The Challenge
A firm’s audit practice relied heavily on manual processes to manage and verify large volumes of client data. Facing tight financial year deadlines and growing demand, the audit teams were under significant pressure to deliver accurate results quickly and efficiently. The manual nature of the work made the process complex, time-consuming, and costly.
The client sought to streamline and automate key aspects of their audit workflow to improve efficiency, reduce costs and address the unique needs of their diverse client base.
Our Solution
We developed a bespoke application to automate the ingestion and processing of data from the client’s extensive range of file formats and sources. Leveraging Google Cloud and machine learning technology, the solution efficiently ingests, validates and processes data as part of the audit requirements.
Our team designed, built, and continuously refined machine learning models to ensure accurate data ingestion and processing. The models were tailored to handle the complexity and variety of the firm’s client data, enabling greater automation across auditing and vouching tasks.
The Results
Increased Efficiency and Cost Savings
By automating previously manual auditing processes, the solution has eliminated more than 75,000 hours of manual effort annually. This equates to a forecasted reduction of approximately 29 full-time equivalent (FTE) positions, delivering an estimated annual saving of over AUD $4 million.
Continuous Improvement
The machine learning models are continually retrained, improving accuracy and efficiency over time. Each audit cycle builds on previous enhancements, delivering year-on-year improvements to the process.
Ongoing Partnership
We continue to work closely with the client, enhancing the solution and ensuring the automation evolves to meet changing audit requirements and deliver ongoing business value.
