The Client

The client is an American multinational energy giant, operating across upstream, midstream, and downstream segments in over 180 countries. The company manages large volumes of financial records, audits, contracts, and daily material data requiring rigorous regulatory compliance, financial transparency, and operational efficiency.

Business Challenges

Finance and audit functions are under mounting pressure as they manage growing volumes of structured and unstructured data from across the business. Manual audit checks and traditional data processing methods are slow, resource-intensive, and prone to errors, making it difficult to maintain accuracy and compliance. As organizations work to improve efficiency and decision-making, the ability to manage diverse, high-volume data with consistency and quality has become critical. AI-driven audit automation directly addresses these challenges by streamlining data handling, enhancing accuracy, and reducing dependence on manual processes.

Difficulty in establishing a robust framework

that could effectively leverage Azure OpenAI to improve the efficiency and accuracy of data management processes.

Struggles with eliminating irrelevant information

from Excel and CSV files generated from multiple purchase orders and invoices, leading to cluttered datasets.

The existing data extraction engine (DocIntel) has limitations

frequently including redundant or irrelevant data during extraction and parsing, which creates obstacles for accurate data analysis and informed decision-making.

Solution

Organizations are increasingly seeking automated solutions to improve the quality and reliability of their data management processes. AI in audit automation leverages advanced technologies to streamline data cleaning, reduce manual effort, and ensure that only relevant information is retained for analysis and decision-making.

1. The AI solution framework leverages Azure OpenAI to automatically detect and remove irrelevant data from Excel and CSV files, using Generative AI to filter and retain only pertinent information. This ensures greater accuracy and integrity in data management.
2. By automating the data cleaning process, the framework significantly reduces manual effort and optimizes resource utilization. Its ability to efficiently handle large data volumes is supported by Azure’s scalable infrastructure, which guarantees high availability and reliability.
3. The solution is designed for seamless integration with existing systems and features an intuitive interface, making configuration, monitoring, and management straightforward. Ultimately, this approach aims to enhance data quality, operational efficiency, and decision-making capabilities.

Doc Intel Technical Architecture

Figure 1: Doc Intel Technical Architecture

Tech Stack

Programming Languages   

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

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.

IDEs  

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

Other Components 

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

Business Benefits

AI-Driven Automation Impact

• Achieved complete (100%) document processing, eliminating the need for sampling.
• Increased audit coverage and accuracy by scaling from 25 documents to 200–1000 documents per audit.
• Minimized manual effort, enabling auditors to concentrate on insights and exceptions rather than routine data extraction.

Key Outcomes realized

• Enhanced audit frequency and depth without expanding team size.
• Improved data accuracy and completeness through comprehensive document analysis.
• Accelerated turnaround times, supporting more agile and responsive audit cycles.
• Strengthened risk detection and decision-making with full data visibility.

Conclusion

By leveraging Azure OpenAI and advanced automation, the proposed framework significantly enhances the efficiency, accuracy, and reliability of data management within finance and audit functions. Generative AI in finance audits automates the detection and removal of irrelevant information, streamlining data processing and empowering organizations to make faster, more informed decisions. Seamless integration and scalability ensure that the solution remains robust as data volumes grow, supporting ongoing compliance and operational excellence.