Retailers, manufacturers and governments lose an average of 2-3% of their annual procurement budget due to supply chain loss. This is defined as anything that impacts the smooth delivery of goods and services from suppliers to your enterprise. Common issues include supplier overpayments, logistical inefficiencies, and incorrect customs/tax assessments that today take 12-18 months or longer for organizations to identify and resolve.
Vigilant AI has pioneered a platform which allows the office of finance accountants and internal audit teams to automate the review of all available documentation and financial data involved in your operations, allowing you to find this loss before it impacts your operations.
In this aforementioned supply chain example, we can automatically test your supplier’s contractual terms against your accounts payable data to ensure they are meeting their contractual terms before it impacts your customers and your bottom line.
This workflow can be extended to other financial analysis use cases, such as month-end revenue reconciliations, compliance reporting, fraud investigations and even corporate benchmarking. The versatility of the system is that it is designed to model any type of documentation, extract the critical terms and allow you to test those extracted values against other documents, or your financial ledgers.
The speed and completeness of our platform empowers you to identify errors faster and resolve them with your suppliers or customers before it causes friction.
Automated data analysis
Cross-correlation of any document with account ledger entries
Correct and accurate verification of transactions
Let’s connect to discuss how we can help you optimize your supply chain and improve your bottom line by analyzing all your financial data, all the time.
Our team is available to advise and collaborate with your analysts and internal audit teams to implement our deeply contextualized platform to deliver the timely results and insights you require to run your corporate operations more efficiently.
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