility through a configurable interface

was challenging, as the solution needed to provide flexibility and ease of use for multiple stakeholders with varying requirements.

Solution

To address the complexities of material mapping across diverse document sources, an intelligent and automated solution is essential. AI-powered material mapping streamlines the process, enhances accuracy, and supports auditors in their review tasks.

• The solution automatically detects the type and template of uploaded documents, ensuring precise identification and mapping of materials from multiple sources.
• It generates comprehensive mapping reports, tagging materials, and maintains consistency across both vendor and internal records.
• Serving as an assistant to auditors, the solution simplifies the material mapping process. While auditors will continue to manage report approval and publishing milestones, there is potential for further automation in future iterations.

Material Reconciliation Technical Architecture

Figure 1: Material Reconciliation Technical Architecture

Tech Stack

1. Programming Languages   

Python for machine learning models, Azure SDK, scripting, and SQL for database queries and data management.

2. Azure Cloud Platform  

Azure Cloud Platform, Azure Blob Storage, Azure Functions, Azure Machine Learning, Azure OpenAI services, Azure SQL Database, Azure AI Search, Azure Key Vault, API Management, and GitHub.

3. IDEs  

Visual Studio Code for Python and Azure function development and Azure Machine Learning Studio for building and managing ML models.

4. Other Components 

 Azure SDKs for Python, Scikit Learn, Azure Cognitive Services API, Azure Logic Apps, or Data Factory.

Business Benefits

Integrating automation and AI-driven solutions into audit processes delivers substantial improvements in efficiency, accuracy, and strategic value. Material mapping automation streamlines manual tasks and accelerates audit cycles, enabling organizations to optimize resource utilization and enhance the quality of their audit outcomes.

• Increased audit frequency, with each audit previously requiring significant manual effort.
• Automation enabled each auditor to save approximately six weeks per audit.
• Achieved total annual savings of around 18 person-weeks, previously spent on manual reconciliation and validation.
• Automated material reconciliation drastically reduces manual intervention.
• Material reconciliation that previously took days or weeks was reduced to hours, enabling faster identification and resolution of issues.
• Freed up auditor capacity to focus on deeper analysis and strategic insights.
• Improved accuracy and consistency in data validation and reporting.

Conclusion

The Audit AI – Material Recon solution delivers a transformative approach to material mapping and reconciliation by harnessing the power of AI and automation. By accurately extracting, mapping, and tagging materials from diverse document sources, the solution streamlines audit workflows, enhances data consistency, and reduces manual effort. This intelligent system not only supports auditors in their review and approval processes but also lays the foundation for further automation and operational efficiency in the future.